(PDF) Optimal Forms - A Master Thesis on Optimization of Generative Models

Master Thesis

(Dissertação de Mestrado)

Master in Design and Product Development Engineering

(Mestrado em Engenharia da Concepção e Desenvolvimento de Produto)

Optimal Forms

Generative Modeling Techniques in Optimization

Nelson de Jesus Silvério da Silva

Leiria, July 2011

Master Thesis

(Dissertação de Mestrado)

Master in Design and Product Development Engineering

(Mestrado em Engenharia da Concepção e Desenvolvimento de Produto)

Optimal Forms

Generative Modeling Techniques in Optimization

Nelson de Jesus Silvério da Silva

Scientific Adviser: Dr. Nuno Alves

(Escola Superior de Tecnologia e Gestão do Instituto Politécnico de Leiria, Portugal)

Scientific Co-Adviser: Dr. Eva Eggeling

(Fraunhofer, Austria)

Leiria, July 2011

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Report submitted to the Polytechnic Institute of Leiria in partial fulfillment of the

requirements for the degree of Master in Design and Product Development

Engineering (Mestrado em Engenharia da Concepção e Desenvolvimento de

Produto).

ISBN:

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The Jury

President

Vogals

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To My Family

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Acknowledgments

To my Scientific Advisor, Prof. Dr. Nuno Alves (Vice-director of CDRSP

Research Centre), for the constant attention, motivation and ideas given

throughout the course of the thesis and for helping me in achieving success.

Thank you also for your friendship and whole-hearted smile.

To my Scientific Co-Adviser, Dr. Eva Eggeling (Business Unit Manager of the

Visual Computing Fraunhofer Austria Research GmbH), thank you so much

for the support and incentive given to me personally, with your warm

friendship and also for the support given professionally that made the

realization of this thesis possible.

To M.Sc. Torsten Ullrich (Researcher at Fraunhofer, Austria), for priceless

knowledge, contribution, helps and of course, he‟s sincere friendship, this was

crucial for the development of this innovative work.

To Prof. Dr. Paulo Bártolo (Director of CDRSP Research Centre) and Prof. Dr.

Helena Bártolo (CDRSP Research Centre) for all the support throughout the

course of the thesis and for receiving me at the CDRSP Research Centre.

To Prof. Dr. Dieter Fellner (Director of the Fraunhofer Institute for Computer

Graphics Research - IGD) for giving me the opportunity to develop this work

with the CGV Group and Fraunhofer Austria and for allowing me to have

access to all the materials and valuable information produced within the

CGV/Fraunhofer group.

To M.Sc. Volker Settgast (Researcher at Fraunhofer Austria) for all the great

tips about Autodesk Maya and Rendering in general and he‟s invaluable

friendship.

To Dr. Christina Lemke (Architect with projects in Germany and Spain, urban

planner, construction biologist & ecologist) for her support, that kindly allowed

me to have access to her published PhD thesis work.

To my wife, for her patience, for being always there, supporting and

encouraging and for understanding all the time I wasn‟t around.

To ALL of you, that made this thesis possible, with your comprehension,

motivation, support and encouragement.

This master thesis was only possible to achieve, due to the good relationships

and close partnership that was settled between CDRSP and Fraunhofer

Austria. Thank you all so much…

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Keywords

Procedural, Optimization, Evolutionary, Algorithm, Simulation,

Building

Abstract

The generative modeling paradigm is a shift from static models to

flexible models. A generative model describes a modeling process

using functions, methods and operators. The result is an

algorithmic description of the construction process. Each

evaluation of such an algorithm creates a model instance, which

depends on its input parameters (width, height, radius, orientation,

etc.). These values are normally chosen according to aesthetic

aspects and style. In this study, the model‟s parameters are

automatically generated according to an objective function. A

generative model can be optimized according to its parameters, in

this way, the best solution for a constrained problem is determined.

The field of application is energy and architecture. Besides the

establishment of an overall framework design, this work consists

on the identification of different building shapes and their main

parameters, the creation of an algorithmic description for these

main shapes and the formulation of the objective function,

respecting a building‟s energy consumption (solar energy, heating

and insulation). Also, this work aims the conception of an

optimization pipeline, combining an energy calculation tool with a

geometric scripting engine. In this study, one can read about state

of the art developments related to architecture and procedural

modeling. The major contribution of this development is to present

methods that lead to an automated and optimized 3D shape

generation for the projected building (based on the desired

conditions and according to specific constrains), this will help in

the construction of real buildings that account for less energy

consumption and for a more sustainable world.

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Table of

Contents

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Acknowledgments ......................................................................................................................................... vii

Keywords ........................................................................................................................................................ ix

Abstract .......................................................................................................................................................... ix

Table of Contents ....................................................................................................................................... xi

Figures List ............................................................................................................................................... xvii

Tables List ................................................................................................................................................ xxv

Abbreviations and Acronyms .................................................................................................................. xxix

Introduction ......................................................................................................................................... 1 I.

1 The Problematic .................................................................................................................................. 1

1.1 Thesis Structure ............................................................................................................................... 4

State of the Art ................................................................................................................................. 7 II.

2 Overview ............................................................................................................................................. 7

2.1 Parametric and Procedural Modeling .............................................................................................. 8

2.1.1 Plugins for existent 3D Software: Blender, 3D Studio Max and Others ....................................... 9

2.1.2 CityEngine .................................................................................................................................. 10

2.1.3 Bentley – MicroStation Extension: GenerativeComponents (GC).............................................. 13

2.1.4 Rhinoceros and Grasshopper ..................................................................................................... 14

2.1.5 Generative Modeling Language (GML) ...................................................................................... 16

2.1.6 Euclides Framework and JavaScript ........................................................................................... 18

2.1.7 Autodesk Revit Architecture 2012 ............................................................................................. 19

2.1.8 Project Vasari and Project Nucleus ............................................................................................ 21

2.1.9 Autodesk Adaptive Components ............................................................................................... 23

2.2 Evolutionary Architecture and the Use of Algorithms in Optimization of Problems ..................... 25

2.2.1 Differential Evolution (DE) ......................................................................................................... 28

2.2.2 Pros and Cons of using Evolutionary Algorithms (EAs) .............................................................. 38

2.2.3 Other Evolutionary Based Algorithms - DE/EDA and Hybrid-DE ................................................ 39

2.3 Advanced Rendering, Visualization and Interaction Techniques in Architecture ......................... 40

2.3.1 Multitouch (MTT) ....................................................................................................................... 40

2.3.2 Virtual and Augmented Reality .................................................................................................. 43

2.3.3 Computer Generated Holography ............................................................................................. 47

2.3.4 Advanced Rendering .................................................................................................................. 50

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2.3.5 Virtual World Interactivity ......................................................................................................... 53

2.4 “A World Full of Sensors” .............................................................................................................. 57

2.4.1 Sensors Feed Information into Virtual Worlds .......................................................................... 57

2.4.2 Remote Monitoring of Persons Inside Buildings ........................................................................ 60

2.4.3 Kinetic, Responsive Performative and Adaptive Architecture ................................................... 61

2.5 Reverse Engineering and Rapid Prototyping ................................................................................. 62

2.5.1 Reverse Engineering................................................................................................................... 63

2.5.2 Rapid Prototyping ...................................................................................................................... 68

2.5.3 Rapid Prototyping Techniques ................................................................................................... 69

2.5.4 Personal Fabrication and Future Manufacturing ....................................................................... 76

2.6 Building Information Modeling (BIM) and Automated Construction of Buildings ........................ 78

2.6.1 Building Information Modeling (BIM) ........................................................................................ 78

2.6.2 Automated Construction of Buildings ........................................................................................ 79

Simulation Tools in Architecture ..................................................................................................... 81 III.

3 Outline ............................................................................................................................................... 81

3.1 EnergyPlus and DesignBuilder ....................................................................................................... 82

3.2 Autodesk Ecotect Analysis ............................................................................................................. 84

3.2.1 Short Comparison between Autodesk Ecotect and EnergyPlus ................................................. 87

3.3 Ansys: AirFlow ............................................................................................................................... 88

3.4 Sustainability Tools in Architecture – Comparison Studies/Audits ............................................... 90

A Global Optimization Framework ................................................................................................. 91 IV.

4 Problematic of “Form Follows Energy” and the Pursuit of Solutions................................................ 91

4.1 Answer to the Problematic: Optimal Forms - A Global Optimization Framework ........................ 97

4.2 Identification of Essential Forms Used in the Real World ............................................................. 98

4.2.1 Procedural Shape Generation .................................................................................................... 99

4.2.2 Code Writing Using Euclides and JavaScript ............................................................................ 101

4.3 Simulation Tools Integration ....................................................................................................... 102

4.3.1 Simulation in Ecotect and the Admittance Method................................................................. 104

4.3.2 Initial Manual Workflow Tests ................................................................................................. 105

4.4 Differential Evolution................................................................................................................... 107

4.5 The Developed Global Optimization Framework ........................................................................ 110

4.6 Case Studies and Presentation of Results.................................................................................... 112

4.6.1 Case Study 1 – Classic Shape Building Optimization ................................................................ 113

4.6.2 Case Study 1 - Presentation of Results..................................................................................... 116

4.6.3 Case Study 2 – Cube Shape Building Optimization .................................................................. 126

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Conclusions and Future Work ....................................................................................................... 131

V.

5 Summary ......................................................................................................................................... 131

5.1 Final Conclusions ......................................................................................................................... 132

5.2 Future Work................................................................................................................................. 133

REFERENCES ............................................................................................................................................ 135

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Figures List

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FIG. II-1 – SUICIDATOR CITY GENERATOR (SCG) FOR BLENDER 9

FIG. II-2 – PROCEDURAL/PARAMETRIC EXAMPLES CREATED IN CITYENGINE 10

FIG. II-3 – CITYENGINE IDE AND THE RULE EDITOR CAPABILITIES 12

FIG. II-4 – GENERATIVECOMPONENTS (GC) IDE, BENTLEY MICROSTATION 13

FIG. II-5 – RHINOCEROS IS USED IN MULTIPLE FIELDS, INCLUDING ARCHITECTURE 14

FIG. II-6 – VORONOI EXAMPLES, CREATED USING RHINOCEROS AND GRASSHOPPER, BY ATSUO NAKAJIMA (TOKYO, JAPAN) 15

FIG. II-7 - PARAMETRIC STRATEGIES ACHIEVED USING RHINO AND GRASSHOPPER. (CREATED BY THE AUTHOR OF THIS MASTER

THESIS) 15

FIG. II-8 - CREATION OF A SIMPLE HOUSE MODEL USING GML, THE EXTRUDE OPERATOR IS REPEATEDLY APPLIED TO THE GROUND

POLYGON. TO CREATE THE ROOF, THE COMBINED OPERATOR COLLAPSE-MID IS APPLIED TO THE FACECW AND FACECCW

EDGES OF THE EDGE RETURNED BY THE EXTRUDE OPERATION. 16

FIG. II-9 – PARAMETERIZATION/CONFIGURATION OF A CHAIR WITH GML 17

FIG. II-10 – GOTHIC STYLE BUILDING GENERATED WITH GML 17

FIG. II-11 – CONFIGURATION OF DIFFERENT WHEEL RIM STYLES USING GML 17

FIG. II-12 – EXAMPLE OF A 3D APPLICATION CREATED FOR THIS THESIS USING EUCLIDES AND JAVASCRIPT. IT ALLOWS THE

CONTROL OF SEVERAL SHAPE PARAMETERS ON THE “CLASSIC BUILDING FORM EXAMPLE”. 18

FIG. II-13 – ENERGY CONSUMPTION STUDY USING AUTODESK REVIT 19

FIG. II-14 – CONCEPTUAL DESIGN IN AUTODESK REVIT ARCHITECTURE 20

FIG. II-15 – SUN STUDIES USING PROJECT “VASARI” 21

FIG. II-16 - PANEL STUDY USING REVIT AND VASARI 22

FIG. II-17 - USING REVIT, VASARI AND NUCLEUS PHYSICS FOR A PANEL STUDY, PLUS ANALYSIS 22

FIG. II-18 – ADAPTIVE COMPONENTS IN AUTODESK REVIT 23

FIG. II-19 – ADAPTIVE PANEL EXAMPLE IN AUTODESK REVIT 24

FIG. II-20 – ANOTHER ADAPTIVE PANEL EXAMPLE, BUILT USING ADAPTIVE COMPONENTS 24

FIG. II-21 – IMAGE TAKEN FROM THE BOOK “THE SELFISH GENE” BY RICHARD DAWKINS 25

FIG. II-22 – SEVERAL VISIONS RELATED TO EVOLUTIONARY ARCHITECTURE AND BIOMIMETIC 26

FIG. II-23 – EVOLUTIONARY EXAMPLES TAKEN FROM THE BOOK “AN EVOLUTIONARY ARCHITECTURE” BY JOHN FRASER [23] 26

FIG. II-24 – DYNAMIC GEOMETRY COMPUTATION, “SHANGHAI TOWER - GEOMETRY GENERATE AND RENDERING” (MICHAEL

PENG) 27

FIG. II-25 – ECOLOGICAL HOUSE OF THE FUTURE (EUGENE TSUI) 27

FIG. II-26 – THE GLOBAL OPTIMIZATION PROBLEM (EXAMPLE IN MATLAB). SEARCH OF THE HIGHEST MOUNTAIN PEAK AMONG A

NEIGHBORHOOD OF OTHER HIGH MOUNTAINS PEAKS. 28

FIG. II-27 - OBSERVATION, ANALYSIS AND COMPUTATION OF BRANCHING PATTERNS IN NATURAL SYSTEMS (BY EVAN GREENBERG

[29]) 29

FIG. II-28 – SIMPLE EA’S STEPS 32

FIG. II-29 – GENERAL EVOLUTIONARY ALGORITHM: I: INITIALIZATION, F(X): EVALUATION, ?: STOPPING CRITERION, SE: SELECTION,

CR: CROSS-OVER, MU: MUTATION, RE: REPLACEMENT, X*: OPTIMUM. AUTHOR: JOHANN "NOJHAN" DRÉO 33

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FIG. II-30 - DE OPTIMIZATION PERFORMANCE (PEDERSEN, M. [31]) ON SEVERAL DIFFERENT PROBLEMS USING DE/RAND/1/BIN

ALGORITHM. PLOTS SHOW THE MEAN FITNESS ACHIEVED. OVER 50 OPTIMIZATION RUNS 34

FIG. II-31 – 3D ANAGLYPH AND ACTIVE STEREO VISUALIZATION ON A MULTITOUCH DI TABLE (MTT4ALL MULTITOUCH TABLE

WAS BUILT BY THE AUTHOR OF THIS MASTER THESIS) 41

FIG. II-32 – INITIAL MTT4ALL SCALE MODEL (LEFT) AND FINAL MTT4ALL FUNCTIONAL PROTOTYPE IN USE , RUNNING

FRAUNHOFER VIRTUALDESK APPLICATION, DEVELOPED BY THE AUTHOR OF THIS MASTER THESIS FOR FRAUNHOFER AUSTRIA

(RIGHT) 42

FIG. II-33 – FRAUNHOFER, MULTITOUCH ARCHITECTURE VISUALIZATION – MESSE FRANKFURT GMBH (USING THE

INSTANTREALITY FRAMEWORK) 43

FIG. II-34 – ANAGLYPH VISUALIZATION (CREATED BY THE AUTHOR OF THIS MASTER THESIS) 44

FIG. II-35 – ACTIVE STEREO VISUALIZATION (CREATED BY THE AUTHOR OF THIS MASTER THESIS) 44

FIG. II-36 – AN AUGMENTED REALITY SYSTEM DEVELOPED BY FRAUNHOFER (MONITOR + CAMERA + VIRTUAL REALITY

SOFTWARE) 45

FIG. II-37 – DAVE, CGV AUSTRIA: IMAGES ARE PROJECTED ON THE BACK PROJECTION SIDE WALLS AND ON THE FLOOR FROM

ABOVE, MMIRRORS ARE USED TO REDUCE THE SPACE NEEDED 45

FIG. II-38 – CGV AUSTRIA, THE DAVE, A 3D IMMERSIVE SYSTEM (EXPLORING THE NATIONAL LIBRARY OF VIENNA) 46

FIG. II-39 – THE SWEETHOME3D (MODELING APPLICATION) OUTPUT IS TRANSFERRED WITH A WEB SERVICE TO AN OPENSG

CAVE APPLICATION WHICH LETS THE USER WALK THROUGH A 3D REPRESENTATION OF THE HOUSE PLAN 46

FIG. II-40 – HEYEWALL (HIGH RESOLUTION MULTITOUCH SCREEN) 47

FIG. II-41 – COMPUTER GENERATED HOLOGRAPHY. A COMPUTER CALCULATES A HOLOGRAPHIC FRINGE PATTERN FOR DISPLAY BY

THE SPATIAL LIGHT MODULATOR (SLM), WHICH DIFFRACTS LASER LIGHT TO YIELD AN INTERACTIVE, TRUE 3D IMAGE 48

FIG. II-42 – TRADESHOW (AN HOLOGRAM SYSTEM) 49

FIG. II-43 – ALTHOUGH HE WAS IN MELBOURNE, TELSTRA'S CHIEF TECHNOLOGY OFFICER, HUGH BRADLOW (RIGHT), MAKES IS

PRESENCE FELT AT A CONFERENCE IN ADELAIDE (PHOTO: TELSTRA) 49

FIG. II-44 – TOUCHABLE HOLOGRAPHY INTERACTION SYSTEM. AN AERIAL IMAGING SYSTEM, A NON-CONTACT TACTILE DISPLAY

AND A WIIMOTE-BASED HAND-TRACKING SYSTEM ARE COMBINED. IN THIS FIGURE, THE ULTRASOUND IS RADIATED FROM

ABOVE AND THE USER FEELS AS IF A RAIN DROP HITS HIS PALM 50

FIG. II-45 – 3D PHOTOREALISTIC RENDERING (CREATED BY HARCHI, AN ARCHITECTURE COMPANY BASED IN PORTUGAL) 51

FIG. II-46 – IPL/CDRSP FUTURE BUILDING (RENDERED IN AUTODESK MAYA 2011 BY THE AUTHOR OF THIS MASTER THESIS) 52

FIG. II-47 – MEDIUM QUALITY RENDERING OF A FACTORY INSTALLATION (CREATED IN DEEP EXPLORATION BY THE AUTHOR OF

THIS THESIS) 52

FIG. II-48 – HIGH QUALITY REAL-TIME INTERACTIVE RENDERING (WWW.ICREATE3D.COM) 53

FIG. II-49 – MANIPULATION OF VR DATA, PROVIDED BY MEMPHIS [1] (TICIVIEW VR SYSTEM). IGI/FRAUNHOFER RESEARCH

GROUP, 2007, SEOUL - SOUTH KOREA, (THE AUTHOR OF THIS MASTER THESIS WAS A MEMBER IN THE TEAM RESPONSIBLE

FOR THE DEVELOPMENT OF THIS SYSTEM) 55

FIG. II-50 – ADVANCED INTERACTIVE VISUALIZATION (USING IVIEWER), STARTING FROM LEFT TO RIGHT: (A) SKYSCRAPPER; (B) AN

APARTMENT INSIDE THE SKYSCRAPPER (WWW.ICREATE3D.COM) 55

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FIG. II-51 – VIRTUAL FACTORY SIMULATION/SERIOUS GAME THAT WILL ALLOW A COMPANY TO GIVE TRAINING TO USERS, (THIS

PROJECT WAS CREATED AT CDRSP BY THE AUTHOR OF THIS MASTER THESIS) 56

FIG. II-52 – SCREENSHOT OF THE TRICORDER DEVICE SHOWING THE FLOORPLAN OF A LAB, OVERLAYED WITH PLUG ICONS TO

REPRESENT SOUND, LIGHT, CURRENT CONSUMPTION, MOTION AND VIBRATION. ALSO AVERAGE DATE FROM ALL SENSORS IS

DISPLAYED [52] 58

FIG. II-53 – A PORTAL IN SECOND LIFE SHOWS SENSOR DATA OVER TIME 59

FIG. II-54 – A VIRTUAL DATAPOND IN THE VIRTUAL ATRIUM (LEFT) AND A REAL DATAPOND IN THE REAL MEDIA LAB ATRIUM

(RIGHT) 60

FIG. II-55 – DIFFERENT REPRESENTATIONS OF A PERSON DETECTION IN 3D, STARTING FROM LEFT TO RIGHT: (A) BILLBOARD WITH

A THIN COLORED SURROUNDING LINE, (B) ADDITIONAL GEOMETRY, (C, D) OVERLAY MARKER WHICH IS NOT OCCLUDED BY

THE SCENE 60

FIG. II-56 – A MODEL WHICH IS A KINETIC PAVILION THAT REACTS ON WEATHER DATA 61

FIG. II-57 – PHASES OF THE REVERSE ENGINEERING PROCESS 63

FIG. II-58 – REVERSE ENGINEERING - CLASSIFICATION TECHNIQUES FOR 3D DATA ACQUISITION 64

FIG. II-59 – STEINBICHLER COMET 5 PHOTOGRAMMETRY 3D SCAN EQUIPMENT AT CDRSP REVERSE ENGINEERING LABORATORY

65

FIG. II-60 – A 3D SCAN OF A REAL GRAPHITE ELECTRODE USED IN MOLDS INDUSTRY. (3D SCAN AND ANALYSIS/INSPECTION DONE

BY THE AUTHOR OF THIS MASTER THESIS, USING STEINBICHLER COMET 5 EQUIPMENT, AND STEINBICHLER COMET PLUS AND

COMET INSPECT SOFTWARE) 65

FIG. II-61 – “SANTUÁRIO DO SENHOR DA PEDRA”, ÓBIDOS, PORTUGAL 66

FIG. II-62 – POINTS OBTAINED FOR THE “SANTUÁRIO DO SENHOR DA PEDRA” BUILDING 66

FIG. II-63 – 3D CLOUD OF POINTS FOR THE “SANTUÁRIO DO SENHOR DA PEDRA” BUILDING 66

FIG. II-64 - A) STL AND B) 3D MODEL 67

FIG. II-65 – SCALE MODEL OF “SANTUÁRIO DO SENHOR DA PEDRA” OBTAINED BY RAPID PROTOTYPING 67

FIG. II-66 – MAIN COMPONENTS OF A STEREO-LITHOGRAPHY MACHINE 69

FIG. II-67 – PROTOTYPES PRODUCED USING STEREO-LITHOGRAPHY 70

FIG. II-68 – SIMPLIFIED FDM PROCESS 71

FIG. II-69 – SCALE MODELS OBTAINED USING FDM 72

FIG. II-70 – SCHEME OF THE LOM PROCESS 73

FIG. II-71 – SCALE MODEL FOR OPORTO MUSIC HOUSE, PRODUCED USING LOM (PORTUGAL) 73

FIG. II-72 – SCHEME OF THE SELECTIVE LASER SINTERING (SLS) 74

FIG. II-73 - 3D PRINTING PROCESS 75

FIG. II-74 – COMPLETE SCALE MODEL OBTAINED USING THE 3D PRINTING PROCESS 75

FIG. II-75 – COMBINING SEVERAL TECHNOLOGIES/PROCESSES 76

FIG. II-76 - A BRUSH MADE IN A 3D PRINTER, USING TWO DIFFERENT MATERIALS, PRINTED SIMULTANEOUSLY INTO A SINGLE AND

NOT ASSEMBLED FUNCTIONAL OBJECT (OBJECT INC.) 77

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FIG. II-77 – BIM VIRTUAL INFORMATION (VIRTUAL SIMULTANEOUS VISUALIZATION OF SIX DIFFERENT PHASES OF AN ONGOING

BUILDING PROJECT, CREATED USING GRAPHISOFT ARCHICAD PLATFORM) 79

FIG. II-78 – VISION FOR AN AUTOMATED SYSTEM FOR AUTONOMOUS CONSTRUCTION OF BUILDINGS (BEHROKH KHOSHNEVIS) 80

FIG. II-79 – A REAL PROTOTYPE FOR AN AUTOMATED SYSTEM THAT WILL ALLOW THE CREATION OF BUILDINGS 80

FIG. III-1 – ENERGYPLUS SIMULATION ZONES 82

FIG. III-2 – DESIGNBUILDER AND ITS BUILDINGS ENERGY EFFICIENCY RATING 83

FIG. III-3 - WORKING IN ENERGYPLUS-MODE INSIDE ECOTECT, WHEN DEFINING OPERATIONAL SCHEDULES 84

FIG. III-4 - INTERNAL DAYLIGHT FACTORS SHOWN OVER A STANDARD WORKING PLANE 85

FIG. III-5 - OVERLAYING A SUN-PATH ON THE MODEL VIEW 85

FIG. III-6 - ANNUAL CUMULATIVE SOLAR RADIATION OVER THE EXTERNAL SURFACES 86

FIG. III-7 – COLOURED CONTOURS OF THERMAL CONFORT IN A CONFERENCE ROOM PREDICTED FOR A PARTICULAR VENTILATION

SYSTEM DESIGN (ANSYS, INC. PROPRIETARY) 88

FIG. III-8 – ANSYS CFD MODELLING OF REGIONAL FLOW PATTERNS NEAR CAPE SHOPPING CENTRE (STEPHAN SCHMITT &

THOMAS KINGSLEY; QFINSOFT, SA) 89

FIG. IV-1 – DIFFERENT POSSIBLE BUILDINGS FORMS, ALL WITH 1000M3 OF VOLUME, THIS CAN ALLOW THE COMPARISON OF

RESULTS OBTAINED WITH DIFFERENT FORMS (PHD THESIS OF CHRISTINA LEMKE [88]) 92

FIG. IV-2 – DEFINITION OF AN ELEMENTARY VOLUME, ACCORDING TO DEPECKER, P., ET AL. [91] 93

FIG. IV-3 – FLOW FIELD AT A STREET INTERSECTION WITH A TALL BUILDING, ILLUSTRATING EXCHANGES BETWEEN THE STREETS AND

ADDITIONAL MIXING PROCESSES DUE TO THE LARGE BUILDING 94

FIG. IV-4 – VIEW OF GREENHOUSE SHAPES IN E-W ORIENTATION 96

FIG. IV-5 – 3D MODELS THAT REPRESENT REAL WORLD FACTORIES 98

FIG. IV-6 - SELECTED BASIC FORMS INSPIRED IN REAL WORLD BUILDING SHAPES, STARTING FROM LEFT TO RIGHT: (A) CUBE, (B)

CLASSIC AND (C) CYLINDER) CREATED USING EUCLIDES AND RENDERED USING DEEP EXPLORATION 98

FIG. IV-7 – TESTS FOR CREATING DIFFERENT 3D SHAPES (AND CONTROLLING ITS PARAMETERS) USING PARAMETRIC EQUATIONS 99

FIG. IV-8 – DEFINITION OF PARAMETRIC EQUATIONS IN MAPPLE 14 100

FIG. IV-9 - OPTIMIZED CODE GENERATION PRODUCED BY MAPPLE 14 100

FIG. IV-10 – PIECE OF JAVASCRIPT CODE TO GENERATE THE 3D CYLINDER SHAPE (CODE ADAPTED FROM PARAMETRIC EQUATIONS

AND MAPPLE 14) 101

FIG. IV-11 – RESULTING JAVASCRIPT/EUCLIDES INTERFACE THAT ALLOWS THE CONTROL OF EACH SHAPE PARAMETER 101

FIG. IV-12 - 3D SHAPE GENERATION IN EUCLIDES (1.000 M3 OF VOLUME); FOLLOWED BY THERMAL ANALYSIS; AND

PRESENTATION OF THE ANALYZED SHAPE. OTHER TYPES OF ANALYSIS CAN BE PERFORMED AS WELL. 105

FIG. IV-13 – ANOTHER 3D SHAPE GENERATION IN EUCLIDES (MAINTAINING 1.000 M3); FOLLOWED BY THERMAL ANALYSIS; AND

PRESENTATION OF THE ANALYZED SHAPE 106

FIG. IV-14 – EXAMPLE LIST FOR MATERIALS THAT CAN BE USED INSIDE AUTODESK ECOTECT 109

FIG. IV-15 - OVERVIEW OF “OPTIMAL FORMS”, THE DEVELOPED AND PROPOSED GLOBAL OPTIMIZATION FRAMEWORK 110

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FIG. IV-16 – THE GLOBAL OPTIMIZATION FRAMEWORK RUNNING AUTONOMOUSLY (SIMULATION IN ECOTECT FOLLOWED BY 3D

SHAPE GENERATION IN ORDER TO EVOLVE A POPULATION OF NEW BUILDINGS WITH DIFFERENT PARAMETERS USING THE

DIFFERENTIAL EVOLUTION ALGORITHM) 111

FIG. IV-17 – THE ALGORITHM CHOOSES DIFFERENT INDIVIDUALS (BUILDINGS) FOR ANALYSIS, NOT STOPPING ON THE LOCAL

MINIMA THAT WAS FOUND ALONG THE OPTIMIZATION PROCESS AND GIVING ROOM FOR JUMPING THOSE SAME LOCAL

MINIMA 116

FIG. IV-18 – IN THIS RUN IT WAS GENERATED A COMPLETELY DIFFERENT INDIVIDUAL (BUILDING SHAPE), BUT THE ADMITTANCE

VALUE WAS REALLY HIGH AND OTHER BETTER INDIVIDUALS WERE FOUND 117

FIG. IV-19 – A NEW OPTIMIZATION RUN, THIS TIME USING A DIFFERENT VALUE FOR CROSSOVER (0.6) 118

FIG. IV-20 – OTHER “TIGHTER” CONSTRAINS WERE CHOSEN, AND THE RESULTS WERE SLIGHTLY WORST THEN THE INITIAL

ATTEMPTS (RUNS 1, 2 AND 3) WHERE A WIDER DOMAIN OF SEARCH WAS USED 119

FIG. IV-21 – THE OPTIMIZATION RUNS (1, 2 , 3 AND 4) ARE PLOTTED HERE SIMULTANEOUSLY 120

FIG. IV-22 – BY ALLOWING THE ALGORITHM TO GENERATE BUILDINGS THAT COULD USE A DIFFERENT ORIENTATION (LESS

CONSTRAINED REGARDING THE ORIENTATION OF THE BUILDING), MORE EVALUATIONS WERE NEEDED, BUT A MUCH BETTER

RESULT WAS OBTAINED 121

FIG. IV-23 – A NEW CONSECUTIVE OPTIMIZATION RUN (USING EXACTLY THE SAME VALUES) IN ORDER TO CHECK IF THE BEHAVIOR

WAS CONSISTENT FROM RUN TO RUN 122

FIG. IV-24 – BY ALLOWING THE ALGORITHM TO SEARCH FOR SOLUTIONS IN AN ORIENTATION DOMAIN BETWEEN 0O AND 360O

AND BECAUSE THE BUILDING DOES NOT HAVE DOORS OR WINDOWS YET, THE ALGORITHM FOUND A GOOD SOLUTION BY

ORIENTING THE BUILDING ON A DIFFERENT DIRECTION (WHEN COMPARED TO RUNS 5 AND 5_1) 123

FIG. IV-25 – BY PLOTTING ALL THE INDIVIDUALS GENERATED BY THE GLOBAL OPTIMIZATION FRAMEWORK FOR RUNS (5, 5_1 AND

6) IT’S POSSIBLE TO CHECK THE CONSISTENCE OF RESULTS OBTAINED ON THESE MORE COMPLETE OPTIMIZATION RUNS 124

FIG. IV-26 – CASE STUDY 1 (FINAL CLASSIC BUILDING SHAPE) – DAILY AND ANNUAL SUN PATHS + SHADOWS AND DAYLIGHT

LEVELS AT 14:00 PM, FOR THE 8TH OF SEPTEMBER (BASED ON SPECIFIC CLIMATE/WEATHER FILES FOR COIMBRA,

PORTUGAL); WIDTH = 20 M; HEIGHT = 6 M; ROOF ANGLE = 60O; VOLUME = 1000 M3; ORIENTATION: 122,10O 125

FIG. IV-27 – WE CAN OBSERVE THAT AT EVALUATION 289 THE OPTIMIZATION FRAMEWORK HAD ALREADY ACHIEVED A VERY GOOD

RESULT (COMPARED TO THE FINAL RESULT), BUT BECAUSE THE STOP CRITERION USED WAS, 1000 EVALUATIONS OR DX =

1.0, THE OPTIMIZATION CONTINUED UNTIL DX = 1.0 FOR FOUR CONSECUTIVE TIMES 128

FIG. IV-28 - CASE STUDY 2 (FINAL CUBE BUILDING SHAPE) – DAILY AND ANNUAL SUN PATHS + SHADOWS AND TOTAL

RADIATION LEVELS AT 14:00 PM, FOR THE 8TH OF SEPTEMBER (BASED ON SPECIFIC CLIMATE/WEATHER FILES FOR

COIMBRA, PORTUGAL); WIDTH = 10 M; HEIGHT = 6 M; VOLUME = 1000 M3; ORIENTATION: 121,86O 129

xxiv

xxv

Tables List

xxvi

xxvii

TABLE III-1 - CHARACTERISTICS OF TWO DIFFERENT SIMULATION TOOLS [83] ................................................................. 87

TABLE IV-1 – ACTIVITY LEVEL IN AUTODESK ECOTECT ............................................................................................... 108

TABLE IV-2 - THIS TABLE SHOWS SEVERAL RUNS USING THE DEVELOPED FRAMEWORK IN ORDER TO OPTIMIZE THE DESIGN OF A

“CLASSIC SHAPE FORM” BUILDING USING THE DIFFERENTIAL EVOLUTION (DE) ALGORITHM. IN THIS TABLE IS ALSO POSSIBLE

TO SEE THE DIFFERENT CONSTRAINS AND PARAMETERS USED IN THE OPTIMIZATION PROCESS .................................... 114

TABLE IV-3 - RESULTS OBTAINED IN EACH OF THE OPTIMIZATION RUNS (ACCORDING TO TABLE IV-2).................................. 115

TABLE IV-4 – THIS TABLE SHOWS AN OPTIMIZATION RUN USING THE DEVELOPED FRAMEWORK IN ORDER TO OPTIMIZE THE DESIGN

OF A “CUBE SHAPE FORM” BUILDING USING THE DIFFERENTIAL EVOLUTION (DE) ALGORITHM. IN THIS TABLE IS ALSO

POSSIBLE TO SEE THE DIFFERENT CONSTRAINS AND PARAMETERS USED IN THE OPTIMIZATION PROCESS ....................... 127

TABLE IV-5 - RESULTS OBTAINED IN EACH OF THE OPTIMIZATION RUNS (ACCORDING TO TABLE IV-4).................................. 128

xxviii

xxix

Abbreviations

and Acronyms

xxx

xxxi

A

ANM: Annealed Nelder and Mead strategy · 37

AR: Augmented Reality · 42, 44, 53, 54, 55

ASA: Adaptive Simulated Annealing · 37

ASHRAE: American Society of Heating, Refrigerating

and Air Conditioning Engineers · 87

B

BGA: Breeder Genetic Algorithm · 37

BIM: Building Information Modeling · 11, 19, 21, 78, 79

BLAST: Building Loads Analysis and System

Thermodynamics · 82, 83

C

CAD: Computer Aided Design · 8, 10, 14, 61, 63, 65, 78,

79, 125, 133

CAM: Computer Aided Manufacturing · 14

CAVE: Cave Automatic Virtual Environment · 42, 45, 46

CC: Contour Crafting (Automated Construction System)

· 80

CDRSP: Centre for Rapid and Sustainable Development

of the Product · vii, 2, 7, 41, 43, 52, 56, 65, 68

CEP: Complex Event Processing · 59

CFD: Computational Fluid Dynamics · 88, 89, 133

CGA: Computer Generated Architecture (shape

grammar) · 11

CGH: Computer Generated Holography · 47, 48, 49

CIBSE: Chartered Institution of Building Services

Engineers · 87, 104, 105, 141

CNC: Computer Numeric Control · 14

CPU: Central Processing Unit · 51, 52

CR: Crossover · 34

CSG: Constructive Solid Geometry · 16

CT: Computed Tomography · 51

D

DAVE: Definitely Affordable Virtual Environment

(Immersive VR System Developed by Fraunhofer) ·

45, 46

DDE: Dynamic Data Exchange · 87

DE: Differential Evolution · 4, 28, 32, 33, 34, 35, 36, 37,

39, 107, 112, 114, 127

E

EA: Evolutionary Algorithm · 28, 32, 38

EASY: Evolutionary Algorithm with Soft Genetic

Operators · 37

EDA: Estimation of Distribution Algorithm · 39

ES: Evolutionary Strategies · 37

ESRI: Environmental Systems Research Institute · 10

ESTG: Superior School of Technology and Management

· 2

Euclides: Fraunhofer JavaScript Procedural Modeler ·

2, 4, 5, 18, 81, 97, 98, 100, 101, 102, 103, 105, 106,

107, 109, 112

F

FAR: Floor Area Ratio · 12

FCT: Portuguese Foundation for Science and

Technology · 7

FDM: Fused Deposition Modeling · 68, 70, 71, 72

G

GA: Genetic Algorithm · 30

GC: Bentley Microstation GenerativeComponents · 13

GFA: Gross Floor Area · 12

GIS: Geographic Information Systems · 10, 40

GML: Generative Modeling Language · 16, 17, 18

GPU: Graphics Processing Unit · 51, 52

xxxii

H

HDE: Hybrid Differential Evolution · 39

HSM: High Speed Machining · 68

HVAC: Heating, Ventilation and Air Conditioning · 82,

83

I

ICEO: IEEE Competition on Evolutionary Optimization ·

37

IEEE: Institute of Electrical and Electronics Engineers ·

37

IPL: Polytechnic Institute of Leiria · 2, 52

L

LUA: Lightweight multi-paradigm programming

language designed as a scripting language with

extensible semantics as a primary goal · 87, 102,

103, 141

M

MTT: 2.3.1 Multitouch · 40

MTT4ALL: Multitouch Table developed by the Author

of this master thesis · 41, 42

N

NP: Number of Elements in Each Generation · 34, 36,

37

NURBS: Non-uniform rational basis spline · 14, 16

O

OLED: Organic Light-Emitting Diode · 58

P

PDM: Product Data Management · 8

PLM: Product Lifecycle Management · 8

R

Rhino: (a.k.a. Rhinoceros), it's a commercial NURBS-

based 3D modeling tool, developed by Robert

McNeel & Associates · 14; Rhinoceros (Robert

McNeel & Associates) · 14, 15

RICS: Royal Institution of Chartered Surveyors · 90, 140

S

SCG: Suicidator City Generator · 9

SDE: Stochastic Differential Equations · 37

SLM: Spatial Light Modulator · 48

SLS: Selective Laser Sintering process · 68, 69, 73, 74

STL: A file format native to the stereolithography CAD

software · 67

T

Tabletops: Horizontal Interactive Displays · 40, 41

V

VEs: Virtual Environments · 53

VR: Virtual Reality · 42, 43, 53, 54, 55

VRML: Virtual Reality Markup Language · 43

xxxiii

1

I.

Introduction

1 The Problematic

The adjustment of architectural forms to local and specific solar radiation conditions is a

fundamental study that must be always conducted by architects. When discussing energy

consumption and solar power harness in buildings, important topics of discussion come

into play, like the real relation between a building form and its energy behavior, or finding

the right building shape for a specific location and weather conditions on an all year basis.

Several studies were published so far, to try to answer and demonstrate these and other

important questions. Form follows energy, but how exactly is this happening it‟s somehow

difficult to demonstrate without having automated tools and models. One must try to

manually analyze the energy dependence between form and volume. With this kind of

studies, there is an attempt to simultaneously adapt a building form, in order to increase the

potential areas for solar radiation “reception” and at the same time looks for ways to

reduce the thermal loss (here the admittance method, well known by architects, it‟s useful),

taking in account the need to design for specific locations and specific weather conditions.

This research work aims, to examine the theoretical concepts associated to the problem of

“Form Follows Energy”, pointed out, in studies done by some researchers. Also, the

present study discusses emergent methods based on evolutionary algorithms and

environmental simulation tools and it targets the development of new design methods that

allow the construction of sustainable optimized buildings by using digital technologies,

through the creation of an automated tool and an optimization framework that will allow

the optimization of 3D shapes (buildings), taking in account the geo-location and specific

weather conditions, throughout the run of automated simulations, making autonomous

changes and optimizations utilizing evolutionary algorithms.

2

For the creation of this work, a strong collaboration between the Polytechnic Institute of

Leiria/Superior School of Technology and Management/Centre for Rapid and Sustainable

Product Development (IPL/ESTG/CDRSP) and Fraunhofer Austria was established.

The main research objectives of this work can be listed as follows:

(i) To give an overview of state of the art technologies and techniques

currently employed in the architecture field, regarding simulation and

analysis, visualization, rendering, virtual interaction, rapid prototyping,

reverse engineering and automated construction;

(ii) To evaluate common shapes used in real world buildings, with focus on

greenhouses forms as a practical example case study;

(iii) To investigate, in order to obtain the necessary parametric equations

(required to the use of computer graphics in the creation of procedural 3D

models) for the several identified common building shapes. Also, to

extract the essential parameters (height, width, length, orientation, roof

angle…) of those fundamental shapes, in order to achieve a fully

parametrical defined 3D model, this will allow the use of Euclides

(JavaScript Procedural Modeler);

(iv) To research on the possibility of having a programming integration with

commonly used simulation packages and tools, to simulate how the

different shapes of buildings have an influence in energy consumption

throughout the life of these real buildings, with the final purpose of

developing an automated tool capable of running automated simulations;

3

(v) To make use of evolutionary algorithms in order to perform autonomous

and automatic optimizations of 3D shapes based on automated simulations

and the well-known method of admittance, always employing tools and

methods which are widely accepted in the architecture field. The final goal

is the creation of a global optimization framework for automatic

generation of optimized 3D building forms, also taking in account the

specific location weather conditions;

(vi) To present and explain the importance of the results achieved with the

developed global optimization framework, pointing out new directions in

sustainable architecture design;

Presently, this study is applied to architecture and sustainability. But it must be referred

that this problematic of evolutionary architecture and simulation tools integration can be

extended to other domains/fields, like the industrial or the medical field. They could also

benefit from an autonomous generation of different 3D shapes, as well as a self-governing

optimization of those same 3D forms.

4

1.1 Thesis Structure

The thesis is divided into five chapters, which develops in accordance with the identified

research objectives. This first chapter (Introduction) comprises an introduction that in

addition to listing the key objectives, also briefly describes the context of the research.

The contents of the remaining chapters are summarized as follows:

 State of the Art

Reviews the latest work developed around procedural modeling, visualization

techniques, digital fabrication and reverse engineering. It also presents and

describes a JavaScript Framework, named “Euclides”, utilized for the easy creation

of 3D procedural and parametric shapes. This was the procedural framework used

in this thesis for the creation of all the necessary parameterized 3D buildings forms.

Lastly, a briefly explanation of how evolutionary algorithms work, is also given.

Moreover, a specific evolutionary algorithm (Differential Evolution - DE) is

described, as well as the reasons why this particularly algorithm was chosen in this

thesis, for the development of an automated tool for 3D shapes (buildings forms)

optimization. Other evolutionary algorithms are pointed out too, as plausible

alternatives to be implemented within the optimization framework in future work,

in order to tackle other problematic;

 Simulation Tools in Architecture

Gives an overview of different interests in simulation, in particular those related to

the problematic of architecture and energy consumption in buildings. Some

simulation tools/packages are presented, together with the reasons for selecting a

particular tool to be used in the work presented here;

5

 A Global Optimization Framework

Explains the work developed throughout this thesis, on the problematic of how

buildings form affects the energy consumption on a daily basis throughout its entire

life. Several methodologies used for choosing parameters to control a specific 3D

shape as well as other tools used to deduce parametric equations and “mathematical

code”, are also described with the objective to show how these 3D parametric

models were generated using Euclides and JavaScript. Also, the general concept of

the developed optimization framework is explained. A practical overview of the

work is given, every framework component is presented in more detail and the

achieved results are presented and clarified. Finally a case study is presented, where

the problem of automatic optimization is extremely relevant and the results

obtained are then presented and explained;

 Conclusions and Future Work

Provides an overall summary of the thesis and points out further progress paths and

improvement options for the autonomous global optimization framework that was

developed and presented in this thesis;

7

II.

State of the

Art

2 Overview

A review on the state of the art is presented, regarding current work focused on procedural,

parametric and adaptive architecture modeling. A short description of evolutionary

algorithms is given. Also, innovative methods of visualization and presentation of

architecture projects are presented, as well as several techniques for rapid prototyping and

reverse engineering. These methods and techniques are essential to capture 3D geometry,

for achieving more complete results on any architecture project (e.g. production of scale

models for simulation in wind tunnels, virtual simulation, building control…) and essential

for presenting the achieved results to final customers (rendering, interactivity…), also, the

author of this thesis was a research member of the CDRSP Research Centre and earned a

scholarship, on the topic “Build-it-Green”, from the Portuguese Foundation for Science

and Technology (FCT). This “Build-it-Green” topic is closely related to the architecture

subject and some of the work that was developed at CDRSP by the author, was focused on

these same areas and it was conducted throughout the realization of this master thesis.

This state of the art review, aims to present a short explanation about each product,

methodology or recent development. For getting more insightful details, the correspondent

references should be further investigated.

8

2.1 Parametric and Procedural Modeling

Parametric Computer Aided Design (CAD) modeling assumes, nowadays, an important

role in the definition of 3D models. There are several active attempts to collect all the

information about a product or about the different parts that compose a product.

Information platforms like Product Data Management (PDM) or Product Lifecycle

Management (PLM) [1], offer a way to gather the different distributed data that is vital for

an efficient product management. However there is some “intelligent” information that

must be captured with each part and product assembly, such as parametric information

(e.g. width, height, volume, orientation, length, relations between parts, formulas …). Also

semantic methods, using ontologies, try to present solutions for solving problems like the

relationship between different, yet related 3D geometric information [2]. Procedural

modeling can be viewed as the use of different techniques in computer graphics to create

(generate) 3D models and textures from sets of rules. L-Systems, fractals, and generative

modeling are procedural modeling techniques since they apply algorithms for producing

scenes. The set of rules may either be embedded into the algorithm, configurable by

parameters, or a set of rules that is completely separated from the evaluation engine. The

output is then called procedural content, which can be used in computer games, films, be

uploaded to the internet while requiring much less bandwidth, or the user may edit the

content manually [3].

Procedural models often exhibit database amplification, meaning that large scenes can be

generated from a much smaller amount of rules. If the employed algorithm produces the

same output every time, the output needs not to be stored. Often, it is sufficient to start the

algorithm with the same random seed to achieve the same result. Although all modeling

techniques on a computer require algorithms to manage and store data at some point,

procedural modeling focuses on creating a model from a rule set.

Procedural modeling is often applied when it would be too cumbersome to create a 3D

model using generic 3D modelers, or when more specialized tools are required, this is

often the case for plants, architecture or landscapes [4].

9

2.1.1 Plugins for existent 3D Software: Blender, 3D

Studio Max and Others

There are many plugins available on the internet for use within commonly used 3D

modeling software, like Autodesk 3D Studio Max, Autodesk Maya or the open source

modeling software Blender and many others that allow the automatic generation of terrain,

buildings or even cities in a procedural way.

These plugins permit the creation of 3D models according to specified rules and custom

parameters specified by the user, they can also be customized through the use of scripting

languages like, Python or MEL. Suicidator City Generator (SCG) is a wonderful example

of such plugin for use inside Blender. It is a Python script for Blender or in other words it

is a program written in the Python programming language that runs inside the Blender

environment [5].

It‟s not the purpose of this work to explain in detail how these plugins perform, however

they must be mentioned here as an existent and possible path for the creation of generative

components in today‟s 3D modeling software packages.

Fig. II-1 – Suicidator City Generator (SCG) for Blender

10

2.1.2 CityEngine

City Engine (now acquired by ESRI) is one of the most successful and powerful examples

for procedural modeling, it‟s a standalone software that provides a unique conceptual

design and modeling solution for the efficient creation of 3D cities and buildings, for

professional users in entertainment, architecture, urban planning, Geographic Information

Systems (GIS) and general 3D content production [4]. CityEngine was also tested in this

master thesis study.

Fig. II-2 – Procedural/parametric examples created in CityEngine

The key highlights of CityEngine include [6]:

 GIS/CAD Data Support and OpenStreet Map Import

CityEngine supports industry standard formats like, ESRI Shape file or DXF which

allow the import/export of any geo-spatial/vector data such as parcels, building

footprints with arbitrary attributes, or line data to create street networks. To copy

real cities or efficiently create an urban environment for our design, it‟s possible to

use data from OpenStreet Map. Geospatial data of real cities can also be

downloaded and directly imported it into CityEngine;

11

 Dynamic City Layouts and Street Networks Patterns

An intuitive toolset is provided to interactively design, edit and modify urban

layouts consisting of (curved) streets, blocks and parcels. Street construction or

block subdivision is controlled via parametric interfaces, giving immediate visual

feedback; CityEngine offers unique street grow tools to quickly design and

construct urban layouts. Street patterns such as, grid, organic or circular, are

available and the topography of the terrain is taken into account;

 Rule-based Modeling Core

Procedural modeling based on Computer Generated Architecture rules (CGA shape

grammar) offers unlimited possibilities to control mass, geometry assets,

proportions, or texturing of buildings or streets on a city-wide scale. We can define

our own rules using custom textures/models in the node- or text-based rule editor;

 Facade Wizard and Parametric Modeling Interface

One can quickly create rules out of an image or a textured mass model with this

simple and easy-to-use visual facade authoring tool. The resulting facade rules are

size-independent, contain level-of-detail and can be extended with e.g. detailed

window asset. A convenient interface to interactively control specific street or

building parameters such as the height or age (defined by the rules) is provided and

with the live mode, parameter modifications invoke the automatic regeneration of

the 3D model;

 Map-Controlled City Modeling and Reporting (Building Information Modeling -

BIM for Cities)

Any parameter of the buildings and streets can be controlled globally via image

maps (for example the building heights or the land use-mix); this allows for

intuitive city modeling and quick changes on a city-wide scale. Furthermore,

terrains can be imported, aligned, and exported. Customized rule-based reports can

be generated to analyze the urban design e.g. automatically calculate quantities

12

such as Gross Floor Area (GFA), Floor Area Ratio (FAR), etc. Reports are updated

automatically and instantaneously and can be made for whole city parts;

 Industry-Standard 3D Formats

CityEngine supports Collada, Autodesk FBX, 3DS, Wavefront OBJ and e-on

software's Vue, which allow for flawless 3D data exchange; FBX and Collada

support asset instancing, multiple UV-sets, grouping and binary encoding;

furthermore, scenes can also be exported to RenderMan RIB or Mental Ray MI

format. Textures can be collected during (batch) export;

 Python

Allows streamlining repetitive or pipeline-specific tasks with the integrated Python

scripting interface (e.g. write out arbitrary meta-data or instancing information for

each building, import FBX cameras, etc...). CityEngine is also available for

Windows (32/64 bits), Mac OSX (64 bits), and Linux (32/64 bits).

Fig. II-3 – CityEngine IDE and the Rule Editor Capabilities

13

2.1.3 Bentley – MicroStation Extension:

GenerativeComponents (GC)

Designers have (since the dawn of times), wanted to innovate. Indeed, innovation is widely

regarded as a trophy that awaits creative professionals who successfully explore endless

design alternatives to ultimately arrive at the most efficient solution - a process that can be

incredibly time consuming as each alternative is thoroughly modeled and assessed. Using

the existing tools, a minor change to a design may require a major update to the model,

thus restricting the number of design alternatives considered by the team due to time

constraints. GenerativeComponents is an associative parametric modeling system used by

architects and engineers to automate design processes and accelerate design iterations. As

an innovation by MicroStation, GenerativeComponents extends proven technologies and

delivers significant advantage to users as they rapidly explore a broad range of design

alternatives. With a hybrid approach, designers who use GenerativeComponents can model

geometry, capture relationships, and generate forms using scripts and/or direct

manipulation for unrivalled creative flexibility [7].

Fig. II-4 – GenerativeComponents (GC) IDE, Bentley MicroStation

This combination of accelerated iteration, flexible modeling, and automated process,

means that a GenerativeComponents design can be highly efficient, benefiting from a

combination of intuition and logic [7].

14

2.1.4 Rhinoceros and Grasshopper

Rhino (a.k.a. Rhinoceros) is a stand-alone, commercial NURBS-based 3D modeling tool,

developed by Robert McNeel & Associates. The software is commonly used for industrial

design, architecture, marine design, jewelry design, automotive design, CAD / CAM, rapid

prototyping, reverse engineering as well as the multimedia and graphic design industries.

Fig. II-5 – Rhinoceros is used in multiple fields, including architecture

Rhino is specialized in free-form non-uniform rational B-spline (NURBS) modeling. Plug-

ins developed by McNeel includes Flamingo (retrace rendering), Penguin (non-

photorealistic rendering), Bongo and Brazil (advanced rendering). Over one hundred third-

party plugins are available. There are also rendering plug-ins for Maxwell Render, V-ray

and many other engines. Additional plugins for CAM and CNC milling are available as

well, allowing for tool path generation directly in Rhino. Like many other modeling

applications, Rhino also features a scripting language, based on the Visual Basic language

and an SDK that allows reading and writing Rhino files directly. Rhino 3D gained its

popularity in architectural design in part because of the Grasshopper plug-in for

computational design. Many new avant-garde architects are using parametric modeling

tools, like Grasshopper. Rhino's increasing popularity is based on its diversity, multi-

disciplinary functions, low learning-curve, relatively low cost, and its ability to import and

15

export over 30 file formats, which allows Rhino to act as a “converter” tool between

programs in a design workflow. The combination between Rhino and Grasshopper is just

perfect to create all kinds of parametric studies and developments on any field. The power

offered by Rhino and Grasshopper is just amazing. There are also many other plugins

available (rendering, math, physics, kinematics…).

Fig. II-6 – Voronoi Examples, created using Rhinoceros and Grasshopper, by Atsuo Nakajima (Tokyo,

Japan)

For designers who are exploring new shapes using generative algorithms, Grasshopper is a

graphical algorithm editor tightly integrated with Rhino‟s 3D modeling tools [8]. In Fig.

II-7, by using Grasshopper, a building and its structural supports are generated and

calculated using only two splines created initially in Rhino.

Fig. II-7 - Parametric Strategies achieved using Rhino and Grasshopper. (Created by the author of

this master thesis)

Unlike RhinoScript, Grasshopper requires no knowledge of programming or scripting, but

still allows designers to build form generators from the simple to the remarkable [8].

16

2.1.5 Generative Modeling Language (GML)

Traditionally, 3D objects and virtual worlds are defined by lists of geometric primitives:

cubes and spheres in a Constructive Solid Geometry (CSG) tree, NURBS patches a set of

implicit functions, a soup of triangles, or just a cloud of points.

The term “generative modeling” describes a paradigm change in shape description, the

generalization from objects to operations: A shape is described by a sequence of

processing steps, rather than just the end result of applying operations. Shape design

becomes rule design. This approach is very general and it can be applied to any shape

representation that provides a set of generating functions, the “elementary shape

operators”. Its effectiveness has been demonstrated, e.g., in the field of procedural mesh

generation, with Euler operators as complete and closed set of generating functions for

meshes, operating on the half-edge level [9].

Fig. II-8 - Creation of a simple house model using GML, the extrude operator is repeatedly applied to

the ground polygon. To create the roof, the combined operator collapse-mid is applied to the faceCW

and faceCCW edges of the edge returned by the extrude operation.

Generative modeling, gains its efficiency through the possibility to create high-level shape

operators from low-level shape operators. Any sequence of processing steps can be

grouped together to create a new “combined operator”. It may use elementary operators, as

well as other combined operators. Concrete values can easily be replaced by parameters

which makes possible the separation of data from operations: the same processing

sequence can be applied to different input data sets. Data can be used to produce different

shapes by applying different combined operators, from, e.g., a library of domain-dependent

modeling operators. This makes possible the creation of very complex objects from only a

few high-level input parameters, such as, a style library [2].

17

Fig. II-9 – Parameterization/Configuration of a Chair with GML

GML is a concrete implementation of the generative approach. Its main feature is that it is

a full functional programming language that can nevertheless be efficiently used as a file

format for low-level shape descriptions. Only 25 Kilobytes GML code of a Gothic window

style library are sufficient to generate connected manifold control meshes for a variety of

windows [10].

Fig. II-10 – Gothic Style Building generated with GML

The GML comes with an integrated visualization engine. Thus, it can also be seen as a

viewer with an integrated modeler that overcomes the usual separation of 3D modeling

from interactive visualization. Curved parts are represented as subdivision surfaces that,

within 1-2 seconds, unfold to seven million vertices after four steps of recursive

refinement. The surface is adaptively displayed at interactive rates using optimized

methods for culling and per-face per-frame multi-resolution rendering [11].

Fig. II-11 – Configuration of Different Wheel Rim Styles using GML

18

2.1.6 Euclides Framework and JavaScript

Enabling an easy access to programming languages that are usually difficult on a direct

approach will dramatically potentiate their use. GML [9] is such a language and can be

described as being similar to Adobe‟s PostScript. A major drawback of all PostScript

dialects is their unintuitive reverse Polish notation, which makes both - reading and writing

- a burdensome task. According to Strobl, M., et al. [12] a language should offer a

structured and intuitive syntax in order to increase efficiency and avoid frustration during

the creation of code. To overcome this issue, Strobl, M., et al. [12] propose a new approach

to translate JavaScript code to GML automatically. Within the last few years generative

modeling techniques have gained attention especially in the context of cultural heritage.

Because a generative model describes a rather ideal object and not a real one, generative

techniques are a basis for object description and classification. This procedural knowledge

differs from other kinds of knowledge, such as declarative knowledge, in a significant way.

It can be applied to a task. This similarity to algorithms is reflected in the way generative

models are designed: they are programmed. In order to make generative modeling

accessible to cultural heritage experts, Schinko, C., et al. [13] created a generative

modeling framework which accounts for their special needs. The result is a generative

modeler called Euclides based on an easy-to-use scripting language (i.e. JavaScript). The

generative model meets the demands on documentation standards and fulfills sustainability

conditions and its integrated meta-modeler approach makes it independent from hardware,

software and platforms.

Fig. II-12 – Example of a 3D application created for this thesis using Euclides and JavaScript. It allows

the control of several shape parameters on the “Classic Building form example”.

19

2.1.7 Autodesk Revit Architecture 2012

In the latest years, Autodesk made a strong effort in incorporating new technologies (e.g.

multitouch…) and new amazing functionalities (physics, energy analysis, parametric

design) in existing products like Autodesk Revit 2012 or Maya 2012, making a strong

contribution for the development of really innovative products.

Autodesk Revit Architecture can be used to create massing designs; explore design

alternatives based on qualitative and quantitative feedback; and help address various

environmental, constructability, and aesthetic concerns that can arise during project

realization [14].

Fig. II-13 – Energy Consumption Study using Autodesk Revit

In the early stages of a design, visualizing a concept in 3D enhances a designer‟s ability to

communicate ideas. Analyzing these ideas yields the ability to predict and optimize the

real-world performance of the built project.

These attributes form a core value of the Building Information Modeling (BIM) process,

which Revit Architecture software is purpose-built to support.

20

In Autodesk Revit Architecture, users have access to a robust collection of easy-to-use

modeling tools that facilitate design conceptualization, visualization, and communication.

This software supports several new modeling operations, including adaptive, component-

driven geometry, robust UV grid manipulation and increased schedule functionality

through reporting parameters. In addition, Revit users on Autodesk Subscription can now

access tools that enable them to better assess the impact of their early design decisions on

energy consumption and carbon emissions without leaving the Revit environment. In order

to clearly illustrate a complete workflow using the conceptual design and analysis tools

and to address the new features introduced with the previous release [14]:

 The Project requirements section outlines the criteria that will drive the building

design;

 The Parametric Massing Design section describes the steps taken to explore

massing design alternatives informed by qualitative and quantitative feedback;

 The Site and Environmental Analysis section addresses the impact of building mass

and orientation on energy consumption and overshadowing;

 The custom “Panelization” section uses the mass design options generated in the

first section as the basis for informed panel‟s studies.

Fig. II-14 – Conceptual Design in Autodesk Revit Architecture

21

2.1.8 Project Vasari and Project Nucleus

Autodesk Project Vasari is an easy-to-use, expressive design tool for creating building

concepts and it‟s build on the same technology as the Autodesk Revit platform.

Project Vasari goes further, with integrated analysis for energy and carbon, providing

design insight where the most important design decisions are made. And, when it‟s time to

move the design to production, simply bring your Vasari design data into the

Autodesk Revit platform for BIM, ensuring clear execution of design intent.

Project Vasari is still under development and is primarily intended to reduce the building

energy loads, not to replace the more detailed analysis tools. It is able to produce

conceptual models using both geometric and parametric modeling functionality. The

designs can be analyzed using the built-in energy modeling and analysis features. The tools

depends on Green Building Studio (Autodesk‟s green building analysis web service) in

many input energy related parameters [15].

Fig. II-15 – Sun Studies using project “Vasari”

Project Vasari is focused on conceptual building design using both geometric and

parametric modeling. It supports performance-based design via integrated energy modeling

and analysis features. This new technology preview is now available as a free download

and trial on Autodesk Labs.

22

Project Nucleus integrates the Nucleus simulation engine from Autodesk Maya into

Autodesk Revit Architecture and Project Vasari. It allows designers to experiment with

"form-finding" in the conceptual design phase by simulating forces directly in Revit

Architecture and Project Vasari (the latest technology preview of Project Vasari already

includes the Project Nucleus functionality).

Fig. II-16 - Panel Study using Revit and Vasari

Project Nucleus can simulate a wide range of physical phenomena in real time, like wind,

gravity, constraints, and collisions. These forces can help architects generate free-form

shapes, many of which would be impossible to model by hand [16].

Fig. II-17 - Using Revit, Vasari and Nucleus Physics for a Panel Study, plus Analysis

23

2.1.9 Autodesk Adaptive Components

Adaptive geometry can be sized and positioned in the context where it is used. When you

designate under constrained geometry as adaptive, you specify the geometric elements

allowed to change, while controlling the elements that you want to remain a fixed size or

position [17].

“Adaptivity” is the functionality, within Inventor, that allows the size of a part/feature to be

determined by setting a relationship to another part in an assembly. Basically, “adaptivity”

is a special way to add constraints. These constraints differ from regular constraints in that

they are driven from a separate file. This separate file can be an assembly file or another

part within the assembly file.

A good example of “adaptivity“, is constraining a shaft to a hole in another part. If set up

correctly, when the size of the hole changes the diameter of the shaft updates as well.

“Adaptivity” is normally used during the initial design phase of a model, when changes are

made rapidly and many parts are affected.

Fig. II-18 – Adaptive Components in Autodesk Revit

24

Once a design is released, and parts become standard parts, available for use in other

designs, “adaptivity” should be removed to eliminate the possibility of inadvertently

changing a released design. Removing “adaptivity” also improves performance.

Fig. II-19 – Adaptive Panel example in Autodesk Revit

As with using any other constraint, forethought should be given to how a design may

change before “adaptivity” is applied. If a part is not likely to change, it is better to apply

normal (non-adaptive) constraints. “Adaptivity” should be used only when absolutely

necessary [17].

Fig. II-20 – Another Adaptive Panel example, built using Adaptive Components

25

2.2 Evolutionary Architecture and the Use of

Algorithms in Optimization of Problems

The first references to this field of computation, Evolutionary Solvers or Genetic

Algorithms [18], can be found in the early 60's when Lawrence J. Fogel published the

revolutionary paper "On the Organization of Intellect" [19] which steered the first

endeavors into evolutionary computing. The early 70's saw further ventures with important

work produced by Ingo Rechenberg and John Henry Holland (and others) [20].

Evolutionary Computation didn't gain popularity beyond the programmer world until

Richard Dawkins (one of my favorite authors) came out with the book, "The Blind

Watchmaker" in 1986 [21], which was published with a small program that generated an

apparently endless stream of body-plans called "Bio-morphs" based on human selection.

Fig. II-21 – Image taken from the book “The Selfish Gene” by Richard Dawkins

After the 80's, the dawn of the personal computer has made it possible for individuals

without government funding to apply evolutionary principles to personal projects and

making it a common jargon. The term "Evolutionary Computing" is very well commonly

known at this time, but is still very much a programmer‟s tool (by programmers and for

programmers) [18, 22].

26

The applications out there that apply evolutionary logic are either aimed at solving specific

problems or they are generic libraries that allow other programmers to develop their own

software [21].

Fig. II-22 – Several visions related to Evolutionary Architecture and Biomimetic

One of the most important works ever published published about Evolutionary

Architecture was the book of John Fraser, “An Evolutionary Architecture” [23], in the

book introduction one can read: “…in this book the author investigates the fundamental

form-generating processes in architecture, considering architecture as a form of artificial

life, and proposing a genetic representation in a form of DNA-like code-script, which can

then be subject to developmental and evolutionary processes in response to the user and the

environment. The aim of an evolutionary architecture is to achieve in the built

environment, the symbiotic behavior and metabolic balance found in the natural

environment. To do so, it operates like an organism, in a direct analogy with the underlying

design process of nature”.

Fig. II-23 – Evolutionary examples taken from the book “An Evolutionary Architecture” by John

Fraser [23]

27

Also, Gordon Pask wrote on his foreword on this same book: “The book also proposes a

fundamental change in practice… „The role of the architect here, I think, is not so much to

design a building or city as to catalyze them: to act that they may evolve‟. Promising

sustainable design methods are unquestionably emerging through the use of evolutionary

computation and environmental simulation tools, as this is indeed an essential need in

today‟s architecture world”.

Fig. II-24 – Dynamic Geometry Computation, “Shanghai Tower - Geometry Generate and Rendering”

(Michael Peng)

Eugene Tsui on his work and book “Evolutionary Architecture: Nature as a Basis for

Design” [24] and also Javier Senosiain, Michael Paulin, William McDonough, Renzo

Piano and many others architects incorporate in their projects ecological and sustainable

principles, but also integrate an understanding that constructions require “an holistic

approach studying the form, materials and efficiency that Nature have becoming the

infallible mentor in the creation of an comfortable and symbiotic world” [25].

Fig. II-25 – Ecological House of the Future (Eugene Tsui)

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2.2.1 Differential Evolution (DE)

Differential Evolution (DE) [26] has been very successful in solving the global continuous

optimization problem [27]. It mainly uses the distance and direction information from the

current population to guide its further search. The global optimization problem arises in

almost every field of science, engineering or business, and an enormous amount of effort

have been devoted to solving this problem. The major challenge of the global continuous

optimization is that the problems to be optimized may have many local optima (Sun, J., et

al. [27]).

Fig. II-26 – The Global Optimization Problem (example in Matlab). Search of the highest mountain

peak among a neighborhood of other high mountains peaks.

Evolutionary Algorithms (EA‟s) are similar to the evolution process of a biological

population which can adapt to the changing environments in order to find the optimum of

the optimization problem by evolving a population of candidate solutions. Differential

Evolution (DE) is one of the most successful EAs for the global continuous optimization

problem. Several examples of problem solving using DE were already presented in the

past, particularly those ones presented by the creators of the DE algorithm (Price, K. S., et

al., [28]).

29

Evan Greenberg [29] discusses in is master thesis a natural behavior called “Branching”

that occurs in natural systems for functional reasons. The branching logic for each specific

system is quite different due to environmental and mathematical factors. In the

computation of branching systems, these mathematical factors can be incorporated easily

into the coding of each system. Nevertheless, the environmental components deserve

further consideration in the simulation of these natural systems. Through the engine of

genetic algorithms based on evolutionary developmental theory, the specific logics

observed and analyzed in branching patterns of river systems or trees, can be simulated and

optimized in a digital environment.

Fig. II-27 - Observation, Analysis and Computation of Branching Patterns in Natural Systems (by

Evan Greenberg [29])

There are some biological terminologies which are used in evolutionary algorithm

implementations, such as:

 Individual: an autonomous piece characterized by a chromosome. In this case, one

possible solution to the design problem;

 Population: a group of individuals;

 Population Size: the number of individuals in a population Gene (a functional block

of DNA);

30

 Allele: A possible value of a gene;

 Chromosome: Strings of DNA. In this case, a list of parameters;

 Locus: The place of a gene in a chromosome.

In evolutionary algorithms there are also three different types of operators: Selection,

Crossover, and Mutation. After initializing the parameters, these three operators are

iterated until the results satisfy the terminal criteria defined.

Each step of the algorithms is explained as follows (Kawakita, G. [30]):

 Initialization - In this step, some parameters including the population size, number

of generations and so on are entered. After that, the initial input randomly generates

genotype individuals of the first generation. Particularly, population size is

significant in terms of the operations, the lengthier the chromosome length is, the

bigger the population size is. Additionally, a bigger population size requires longer

calculation time until convergence. However, small population sizes may result in

premature and undesirable convergence;

 Evaluation - Fitness scores are calculated for further selection of fitter

chromosomes. One of the most important aspects in this step is the Fitness Function

which calculates the fitness measurement of each individual. This operation is

deeply related to the efficiency of the whole Genetic Algorithm (GA) flow;

therefore, it needs to be determined carefully;

 Selection - The fitter chromosomes in the population are basically selected for

reproduction. As in biological evolution, the fitter chromosomes are more likely to

be selected and reproduced in each generation. Meanwhile, lower fitness

chromosomes are also possibly selected, but with a lower probability. This

probabilistic selection depends on the selection method. There are several types of

selection such as elite selection, roulette selection, tournament selection, etc. Each

selection type has advantages and disadvantages. For instance, in elite selection, the

31

fitter chromosomes are certainly selected in order; however, premature

convergence is highly possible;

 Crossover - Crossover roughly mimics the genetic operation of biological

recombination between two chromosomes. The fitter chromosomes are chosen by

the selection operator. However, it is not effective enough to evolve the population.

The crossover operator encourages more variation by exchanging genes between

two chromosomes;

 Mutation - The mutation operator randomly flips or changes genes in a

chromosome between alleles, generally with a very low probability. Chromosomes

generated by the crossover operator are basically copies of the parent

chromosomes. Therefore, premature convergence possibly occurs. Chromosomes

that have been mutated help to avoid premature convergence. Generally speaking,

the mutation rate should be 1/L, where L is the length of chromosome. Moreover, if

the mutation rate is too big, the algorithm becomes similar to a random search;

 Terminal Criterion - In this step, the conditions required to terminate the algorithm

is evaluated. If the process is regarded as being completed, the fittest individual in

the generation is outputted as one of the possible optimum solutions.

The general conditions of convergence in evolutionary algorithms are as follows:

 If the fittest score in the population satisfies the certain target – star gene;

 If the average fitness score in the population satisfies the certain target –

population improvement;

 If the increase or decrease of fitness scores in the population becomes below a

certain value – convergence;

 If the number of generations becomes over the defined value – finite iteration.

32

Fig. II-28 – Simple EA’s steps

As it happens with every algorithm, there are several different variations of the differential

algorithm, in order to classify the different variants, the notation: DE/x/y/z was introduced,

where:

 x specifies the vector to be mutated which can be “rand” (a randomly chosen

population vector) or “best” (the vector of lowest cost from the current population);

 y is the number of difference vectors used;

 z denotes the crossover scheme. Example: “bin” (Crossover due to independent

binomial experiments).

Using this notation, the basic DE-strategy that is generally described can be written as:

DE/rand/1/bin, but there are other variants, e.g. DE/best/2/bin.

33

The DE algorithm work‟s (in general) as follows (Price, K. S., et al.[28]):

1. The DE algorithm maintains a population of N points in every generation, where

each point is a potential solution and N is a control parameter;

Then the algorithm evolves and improves the population iteratively:

2. In each generation, a new population is generated based on the current population;

3. To generate descendants for the new population, the algorithm extracts distance and

direction information from the current population members and adds random

deviation for achieving diversity;

4. If an offspring has a lower objective function value than a predetermined

population member, it will replace this population member;

5. This evolution process continues until a stopping criterion is met (e.g., the current

best objective function value is smaller than a given value or the number of

generations is equal to a given maximum value).

Fig. II-29 – General Evolutionary Algorithm: i: initialization, f(X): evaluation, ?: stopping criterion,

Se: selection, Cr: cross-over, Mu: mutation, Re: replacement, X*: optimum. Author: Johann "nojhan"

Dréo

34

The optimization method known as Differential Evolution (DE) has several parameters that

determine its behavior and efficacy in optimizing a given problem. The selection of good

parameters for DE it is an important question that is discussed by Pedersen, M. [31], this

paper gives a list of good possible choices of parameters values for various optimization

scenarios with the intention to give an easy help when choosing the best values for

achieving the best results, these interrelated parameters are: Crossover (CR), usually a

good initial value for CR would be 0.9 or 1.0 to check if a quick solution is possible,

Number of evaluations (Fitness Evaluations), Number of elements in each generation (NP),

a good value for NP is between 5*D (D = Dimension of the problem) and 10*D, but NP

must be at least 4 to ensure that DE will have enough mutually different vectors to which

to work on, Differential Weight (F), a good initial value for F is usually 0.5, Number of

variables of the problem (Problem Dimensions) and Size of the domain for each variable of

the problem.

Fig. II-30 - DE optimization performance (Pedersen, M. [31]) on several different problems using

DE/rand/1/bin algorithm. Plots show the mean fitness achieved. Over 50 optimization runs

35

In simple terms, optimization is the attempt to maximize a system‟s desirable properties

while simultaneously minimizing its undesirable characteristics. What these properties are

and how effectively they can be improved depends on the problem at hand (Price, K. S., et

al. [32]).

The optimization method known as Differential Evolution (or DE) was originally

introduced by Storn and Price [32] and offers a way of optimizing a problem without using

its gradient. This is particularly useful if the gradient is difficult or even impossible to

derive (Pedersen, M. [31]). DE maintains a population of agents which are iteratively

combined and updated using simple formula to form new agents. The general purpose

optimization method known as DE has a number of parameters that determine its behavior

and efficacy in optimizing a given problem, these parameters must be chosen accordingly

[31, 32]. Small changes to the DE implementation can cause dramatic changes in the

behavioral parameters that cause good optimization performance. The parameters given by

Pedersen [31] have been tuned for the DE/rand/1/bin algorithm. If other DE

implementation is chosen, different parameters values must be selected.

According to Storn and Price [26], the DE algorithm (DE/rand/1/bin) was demonstrated to

converge faster and with more certainty than many other acclaimed global optimization

methods. DE also requires fewer control variables, it‟s robust, easier to use and fits itself

on parallel computation scenarios. Storn and Price [26] compared and tested DE against

other optimization methods in solving minimization problems. The DE algorithm uses

simultaneous search vectors in order to help escape local minima (not using only a usual

greedy criterion, where a new parameter vector is accepted if an only if it reduces the value

of the cost function).

Also DE was compared to Simulated Annealing [33] (annealing relaxes the greedy

criterion by occasionally permitting an uphill move, allowing a parameter vector to climb

out of a local minimum, however in a long run this method also leads to a greedy

criterion).

36

Usually users demand that a practical minimization technique should fulfill five

requirements:

1. Ability to handle non-differentiable, nonlinear and multimodal cost functions;

2. Parallelizability to cope with computation intensive cost functions;

3. Ease of use, i.e. only a few control variables are needed to steer the minimization.

These variables should also be robust and easy to choose;

4. Good convergence properties, i.e. consistent convergence to the global minimum in

consecutive independent trials.

Once more, according to Storn and Price [26], the DE algorithm was designed to fulfill all

of the above five requirements. To fulfill requirement (1) DE was designed to be a

stochastic (random, non-deterministic) direct search method. Requirement (2) is useful for

computationally demanding optimizations, DE fulfills this requirement by using a vector

population where the stochastic perturbation of the population vectors can be done

independently. To satisfy requirement (3) (contrary to basic local minimization methods

with annealing concept), DE´s self-organizing scheme takes the difference vector of two

randomly chosen population vectors to perturb an existing vector, the perturbation is done

for every population vector, according to the authors this is the crucial idea of DE. Finally

to fulfill requirement (4), good convergence properties are mandatory for a good

minimization algorithm, this can only be shown with extensive testing under various

conditions.

DE generates new parameter vectors by adding the weighted difference between two

population vectors to a third vector (mutation). The mutated vector‟s parameters are then

mixed (crossover) with the parameters of another predetermined vector (target vector), to

achieve the so called trial vector. If the trial vector yields a lower cost function value than

the target vector, the trial vector replaces the target vector in the following generation

(selection). Each population vector has to serve once as the target vector so that NP

competitions take place in one generation.

37

In one DE variant (DE/best/2/bin), the usage of two difference vectors, seems to improve

the diversity of the population if the number of population vectors NP is high enough.

The DE algorithm participated in the First International IEEE Competition on Evolutionary

Optimization (1st ICEO) and at that time, DE proved to be the fastest evolutionary

algorithm. Encouraged by these results Storn and Price [26] tested DE against other

preeminent minimization methods, such as, Annealing Methods, i.e., Annealed Nelder and

Mead strategy (ANM) and the Adaptive Simulated Annealing (ASA), also against,

Evolutionary Strategies (ESs)/Genetic Algorithms (GAs), i.e., Breeder Genetic Algorithm

(BGA) and the Evolutionary Algorithm with Soft Genetic Operators (EASY) and finally

against Stochastic Differential Equations (SDE). Regarding the test against ANM and ASA

methods, DE was the only strategy that could find all global minima on test suite, also on

most cases DE found the minimum in the least number of function evaluations. Regarding

the test against BGA and EASY, the DE performed favorably against those methods and

needed the least number of functions, DE exhibits superior performance when compared to

the SDE‟s reported best results, none of the DE trial runs (1000 trial runs) failed to find the

global minimum and the DE control variable settings could remain the same for most of

the test functions (indication of DE‟s robustness). As demonstrated by the authors, DE

outperformed most of the other mentioned minimizations approaches in terms of required

number of function evaluations necessary to locate a global minimum of the test functions.

DE is also easier to use as it requires only a few robust control variables which can be

drawn from a well-defined numerical interval, the DE method of self-organization is quite

remarkable. Future research should include a mathematical convergence proof like the one

that already exists for Simulated Annealing and also further research must be done to

determine why DE converges so well.

38

2.2.2 Pros and Cons of using Evolutionary

Algorithms (EAs)

EA‟s can be characterized by some features such as coded parameters, global heuristic

search, fitness-based selection, and so on [26].

 Pros of using Evolutionary Algorithms

o They are a very general search method, and are not specific to any particular

application;

o EA‟s are very effective at avoiding becoming trapped in local optima;

o They are remarkably flexible and able to tackle a wide variety of problems.

There are classes of problems which are by definition beyond the reach of

even the best solver implementation and other that are just very difficult to

solve by any algorithm, but majority of the problems encountered on a daily

basis really fall into the evolutionary solvable category;

o Evolutionary Algorithms are also quite forgiving. They will happily chew

on problems that have been under- or over-constrained or otherwise poorly

formulated;

o Because the run-time process is progressive, intermediate answers can be

harvested at practically any time. Unlike many dedicated algorithms,

Evolutionary Solvers can give us a never ending stream of answers, where

newer answers are generally of a higher quality than older answers. So,

even a prematurely aborted run will yield something which could be called

a result. It might not be a very good result, but it will be a result that can be

used.

39

 Cons of using Evolutionary Algorithms

o They are fitness-based, and it is necessary to calculate fitness scores for

each individual every generation, therefore, processing loads are very high.

In order to produce good solutions, bigger population sizes are generally

necessary, and the bigger population size causes higher processing loads;

o The most “serious” disadvantage is the complexity and variety of initial

parameters. Satisfactory solutions require accurate input of initial setup

parameters such as population size, mutation rate and so on; however, they

are changeable depending on the objectives. Therefore, it is somehow

complicated and unpredictable for beginner users to determine accurate

setup parameters, not only with this algorithm but with any other algorithm

as well.

2.2.3 Other Evolutionary Based Algorithms - DE/EDA

and Hybrid-DE

In a paper by Sun, J., et al. [27] a combination of Differential Evolution (DE) and

Estimation of Distribution Algorithm (EDA), called DE/EDA is presented, for the global

continuous optimization problem. DE/EDA combines global information extracted by

EDA with differential information obtained by DE to create promising solutions. In such

way, both the global information and local information are used to guide the further search.

DE/EDA was compared with the best version of the DE algorithm and best version of the

EDA on several commonly utilized test problems. According to Sun, J., et al. [27], the

experimental results demonstrate that DE/EDA outperforms the DE algorithm or the EDA

alone. Also, in another proposal a new approach is presented, the algorithm Hybrid

Differential Evolution (HDE) [34] is according to the authors a simple population based,

stochastic function method that constitutes in an extension from the original algorithm of

DE as introduced by Storn and Price [26].

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2.3 Advanced Rendering, Visualization and Interaction

Techniques in Architecture

Nowadays, one important factor in any project and more specifically in architecture is the

graphical presentation of the project. Presently it‟s possible to find many fantastic options

to present final data and information to final customers (or for discussion on an early phase

of the project), from simple presentation of 3D models to internet online services (e.g.

Microsoft Bing Maps, Google Earth, GIS…), 3D games engines, serious games, virtual

and augmented reality, professional rendering and holograms.

Moreover, a more advanced interaction with information can be achieved through the use

of multitouch, augmented reality, interactive visualizations, dashboards, game controllers,

face/hand recognition, haptic devices, airborne ultrasound sensors or brain control. Also,

all these technologies can be sometimes a replacement for other production techniques,

like prototyping, rapid prototyping or small productions, where the only purpose, is to

show or demonstrate the functionality of a product. It‟s not the purpose of this work to

explain in detail all these technologies, but a description of technologies that are

considered state of the art is presented, which in some cases, are technologies used by the

author of this work, throughout his master course.

2.3.1 Multitouch (MTT)

Multitouch designates a set of interaction techniques that allow users to control graphical

applications with several fingers. Multitouch devices consist of a touch screen (e.g.,

computer display, table, wall or touchpad), as well as software that recognizes multiple

simultaneous touch points, different from the standard touchscreen (e.g. computer single

touch touchpad, ATM machines) which recognizes only one touch point [35]. Tabletops

(a.k.a. Horizontal Interactive Displays) are fascinating interfaces with unique features. In

everyday work, on educational and entertainment environments, tabletops can provide a

stunning experience of intuitive interaction by utilizing direct touches and gestures, this is

ideal for small group collaborations.

41

Recent developments of various technologies such as display and multi-touch technologies

open up new possibilities to enrich interaction on horizontal interactive displays. The book

“Tabletops - Horizontal Interactive Displays” (Müller-Tomfelde, C. [36]) brings together

the current research in the domain of tabletops, the book integrates and summarizes

findings from the most important international tabletop research teams. It provides a state-

of-the art overview and allows for the discussion of emerging and future directions in

research and the technology of tabletops.

On the first year of this master course, the author of this thesis participated in the

development of a project called “MTT4ALL” [37]. This project aimed the development of

a horizontal multitouch device, employing all the knowledge acquired during the

realization of this master course (including topics like sustainability, temperature

simulation, molds construction, energy efficiency…), this equipment should also include

new functionalities, like the use of active 3D stereo.

Fig. II-31 – 3D Anaglyph and Active Stereo Visualization on a Multitouch DI Table (MTT4ALL

multitouch table was built by the author of this master thesis)

On the second year of the master course, the author of this thesis (with support from

CDRSP) completed this project on his own, by building a fully functional prototype

according to the previously projected plans, this multitouch equipment is now part of the

virtual reality lab at CDRSP (which the author of this thesis also helped to create). This

equipment is actively used now on regular demonstrations at the CDRSP virtual lab.

42

Fig. II-32 – Initial MTT4ALL scale model (left) and Final MTT4ALL Functional Prototype in Use ,

running Fraunhofer virtualDesk Application, developed by the author of this master thesis for

Fraunhofer Austria (right)

In order to validate the architecture of the planned exhibition halls, the Frankfurt fair used

the instant reality player for interactive exploring the architectural 3D models. Starting

from the architectural 2D plans, a high quality 3D model of the booth area has been

generated by Mainfeld. This model is usually visualized using “instantPlayer” within a

CAVE environment or in a big wall screen.

Therefore the walk model has been implemented realizing intuitive navigation through the

VR model. In contrast to the free 3D flying mode the walk mode avoids the penetration of

walls and it supports a walking simulation on floors and stairs. Thus an intuitive navigation

algorithm has been implemented, giving special attention to users with no experience in 3D

and computer graphics. Using this architecture visualization the decision making process

of the architecture planning was supported. VR visualization of architecture has been

proofed to be a valuable planning tool.

The instantReality framework provides a comprehensive set of features to support both

classic Virtual Reality (VR) and advanced Augmented Reality (AR) equally well. The goal

was to provide a very simple application interface while still including the latest research

results in the fields of high-realistic rendering, 3D user interaction and total-immersive

display technology.

43

The system design includes various industry standards, like VRML and X3D, to allow an

easy application development and deployment [38].

Fig. II-33 – Fraunhofer, Multitouch Architecture Visualization – Messe Frankfurt GmbH (using the

instantReality Framework)

2.3.2 Virtual and Augmented Reality

One particular type of visualization is the Virtual Reality (VR), where the visual

representation of information is presented using an immersive display device (e.g. a stereo

projector for achieving stereoscopy). VR is also characterized by the use of a spatial

metaphor, where some aspect of the information is represented in three dimensions so that

humans can explore the information as if it were present (where instead it is remote), sized

appropriately (where instead it was on a much smaller or larger scale than humans can

sense directly), or had shape (where instead it might be completely abstract) [39].

It‟s possible to see in the next images, two examples of stereoscopy created by the author

of this thesis at the CDRSP Research Centre. A 3D stereo visualization enables users to

feel immersed (as if they were inside the simulation/virtual scene).

44

In Fig. II-34, we can see a passive anaglyph visualization that can be experimented by

wearing one of those existing cyan/red glasses.

Fig. II-34 – Anaglyph Visualization (created by the author of this master thesis)

In Fig. II-35, an active stereo visualization that can be perceived by wearing the new

nVIDIA Vision active stereo glasses.

Fig. II-35 – Active Stereo Visualization (created by the author of this master thesis)

Augmented Reality (AR) combines real and virtual, is interactive in real time and is

registered in 3D (Azuma, R. [40]). In other words: AR is a paradigm shift in accessing,

understanding and experiencing information as it combines the digital and the real world

around us (through real time video capturing, using cameras).

45

With Augmented Reality it is possible to make a more natural experience. The company

“metaio” [41] is without a doubt a reference in this field. Also Fraunhofer have several

research teams working around this topic, in Fig. II-36, we can see an augmented reality

system developed by Fraunhofer IGD that allows seeing the “Old Roman Forum ruins”

[42], the installation uses only a monitor, a camera to film the real environment and virtual

reality software to superimpose virtual objects as if they were really there (by recognition

of a printed giant photo placed on the walls of the museum).

Fig. II-36 – An Augmented Reality system developed by Fraunhofer (Monitor + Camera + Virtual

Reality Software)

The DAVE [43] stands for Definitely Affordable Virtual Environment and it is an

immersive projection environment, a four-sided CAVE. Affordable means that by mostly

using standard hardware components it‟s possible to greatly reduce costs when compared

to other commercial systems.

Fig. II-37 – DAVE, CGV Austria: Images are projected on the back projection side walls and on the

floor from above, mMirrors are used to reduce the space needed

46

Also, by exchanging just the graphics cards every couple of years and less frequently the

PCs, the latest graphics hardware can be used, spending less money. Optimally a normal

room could be used without constructional changes.

Fig. II-38 – CGV Austria, The DAVE, a 3D Immersive System (exploring the National Library of

Vienna)

In 2005 a new version of the DAVE [43] was built in Graz, Austria. This type of

immersive display is the best way to explore virtual worlds. However it requires a large

room and some effort. An alternative technology is the Head Mounted Display which is

uncomfortable and typically has a much lower resolution.

Fig. II-39 – The SweetHome3D (modeling application) output is transferred with a web service to an

OpenSG CAVE application which lets the user walk through a 3D representation of the house plan

47

Currently, at Fraunhofer, a new version of the HEyeWall was built (a large tiled rear

projection setup). With dimensions of 4m x 2m this new HEyeWall version is one of the

largest multi touch screens in the world. With a resolution of approximately 8 Megapixels

(4000 x 2000) a pixel has a size of 1 mm2. While the authors (Lancelle, M., et al. [43])

already implemented a soft edge blending, the team is currently working on better hiding

the seams that are currently visible in the homogeneous regions. A preview of the results is

shown in the photo bellow.

Fig. II-40 – HEyeWall (High Resolution Multitouch Screen)

2.3.3 Computer Generated Holography

Computer Generated Holography (CGH) is a powerful technology suitable for a wide-

ranging of display types. Although CGH-based display systems are currently too expensive

for many applications, they will become a viable alternative in the near future (Slinger, C.,

et al., [44]).

Invented in 1947 by Dennis Gabor, holography from the Greek “holos”, for whole, is a 3D

display technique that involves using interference and diffraction to record and reconstruct

optical wave fronts. Holography‟s unique ability to generate accurately both the amplitude

and phase of light waves enables applications beyond those limited by the light

manipulation capabilities of lens or mirror based systems.

48

Computer-generated holography is an emerging technology, made possible by increasingly

powerful computers, that avoids the interferometric recording step in conventional

hologram formation. Instead (Fig. II-41), a computer calculates a holographic fringe

pattern that it then uses to set the optical properties of a spatial light modulator, such as a

liquid crystal micro display. The Spatial Light Modulator (SLM) then diffracts the readout

light wave, in a manner similar to a standard hologram, to yield the desired optical

Wavefront [44].

Fig. II-41 – Computer generated holography. A computer calculates a holographic fringe pattern for

display by the Spatial Light Modulator (SLM), which diffracts laser light to yield an interactive, true

3D image

Compared to conventional holographic approaches, CGH does not rely on the availability

of specialized holographic recording materials, it can synthesize optical wave fronts

without having to record a physical manifestation of them, and for example, it can generate

3D images of nonexistent objects and offers unprecedented wavefront control by making it

easy to store, manipulate, transmit and replicate holographic data.

Although CGH-based display systems can be built today, their high cost makes them

impractical for many applications.

However, as compute power and optical hardware costs decrease, CGH displays will

become a viable alternative in the near future.

49

Main advantages of CGH are that it provides flexible control of light, making it suitable for

a wide range of display types, including 2D, stereoscopic, auto stereoscopic, volumetric,

and true 3D imaging.

Fig. II-42 – Tradeshow (An Hologram System)

CGH-based display technology can produce systems with unique characteristics

impossible to achieve with conventional approaches [44].

Fig. II-43 – Although he was in Melbourne, Telstra's chief technology officer, Hugh Bradlow (right),

makes is presence felt at a conference in Adelaide (Photo: Telstra)

Mid-air displays which project floating images in free space have been seen in science

fiction movies for several decades.

Recently, they are attracting a lot of attention as promising technologies in the field of

digital signage and home TV, and many types of holographic displays are proposed and

developed. You can see a virtual object as if it is really hovering in front of you. But that

amazing experience is broken down the moment you reach for it, because you feel no

sensation on your hand.

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The objective is to add tactile feedback to the hovering image in 3D free space. One of the

biggest issues is how to provide tactile sensation. Although tactile sensation needs contact

with objects by nature, the existence of a stimulator in the work space depresses the

appearance of holographic images.

Therefore some kind of remote controllable tactile sensation is needed. That is achieved by

an original tactile display. One paper by Hoshi, T., et al., [45], explains the technologies

employed for a “Touchable Holography”.

Fig. II-44 – Touchable Holography Interaction System. An aerial imaging system, a non-contact tactile

display and a Wiimote-based hand-tracking system are combined. In this figure, the ultrasound is

radiated from above and the user feels as if a rain drop hits his palm

2.3.4 Advanced Rendering

Rendering is the process of generating an image from a model (or models in what

collectively could be called a scene file), by means of computer programs. A scene file

contains objects in a strictly defined language or data structure. It would contain geometry,

viewpoint, texture, lighting, and shading information as a description of the virtual scene.

The data contained in the scene file is then passed to a rendering program to be processed

and outputted to a digital image or raster graphics image file [46].

51

The term "rendering" may be by analogy with an "artist's rendering" of a scene. Though

the technical details of rendering methods vary, the general challenges to overcome in

producing a 2D image from a 3D representation stored in a scene file are outlined as

the graphics pipeline along a rendering device, such as a Graphic Card Processor Unity

(GPU) [46].

A GPU is a purpose-built device able to assist a CPU in performing complex rendering

calculations. If a scene is to look relatively realistic and predictable under virtual lighting,

the rendering software should solve the rendering equation. The rendering equation doesn't

account for all lighting phenomena, but is a general lighting model for computer-generated

imagery. “Rendering” is also used to describe the process of calculating effects in a video

editing file to produce final video output [46].

Fig. II-45 – 3D Photorealistic Rendering (created by Harchi, an architecture company based in

Portugal)

Rendering is a very intensive process that consumes huge amounts of computer resources

(memory, CPU…). Recently nVIDIA released CUDA, a parallel computing architecture

that enables dramatic increases in computing performance by harnessing the power of the

GPU.

With CUDA, software developers, scientists and researchers are finding broad-ranging

uses for CUDA, including real-time image and video processing, computational biology

and chemistry, fluid dynamics simulation, CT image reconstruction, seismic analysis, ray

tracing and much more.

52

Fig. II-46 – IPL/CDRSP future building (Rendered in Autodesk Maya 2011 by the author of this

master thesis)

Computing is evolving from "central processing" on the CPU to "co-processing" on the

CPU and GPU. To enable this new computing paradigm, NVIDIA invented the CUDA

parallel computing architecture that is now shipping in Tesla, Quadro and GeForce GPUs.

For example an nVIDIA Quadro FX-4800 graphic card is composed by 192 CUDA

parallel processor cores, if a rendering application is prepared to use the GPU, then the

performance of the rendering platform will be radically improved.

Fig. II-47 – Medium Quality Rendering of a factory installation (created in Deep Exploration by the

author of this thesis)

Founded in Wales, UK, in 2003, iCreate [47] is an established business company,

delivering high quality illustration and animation to architectural firms, property

developers, creative agencies, house building companies and urban planners.

53

The company iCreate creates architectural visualization and 3D architectural renderings,

and has developed so far awesome work on planning and developing new real estate

properties, bringing new buildings to life in 3D virtual reality, CGIs, 3D flythrough,

photorealistic renderings and interactive 3D models). They are specialists in presenting

new developments at their best light before they're actually built, enabling a secure

planning permission, sell property “off plan”, and also helping involving communities in

regeneration and town planning.

Fig. II-48 – High Quality Real-time Interactive Rendering (www.iCreate3d.com)

2.3.5 Virtual World Interactivity

Virtual reality (VR) systems must exploit additional input and output options in order to

make interaction in Virtual Environments (VEs) much more intuitive and to increase the

user's immersion into the virtual world. When developing VR applications, developers

should be able to put more effort on modeling advanced interaction and system behavior.

Many systems and tools for developing virtual reality applications have been proposed to

achieve this goal. However, no de facto standard is available (Steinicke, F., et al., [48]).

Johannes Behr and Dirk Reiners [49] presented interesting additional material and tutorials

to support and simplify different aspects of the VR/AR application development process.

54

Most of the literature defines some basic elements which are critical but variable for all

VR/AR applications [49]:

 Virtual Content

The content of the medium defines the virtual world. The imaginary space, manifested

through a medium, defining any collection or number of objects in a space and the

relations and rules in the corresponding simulation.

 Immersion

Being mentally and physically immersed are important conditions for VR applications.

Where physical immersion is a defining characteristic of virtual reality and mental

immersion is probably the goal of most media creators. Therefore it is important that

there is a synthetic stimulus of the body‟s senses via the use of technology. This does

not imply all senses or that the entire body is immersed. Most systems focus on vision

and sound. Some include touch and haptic, generally known as force feedback. Other

senses are much harder to stimulate by a computer and are only used in very few

specific research environments.

 Sensory feedback

Sensory feedback is an ingredient essential to virtual reality. The VR system provides

direct sensory feedback to the participants based on their physical position. In most

cases it is the visual sense that receives the most feedback, as it is also the sense that

brings most of the information from the environment into the human system anyway.

 Interactivity

For VR to seem authentic, it must respond to user actions, namely, be interactive. The

system must produce sensory feedbacks according to the user action. Most systems

give visual feedback with an update rate from at least 30 times per second. More is

desirable, and immersion gets lost when the time lag between user actions and

“sensable” reactions exceeds 100 ms. There is not a single interaction and navigation

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method or device that would define VR nor AR. There is no Window, Icon, Menu, 2D-

Pointer defining a single metaphor, but every application designer is free to choose

whatever is most attractive or appropriate for the current set of goals. This gives the

developer a lot of freedom, but on the other hand also asks for a new set of

development tools, standards and methods.

Enrico Gobbetti and Riccardo Scateni wrote a report [50] where a short survey of the field

of virtual reality is provided, highlighting application domains, technological requirements,

and currently available solutions.

Fig. II-49 – Manipulation of VR data, provided by MEMPHIS [1] (TiciView VR system).

IGI/Fraunhofer Research group, 2007, Seoul - South Korea, (the author of this master thesis was a

member in the team responsible for the development of this system)

Also in this area of full interaction, iCreate3D [47] has developed many awesome

interactive 3D applications for architecture, as seen in Fig. II-50.

Fig. II-50 – Advanced Interactive Visualization (using iViewer), starting from left to right: (a)

Skyscrapper; (b) an Apartment inside the Skyscrapper (www.icreate3D.com)

56

At CDRSP, the author of this master thesis also created a Virtual Factory Simulation,

where a user can “navigate” inside a factory as if he was a maintenance technician. This

serious game will allow the interaction with different machines and to simulate fire drills,

on virtual scenarios (but very similar to a real environment).

Fig. II-51 – Virtual Factory simulation/serious game that will allow a company to give training to

users, (this project was created at CDRSP by the author of this master thesis)

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2.4 “A World Full of Sensors”

More and more, an increased interest in incorporating sensing technologies in buildings in

an early stage of the project development is expected. These sensors can help monitor

movement of crowds inside a building, prevent fires and warn about imminent

earthquakes, monitor the health of persons and machines and they can also be integrated on

walls and monitor the degradation of the building.

Sensors can be used in the initial development phases to help the engineers in testing and

choosing the right materials or between different design options. Sensors can help reduce

energy consumption by detecting where persons are and by activating light only when

needed, as well as monitoring the temperature inside buildings. Sensors can even track

persons and objects inside a building. This sensor problematic should be faced as an

important part of any new building project [51]. Next, some interesting and innovative

works related to the field of buildings monitoring are presented.

2.4.1 Sensors Feed Information into Virtual Worlds

Environmental monitoring presents many challenges to wireless sensor networks, such as,

the need to collect and process large volumes of data before displaying the information to

the user in an easy to understand format. A very interesting project called Tricorder it‟s

according to Lifton, J., et al. [52], a prototype for a mobile sensor network browser.

There are in fact, several recent attempts to achieve this objective and more surprisingly,

new projects combining the use of really emergent technologies are predictable, based on

augmented reality, multitouch and social networks. This increase on the presence of more

and more sensors in the surrounding environment, leads to the need of the development of

new software applications that can allow the users to easily find, “see” and interact with

this huge amount of sensors.

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Fig. II-52 – Screenshot of the Tricorder device showing the floorplan of a lab, overlayed with Plug

icons to represent sound, light, current consumption, motion and vibration. Also average date from all

sensors is displayed [52]

It‟s possible to already foresee a near future reality (the so called Pervasive Mixed

Reality), where persons will walk in a real environment full of sensors, e.g., in an oil

platform, a shopping mall or in a city, with the help of augmented reality. Persons will be

equipped with mobile equipment with or without augmented reality Organic Light-

Emitting Diode (OLED) glasses; they will be able to request information about those

sensors; sensors will then respond and by pointing the camera into any direction, the user

will be able to locate and see the hidden sensors that are installed on the surrounding

environment. In this way, it will be possible to identify and locate immediately the various

buildings, equipment, services or products. At the same time, the mobile equipment will

receive valuable data from those sensors/active tags. After this, the information can be

instantly represented in a much more understandable and useful way, through the use of

augmented reality, finally the user can interact with this information in a natural and

meaningful way.

SensAR [53] is, according to the authors, a prototype augmented reality interface designed

for monitoring environmental information. The inputs are sound and temperature data,

which are located inside a networked environment. Users can therefore visualize 3D and

textual representations of environmental information in real-time using a lightweight

handheld computer. Also IBM launched a new 3D Data Center in OpenSim, this allows the

creation of Virtual Data centers that are aimed at IT professionals. This 3D application

should let the professionals monitor data centers, more effectively, over long distances,

adding visual information about the real environment to the virtual world through the use

of a network of sensors (Llewelyn, G., [54]).

59

On Ubiquitous Sensor Portals, another recent development is the so called X-Reality, also

known as Dual Reality or Cross Reality. Here the main intention is to mix the real world

and the virtual world by connecting "location-specific 3D animated constructs" in virtual

worlds to in-building sensor installations. IBM also wants to make use of this right now, in

order to “use high fidelity 3D virtual replicas of real plants or factories to remotely browse

and influence industrial processes in real-time”.

In one project, MIT, recently created a cross reality environment called "ShadowLab"

which is in fact a Second Life map of the Media Lab's third floor animated by data

collected from a network of 35 “smart, sensor-laden power strips” (a.k.a. PLUGs) [55].

Fig. II-53 – A Portal in Second Life shows sensor data over time

Landay, J., et al. [56], define cross-reality as the union between Ubiquitous

Sensor/Actuator Networks and shared online virtual worlds, a place where collective

human perception meets the machines view of pervasive computing, the authors also state

that five of their articles expand on aspects for this theme and they should be read as well.

Also, Media Lab says, that the team has plans to explore ways to bridge networked

electronic sensors and human perception; through "cross reality" implementations that

render and manifest phenomena between the real world and virtual environments, through

densely embedded sensor and actuator networks (Coleman, B., [57]).

Complex Event Processing (CEP) is a fundamental functionality for cross-reality

environments. With CEP, raw sensor data generated in the real world can be transformed

into more significant information that has some real meaning for the virtual world.

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Lifton, J., et al. [58] presented DejaVu, a general purpose event processing system built at

ETH Zurich. SmartRFLib, a new cross-reality application, it uses DejaVu and enables real-

time event detection over RFID data streams feeding a virtual library on Second Life [59].

Fig. II-54 – A Virtual DataPond in the Virtual Atrium (left) and a real DataPond in the real Media

Lab Atrium (right)

2.4.2 Remote Monitoring of Persons Inside

Buildings

On an another project developed by Graz University of Technology, Fraunhofer Institute

for Computer Graphics Research and Darmstadt Technical University, the problematic of

monitoring persons inside buildings is discussed. With a new approach for showing a large

number of detection results in real-time, using various levels of abstraction, this system

allows the viewing of information of eight surveillance cameras in one 3D interface

(Settgast, V., et al., [60]).

Fig. II-55 – Different representations of a person detection in 3D, starting from left to right: (a)

Billboard with a thin colored surrounding line, (b) additional geometry, (c, d) overlay marker which is

not occluded by the scene

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2.4.3 Kinetic, Responsive Performative and Adaptive

Architecture

Smart architecture is becoming a buzzword in architecture and its associated disciplines.

Nevertheless, it is not clear what is included in smart architecture and how it relates to or

differs from such similar related camps as responsive architecture, performative

architecture, kinetic architecture and adaptive architecture. One specific paper written by

Senagala, M., [61] poses the essential and critical questions about smart architecture from

a complex-adaptive systems point of view. This work also illustrates the attributes of smart

architecture with a number of seemingly unrelated, however conceptually connected

design developments.

In an experiment (Elsacker, E. and Y. Bontinckx. [62]), a relationship between a CAD

model and a weather “sensoring” system connected to actuators, allows for an autonomous

system that changes the form of the pavilion in reaction to weather conditions.

Fig. II-56 – A model which is a kinetic pavilion that reacts on weather data

This project orients itself on the development of a new kind of pavilion that‟s capable of

acting upon changing weather conditions, human movement or human moods/mindsets. Its

shape has been made dependable of ecological choices and parameters extracted from the

pavilion‟s surroundings (through environment “sensing”). Just like every other organism,

this new prototype changes itself when parameters take different values (Elsacker, E. and

Y. Bontinckx. [62]).

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2.5 Reverse Engineering and Rapid Prototyping

The creation of “scale-models” aims to better show the final result of a project. In this way

there is the need to take in account the materials chosen for the real project (materials used

on the real building), that will help on selecting the right rapid prototyping technology, in

order to achieve a realistic mockup. The different rapid prototyping techniques allow the

construction of scale models with different and complex structures and with a layer by

layer construction; all geometries can be obtained with more precision (than using manual

techniques). Also depending on the objective of the scale model, several different

properties are preferred, there are scale models for presentation purposes, only to show

how a building will look like (as a whole, or as an individual building), the scale model can

represent the interior or the exterior of a building, there are also scale models used for

simulation and testing purposes (functional prototypes). These “additive” techniques have

a link with the requisites needed for scale models construction (Ahrens, C. H., et al., [63]).

There are five main advantages of using rapid prototyping techniques:

 High precision when reproducing a real building on scale model

 Production of complex geometries

 A low cost versus high production quality ratio

 Multiple materials choice, that allow a good reproduction and a good final result

Reverse Engineering can also be associated to rapid prototyping techniques for the creation

of city scale models for urban rehabilitation, with a very high precision and good quality,

that would be nearly impossible to achieve with other manual techniques (Alves, N. M.

and P. Bártolo, [64]).

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2.5.1 Reverse Engineering

Nowadays, urban rehabilitation of old buildings or historic places, with an associated high

cultural value is very interesting and desirable. The creation of scale models for

representing existing buildings, using reverse engineering, allow realistic and reliable

reproductions (that can be edited in CAD software in order to repair damaged areas). In

this way, an understanding how to combine reverse engineering techniques with rapid

prototyping is fundamental (Alves, N. M. and P. Bártolo, [65]).

The reverse engineering process comprehends several phases as seen in Fig. II-57, this

process is not completely sequential as seen in the figure, several iterations and overlays

can occur. Ideally models obtained by reverse engineering should show the same

geometric properties of the original model, however these models are obtained using

approximations due to some errors and imprecisions (numeric errors) that occur during the

reconstruction process (Alves, N., et al., [66]).

Fig. II-57 – Phases of the Reverse Engineering Process

The data acquisition process represents the first phase of the reverse engineering process.

This phase refers to the gathering of numeric data necessary to the reconstruction of the 3D

geometry of the object, it‟s a critical phase of the whole process, because it is directly

related to the geometric quality of the obtained model.

Data Acquisition

Pre-Processing

Shape Reconstruction

CAD Model

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Several methods for capturing the digital data, has been developed, as seen in Fig. II-58.

Each method relies on a specific interactive process with the object and there are several

nondestructive methods, based on using techniques with and without direct surface contact,

and destructive methods, that rely on removing material, using slicing machines and

abrasive tomography.

Fig. II-58 – Reverse Engineering - Classification techniques for 3D Data Acquisition

Each method has several advantages as well as disadvantages, requiring a careful selection

of the reverse engineering method [64, 66, 67]. The objective of the pre-processing is the

treatment of the obtained data by acquisition and includes the selection mechanisms of

points for the model definition and for eliminating the points associated to errors of

3D Acquisition Techniques

Using Contact

Destructive Object Slicing

Non-Destructive

CMM with Contact

Probe

Articulated Arm with

Contact Probe

Without Contact

Transmissive Computer Assisted

Tomography

Reflective

Non-Optical

Sonar

Microwaves Radar

Optical

Active

Triangulation

Image Radar

Active Stereoscopy

Moire Interferometry

Holography

Interfrometry

Passive

Passive Stereoscopy

Movement

Shadowing

Contour/Silhouette

Focus/Unfocus

Textures

65

measurement. This phase includes the use of softening techniques. The 3D reconstruction

phase aims to obtain digital models with a high precision. Several strategies have been

proposed, especially the segment-and-fit methods and the triangulation algorithm of

Delaunay.

Fig. II-59 – Steinbichler Comet 5 Photogrammetry 3D scan equipment at CDRSP Reverse Engineering

Laboratory

After completing a 3D scan of a part, it can also be useful to compare the acquired 3D data

with a referential, e.g. original CAD model, differences between 2 manufactured parts.

This comparison (analysis and inspection) will evidence the differences between the

referential part and the reversed engineered part, allowing the detection of errors on the

newly produced part.

Fig. II-60 – A 3D scan of a real graphite electrode used in molds industry. (3D scan and

analysis/inspection done by the author of this master thesis, using Steinbichler Comet 5 equipment,

and Steinbichler Comet Plus and Comet Inspect software)

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In a case study (created by Dr. Nuno Alves [66]), a scale model of a building: ”Senhor da

Pedra”, Óbidos in Portugal, with a high cultural value (it‟s one of the main “Baroque” style

constructions in Portugal and it was finished in 1747, please see Fig. II-61), was produced

with the use of reverse engineering and rapid prototyping techniques.

Fig. II-61 – “Santuário do Senhor da Pedra”, Óbidos, Portugal

Several photos were taken to this building from different perspectives, then, two “point

images” were obtained, please see Fig. II-62.

Fig. II-62 – Points obtained for the “Santuário do Senhor da Pedra” building

Next and after overlaying these two “point images”, a cloud of three dimensional points

was extracted, corresponding to the studied building, please see Fig. II-63.

Fig. II-63 – 3D cloud of points for the “Santuário do Senhor da Pedra” building

67

After getting the cloud of points, a STL file is created and the 3D model is obtained.

Fig. II-64 - a) STL and b) 3D model

At last, a scale model (Fig. II-65) was produced using rapid prototyping. The resultant

scale model contains precise details, complex geometries and it is a really realistic scale

model (Alves, N., et al., [66]).

Fig. II-65 – Scale model of “Santuário do Senhor da Pedra” obtained by rapid prototyping

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2.5.2 Rapid Prototyping

The production technologies must be flexible and allow a rapid processing of a wide range

of materials, so that it can replicate (the best possible) real materials at several levels

(texture, color and capacity of reflecting/transmitting light). In a project developed by

CDRSP, called “MaquetaPlus” [68], in which the author of this thesis also participated,

several existent technologies and materials were identified, such as, prototyping

technologies based in addition of material (e.g. SL, SLS, FDM...) and prototyping

technologies based on subtraction of material (e.g. HSM), that allow the processing of

different supplies like, ceramic, metallic and plastic materials.

Nowadays, the diversity of technologies and materials it‟s so big that selecting the right

rapid prototyping method is difficult but crucial, otherwise rapid prototyping will be

considered an expensive and non-attractive method. The selection of the right technology

will allow the production of scale models with the expected properties at the first attempt.

Actual rapid prototyping processes can be grouped by the state or initial form of the

materials used in the production.

In this sense, there are three different main categories: processes based on liquid materials,

solid materials and powder particles.

 Liquid based: the raw material used in the production is on an initial liquid state

before being processed. In this category there are technologies that require the

action of a UV Light (e.g. Stereo-lithography, SL) or a liquid resin jet followed by

the action of an UV light (e.g. Inkjet printing, IJP);

 Solid Based: the raw material is on a solid state, like a filament or blade. Some of

these processes first melt the material before actually using the material (i.e.,

fusion/deposition, FDM), others just cut a thin slice of material (i.e., object slicing

manufacturing, LOM or the paper slicing technology, PLT);

69

 Powder based: the raw material is a powder form before being processed. A laser

can be used in its processing (e.g. Selective Laser Sintering, SLS or Final Shape

Sintering, LENS) or also a gluing component applied by a jet head (e.g. 3D

Printing) [63, 69];

2.5.3 Rapid Prototyping Techniques

Stereo-lithography is a layer by layer construction process where the prototypes are built

through polymerization of a liquid resin by the action of a laser, as seen in Fig. II-66. After

the creation of each layer, the platform will go down, in a way that the solidified layer can

be uniformly covered again by another layer of liquid resin, with the help of a blade, and a

new cycle will begin. This process is repeated until all the necessary layers are over and

the prototype is finished [63].

Laser

Mirror

Construction

platform

Resin Recoat

Elevator

Part

(being built)

Fig. II-66 – Main components of a Stereo-lithography machine

After the construction of the physical model, is still necessary to consider an additional

phase for “post-curing”, in that way a higher global density and better mechanical

properties in the produced model are achieved. This final polymerization is done with the

help of UV radiation, heat or with an additional chemical agent.

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The time necessary for this extra step of “post-curing” is dependent on other conditionings,

such as: the type of resin, construction parameters, the construction (and velocity of

construction) strategy and also the strength of the laser beam. This process presents high

costs, for buying the equipment, the necessary materials, this leads to a high cost of the

final prototype, however with an excellent final precision. Also, this process only allows

the use of a small variety of raw materials (resins) it allows the construction using two

different colors (only translucent colors and using a special resin). The production of these

prototypes needs some supports, and the production velocity is done on an average speed,

also the mechanical resistance is considered to be medium [63]. All the prototypes will

need to be “post-cured” in a special hoven, please see Fig. II-67.

Fig. II-67 – Prototypes produced using Stereo-lithography

The extrusion process, initially developed by Crump Company under the designation of

“Fused Deposition Modeling – FDM” consists on a layer by layer production, using an

extruded thermoplastic material.

This process (Fig. II-68) uses a special extruding head that will melt a filament of

thermoplastic material and it will move on the X and Y axis. Besides this main material

extruding head, there is an additional one that will extrude a support material, this material

cab be wax or a water soluble material. The melted material is deposited over the previous

layer and it will solidify.

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Material

Roll

Platform

Extrusion Heads

Model

Material filament

Fig. II-68 – Simplified FDM process

After each deposition of a layer the platform will go down, just the enough for allowing for

a new layer to be deposited and the process will repeat until the prototype is finished

(Ahrens, C. H., et al., [63]).

The distance between filaments it‟s determined by the diameter of the extrusion jet, this jet

cannot be exchanged during the production process, as so, this jet should be carefully

chosen so that the material viscosity is not affected, avoiding changes in the space between

the deposited filaments. The temperature inside the camera, where the prototype is

produced, should be maintained a little bit below the fusion temperature of the material

that will be deposited, in this way, it will be necessary just a little bit of energy in order to

melt the filament, avoiding deformations in the prototype being produced.

The equipment‟s that are used in this process have a medium initial cost, however the raw

materials used in the production have a high cost, in the end a medium-high cost it is

associated to this production process. It has a medium precision with a medium variety of

materials to choose from, and the speed of the production process is slow with a medium

mechanical resistance.

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The production of prototypes needs support materials and presents a regular finishing in

the end of the production process and it‟s also possible to use colored materials. Some

good scale prototypes can be obtained using this technique (Ahrens, C. H., et al., [63]).

Fig. II-69 – Scale models obtained using FDM

The additive production process by slicing (a.k.a. Laminated Object Manufacturing or

LOM) consists in the deposit and the repetitive and selective slices cuts of material,

containing adhesive glue in one of the sides. The raw material used with this process

comes in a reel (Fig. II-70). After the deposition of a slice/sheet, a rolling press is heated

and rolls through the surface, activating the adhesive glue of the bottom part, gluing it

together with the previous sheet. A CO2 laser beam (25 or 50 W), that is directed using a

group of mirrors (that are controlled by an X/Y axis moving system) it‟s used to cut the

geometry profile of the part, in the actual layer. This laser also cuts in small rectangles all

the material that does not belong to the part/model, in this way it will be easier to remove

the remaining (unwanted) material in the end of the production process. The platform will

go down (according to a Z axis coordinate and a new material section goes forward and the

process will continue until the prototype is finished. The material that does not belong to

the part (around the part) will act as natural support during the production process (Ahrens,

C. H., et al., [63]).

73

8.JPG

Mirror

Laser

Feeding Roll

Paper Roll

Thermal Roll

Part

(being built)

Paper

Construction

Platform

Support

Material

Fig. II-70 – Scheme of the LOM Process

The equipment used in this process has a medium cost, the raw material cost and the

overall prototype cost will be reduced (as well as the final precision of the prototype). The

production speed is slow and the mechanical resistance of the prototype is medium, this

process does not need any extra support material, but the overall surface finishing is

regular or bad, this process does not allow colored prototypes, please see Fig. II-71,

(Ahrens, C. H., et al., [63]).

Fig. II-71 – Scale model for Oporto music house, produced using LOM (Portugal)

The Selective Laser Sintering process (SLS), it‟s a rapid prototyping process, based in a

layer by layer construction method that allows a huge variety of metallic, ceramic and

polymeric materials to be used. As seen in Fig. II-72, a thin layer of material is spread over

a platform, next a CO2 laser beam runs through this layer, so that a new section of the

prototype can be built. In stereo-lithography the laser will supply energy for the

74

polymerization of the resin, in the SLS process, the laser beam supplies energy to promote

the heating and melting of material in powder form, promoting the material sintering.

After finishing one layer, the platform goes down, in order to get room for spreading a new

powder layer over the construction section and a new sintering will happen (Ahrens, C.

H., et al., [63]).

Mirror

Laser

Part

(being built)

Powder

Tank

Roll

Powder

tank

Construction

Platform

Powder

Fig. II-72 – Scheme of the Selective Laser Sintering (SLS)

An important aspect in a part dimensional control is the regulation of the amount of energy

transferred to the powder when the beam passes, in order to avoid an excessive fluidity of

the material or even its decomposition, in that case, the contraction of the material would

be unpredictable. The chamber must be heated up, at a temperature just below the fusion

temperature, so that only one small amount of energy will be enough for the particles

sintering. In this way, the thermal contraction of the production chambers is also

prevented. This is a process, where the use of supports it‟s not necessary, the powder itself

will be enough. The initial cost for the acquisition of this equipment it‟s very high, and the

raw material has a medium cost, the prototype will have a high cost in the end and the

precision is considered to be medium, a high variety of raw materials is available (carbon

fibers, thin polyamide, polystyrene).

The production speed is medium and the mechanical resistance of the produced prototype

is high with a good surface finishing, this technique doesn‟t allow the use of colors, also

the prototype should be hit by air jets glass microspheres in order to eliminate the

remaining sintering powder (Ahrens, C. H., et al., [63]).

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The 3D printing, it‟s a process that uses a construction head, composed by several multiple

jets in order to apply a layer of polymeric material in three dimensions (Fig. II-73). All the

prototypes are produced by the printing head, composed by the multiple jets that allow a

layer by layer construction. If the part is bigger than the working space, the platform will

do a repositioning in the Y axis so that the process can continue.

Part

(being built)

Construction

Platform

Multiple jet

heads

Polymer

Fig. II-73 - 3D Printing Process

Many of the models that are produced using the 3D printing process are fragile and can be

easily damaged or deformed. In these cases, wax infiltrations can make these produced

models stronger, and using ink, colored models can be obtained. The initial cost of

acquiring the equipment is considered low, and the raw materials have a medium cost, the

produced prototypes will have a low cost, but the precision is also low. A medium variety

of different raw materials can be used and the production speed is high. In this process,

support materials are not needed, the surface finishing is regular, the prototypes need some

post processing by using air jets and resin infiltration [63-65, 67, 70].

Fig. II-74 – Complete Scale Model obtained using the 3D printing process

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There are also, hybrid processes, using the combination of two or more methods for

additive production, where the advantages of each process can be explored together.

Hybrid technologies allow new possibilities, such as the use of different materials and

shapes that could not be done before. In production technologies the term “hybrid” is

commonly used to designate processes/products that combine different kinds of

technologies.

The best compilation available about rapid prototyping and other related topics can be

found in the annual report published by Wholers Associates [71].

Fig. II-75 – Combining several technologies/processes

2.5.4 Personal Fabrication and Future Manufacturing

It‟s important to refer here, the revolutionary switch that the world is facing today in the

way manufacturing is seen and in the way it will be seen in a really “near” future. Today‟s

economy presents challenges that provoke the “need” for producing new ideas, concepts,

prototypes and even finished goods in a really reduced amount of time. More and more the

production at home, of high quality scale models or products is a reality. Rapid Prototyping

equipment‟s are now widely available and consumers just want new and innovative

products all the time and this, ultimately leads to the necessity of having reduced times of

setup in the manufacturing and assembling lines. If the product to be produced is highly

customizable or unique, even the production series will be really small.

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This certainty opens the path for a new reality in production, where eventually every

person can conceive and produce its own ideas and products at home with a relatively low

cost and sell them easily on the web. The report commissioned by the US Office of

Science and Technology Policy [72] beautifully outlines the emergence of personal

manufacturing technologies, it describes their potential, economic and social benefits and

recommends programs that the government should consider to realize this potential. The

personal manufacturing machines, sometimes “called fabbers”, are the small sized, low

cost descendants of factory scale, mass manufacturing machines. In the future, the world is

expecting to achieve a state where the creation of machines that replicate themselves will

be a reality, the real implications in Humankind of such transformations are yet to be

studied, analyzed and seen. For now, one can rest and just buy these cheap and powerful

rapid prototype machines to easily produce building scale-models at a very low-cost, as

this will help on the design and study of new and more efficient buildings.

Fig. II-76 - A brush made in a 3D printer, using two different materials, printed simultaneously into a

single and not assembled functional object (Object Inc.)

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2.6 Building Information Modeling (BIM) and Automated

Construction of Buildings

In today‟s world of architecture and building constructions, new tools are being developed

in an effort to gather all the information about the design, project, development,

construction and maintenance of a particular new building. These new tools allow the users

to do a complete track and simultaneously control and visualize in real-time all the

operations in all phases of a project (even through is use by customers, capturing the entire

life-cycle), allowing to go back in time and see what happened at a specific stage of the

project or moving forward in time and plan in advance. These tools are also essential to the

vision of autonomous/automated construction of buildings. Classic CAD software is used

by architects to create the geometrical shapes of a building design. In traditional

workflows, these shapes are then interpreted by construction workers that build the real

structure.

2.6.1 Building Information Modeling (BIM)

Building Information Modeling (BIM) aims to create a virtual model of a building adding

additional and important information to the geometry. This incorporates all building

constituents and the 3D model is used and worked on by the whole design team (architects,

engineers and specialist consultants). This information might also be handed over to the

client and final users of the building, serving as a database for facility management and

maintenance. This BIM model (e.g. using the “sgxml” format) does not only contain

geometrical information, but also contains properties and relations of building objects and

components: geometrical properties (area, volume, and amount), physical properties (U-

value, weight), cost information and planning relations (neighbors, room functions). This is

expected to drastically change the traditional planning workflow and improve

communication of the design team. Planning mistakes could be found early in the process,

incorporated easier and faster, calculations of areas and costs become more precise [73].

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Also, keeping track of time and having the capability of visually, seeing the 3D model of a

building and all the associated information, and being able to navigate back and forward in

time (the so called N-D CAD/BIM tools) is indispensable on such an information platform.

A series of four essential reports from McGraw-Hill Construction [74-77], provide a very

insightful vision on the BIM topic as well as other topics (e.g. real world of construction,

simulation tools, surveys...).

Fig. II-77 – BIM Virtual Information (virtual simultaneous visualization of six different phases of an

ongoing building project, created using GraphiSoft ArchiCAD platform)

2.6.2 Automated Construction of Buildings

A new automated construction system is all set to change the face of home-building, for it

is soon going to step out of the developmental phase and become a functional house-

building robot. A prototype was developed by Behrokh Khoshnevis of the University of

Southern California [78]. A computer-controlled crane or gantry will build houses

efficiently without any manual labor. It will use a quick-setting concrete-like material and

will build up structures in a layer-by-layer fashion. This system will be skillful at erecting

walls of almost any shape and specification and can finish a full-fledged house in a matter

of a day without any breaks.

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Fig. II-78 – Vision for an Automated System for Autonomous Construction of Buildings (Behrokh

Khoshnevis)

In addition to the savings on labor costs, the machine is believed to be eco-friendly in

contrast to the standard home construction method, which churns up to seven tons of waste

and fumes. Such is the quality of this prototype that NASA is eyeing this machine for aid

in building a lunar habitat. The maker suggests that the robot‟s ability to rapidly construct

housing will help authorities in reconstructing shelters in areas struck by natural calamities.

Fig. II-79 – A Real Prototype for an Automated System that will allow the Creation of Buildings

Although automation has advanced in manufacturing, the implementation of automation in

construction has been slow. Conventional methods of manufacturing automation do not

lend themselves to construction of large structures with internal features. This may explain

the slow rate of growth in construction automation. According to the referred authors [79],

Contour Crafting (CC) is a layered fabrication technology that has a great potential in

automated construction of whole structures as well as subcomponents. Using this process,

a single house or a colony of houses, each with possibly a different design, may be

automatically constructed in a single run, embedded in each house, will be all the conduits

for electrical, plumbing and air-conditioning.

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III.

Simulation

Tools in

Architecture

3 Outline

Regarding the use of simulation tools for the creation of an automated optimization

framework, the first feature to look for is the feasibility/easiness of programming

integration. A simple text import of simulation parameters and a mere export of results also

in text format was not the ideal path of development for this research work, not only

because that would mean a decrease in performance, but also because it would be one more

bottleneck for the development of a multithread framework for optimization. Therefore,

the quest for finding simulation packages that could allow a complete integration between

Euclides/JavaScript, the evolutionary optimization framework and the chosen simulation

package, was not easy. Several simulation tools/packages were evaluated in terms of

simulation results as well as easiness of API integration [80]. Also there were some

constrains, related to licenses for software development purposes that had to be taken in

account.

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3.1 EnergyPlus and DesignBuilder

EnergyPlus [81] is a modular and complete building energy simulation program that

engineers, architects and researchers use to model energy and water usage in buildings.

Modeling the performance of a building with EnergyPlus enables building professionals to

optimize the building design to use less energy and water. This software is also well

known, for being tested extensively before each release.

With EnergyPlus is possible to model heating, cooling, lighting, ventilation, other energy

flows and water use. It includes many innovative simulation capabilities, like time-steps

less than one hour, modular systems and plant integrated with heat balance-based zone

simulation, multi-zone air flow, thermal comfort, water usage, natural ventilation and

photovoltaic systems.

Fig. III-1 – EnergyPlus Simulation Zones

EnergyPlus [81] has its roots in both the BLAST (Building Loads Analysis and System

Thermodynamics) and DOE–2 programs. BLAST and DOE–2 were both developed and

released in the late 1970s and early 1980s as energy and load simulation tools. Their target

audience was the design engineer or architect that wishes to size appropriate HVAC

equipment, develop retrofit studies for life cycling cost analyses, optimize energy

performance or other problematic.

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Born out of concerns driven by the energy crisis of the early 1970s and the recognition that

building energy consumption is a major component of the American energy usage

statistics, the two programs attempted to solve the same problem from two slightly

different perspectives.

Both packages had their merits and shortcomings, their supporters, detractors and solid

user bases, internationally [81]. Like its parent programs, EnergyPlus is an energy analysis

and thermal load simulation program. Based on a user‟s description of a building from the

perspective of the building‟s physical make-up, associated mechanical systems, etc….

EnergyPlus will calculate the heating and cooling loads necessary to maintain thermal

control set points, conditions throughout a secondary HVAC system and coil loads, and the

energy consumption of primary plant equipment as well as many other simulation details

that are necessary to verify that the simulation is performing as the real building would.

Many of the simulation characteristics have been inherited from the legacy programs of

BLAST and DOE–2. A not exhaustive list of some of the features of the first release of

EnergyPlus, that give an idea of the rigor and applicability of EnergyPlus to various

simulation situations can be found in "Constrating the Capabilities of Building Energy

Performance Simulation Programs", written by Crwaley, D. B., et al., [81]. There are

software interfaces, like DesignBuilder, the first exhaustive user interface for the

EnergyPlus dynamic thermal simulation software. It allows a fast and easy geometry

introduction and offers a set of tools that allow an easy modeling of buildings.

Fig. III-2 – DesignBuilder and its Buildings Energy Efficiency Rating

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3.2 Autodesk Ecotect Analysis

Autodesk Ecotect Analysis is a software package with a distinctive approach to conceptual

building design. It couples an intuitive 3D design interface with a comprehensive set of

performance analysis functions and interactive information displays. The latest version of

Autodesk Ecotect contains a few new refinements for the export of building models to

EnergyPlus (and also Radiance for ambient light simulation). This means that it‟s possible

to work within an advanced modeling and visualization interface, making use of a vast

array of conceptual design tools, while still using EnergyPlus, considered one of the best

analysis and validation software.

Fig. III-3 - Working in EnergyPlus-mode inside Ecotect, when defining operational schedules

Fundamentally, there are five main reasons to consider Ecotect as part of an analysis

workflow in building design:

1. Modeling and Visualization as a Conceptual Design Tool

Ecotect provides its own fast and intuitive modeling interface for generating even

the most complex building geometry. Most importantly however is that the model

is editable. Tasks such as resizing or inclining walls, manipulating complex curves,

rearranging zones, moving apertures or even adding and deleting surfaces are all

straightforward;

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Fig. III-4 - Internal daylight factors shown over a standard working plane

2. OpenGL

To assist in the design process, the model can be visualized in OpenGL, overlaying

Sun-path diagrams, displaying shadow information, lighting grids or simply

moving the model around in real time, is also possible. With its unique

“sketchiness” parameters, analysis results can be presented directly within the

context of the building model, knowing that the client will understand that they are

looking at preliminary ideas and not the future finished product;

Fig. III-5 - Overlaying a Sun-path on the model view

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3. One Central Repository For All Building Data

Each material in Ecotect can store a wide range of information including basic

thermal and surface properties, detailed layer descriptions, acoustic response and

even cost and environmental impact data (if available). Likewise, complex annual

operational schedules and hourly profiles can be generated and assigned for

controlling occupancy, appliances or internal conditions;

4. Internal Analysis Functions

Ecotect offers a wide range of internal analysis functions which can be used at any

time while modeling. They provide almost instantaneous feedback on parameters

such as sun penetration, potential solar gains, thermal performance, internal light

levels, reverberation times and even fabric costs while the user develops and refine

the design. More importantly, generative functions can be used in the design,

allowing to automatically shape shading devices given specific performance

parameters or even interactively spraying acoustic rays to accurately position

reflectors;

Fig. III-6 - Annual cumulative solar radiation over the external surfaces

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5. Powerful Scripting Engine (LUA) and Easy Integration Capabilities

One of the most important functions for the development of the current work was

the programming integration capabilities of the simulation tool. Ecotect is based on

LUA, one of the best scripting engines available. Also it allows for many different

ways of software development integration (sockets, LUA scripts, MS Windows

Dynamic Data Exchange – DDE…).

Finally, Autodesk Ecotect Analysis also includes innovative building energy and carbon

analysis tools made available through the Green Building Studio web-based service [82].

This web service provides a user-friendly front end to powerful building energy analysis

software. All of the computationally intensive hourly simulations are carried out on remote

servers and the results are provided in a web browser.

3.2.1 Short Comparison between Autodesk Ecotect

and EnergyPlus

In Table III-1 it‟s possible to see a comparison between Autodesk Ecotect Analysis and

EnergyPlus in order to compare the CIBSE and ASHRAE calculation methods [83].

Table III-1 - Characteristics of Two Different Simulation Tools [83]

Software

Autodesk Ecotect Analysis (CIBSE

Admittance method)

EnergyPlus (ASHRAE’s Heat

Balance method)

Time step

Daily and hourly

Hourly

Zoning

Multi-zone

Heating regime

Intermittent or continuous

Heating set point

temp

Optional

Cooling

calculation

Simple dynamic

Detailed dynamic

Internal gains

Daily + hourly values

Hourly

Outside conditions

Local meteorological data can be

implemented

Local meteorological data can be

implemented

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3.3 Ansys: AirFlow

According to Ansys company, the Ansys simulation tools are currently used in all of the

following key “Green Technology” areas [84]: Pollution Reduction, Renewable Energy &

Fuels, Energy Efficiency, Indoor Air Quality and Green Building Design.

Fig. III-7 – Coloured contours of Thermal Confort in a conference room predicted for a particular

ventilation system design (Ansys, Inc. Proprietary)

3D Airflow is a highly functional, innovative software program for analysis of airflow in

simulated 3D geometries. It enables the user to perform examinations of thermal

conditions, CFD simulations and to generate physically founded results even in the early

design phase of buildings.

The priority of the development of 3D Airflow was the simple and user-friendly handling.

It‟s possible to construct complex 3D geometries in a short time and to relocate, reinsert or

resize both single and whole building parts (at any time). Additionally, the central 3D input

integrated in 3D Airflow enables the user to construct a simulation model which can, in

consideration of the integral analysis of buildings, be used also for other simulations

(Lighting, Thermal, Acoustic) [85].

89

Ansys offers not only AirFlow but also other very interesting simulation tools for the

architecture field, like CivilFEM or Fluent. This simulation package is really interesting

due to the fact that it gathers so many different simulation methodologies like CFX and

CFD. However this package was not used in the current study because it was not possible

to get access to the needed programming libraries and other API integration material [86].

Fig. III-8 – Ansys CFD Modelling of Regional Flow Patterns near Cape Shopping Centre (Stephan

Schmitt & Thomas Kingsley; Qfinsoft, SA)

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3.4 Sustainability Tools in Architecture – Comparison

Studies/Audits

Recently, an interesting study by Attia, S., et al., [80], was conducted in order to make

available to the community a more comprehensive examination of a wide range (top ten

most used) of scientifically validated Building Performance Simulation tools (BPS)

available internationally (i.e. Ecotect, HEED, Energy 10, eQUEST, DOE-2, Green

Building Studio, IES VE, Design Builder, Energy Plus and Energy Plus-SketchUp Plugin

called OpenStudio).

The study by Attia, S., et al., [80] also points out the gap between the “wishes” of users

and the real functionalities of the different Building Performance Simulation tools (BPS).

Lastly, this study also summarizes the key findings and underlines the major requirements

for future improvement and development of BPS tools, mainly from an architectural

perspective.

Also, in an audit published by RICS [87], it was examined how RICS members were

engaging with the sustainability agenda and the tools, techniques and information that they

were using to do their professional advice and work.

The research [87] was based on a major survey of 47000 RICS members across three

main global regions (UK and Europe, the Americas and the Rest of the World). For the

first time, this work provided detailed information on why the sustainability topic is so

important.

91

IV.

A Global

Optimization

Framework

4 Problematic of “Form Follows Energy” and the

Pursuit of Solutions

One of the main tasks of this master thesis was the exploration of studies (made by other

researchers related to the architecture field) that could clarify and exemplify the

problematic of how the form of a building determines this “Form Follows Energy”

relationship.

How to create buildings that takes in account the specific weather of a region and the

number of solar hours and the orientation of the building, this seems to be a very difficult

question. Several experts of numerous fields of research try to answer these questions and

develop methods and tools which intention is to facilitate architects and engineer‟s lives on

new projects that ultimately will improve the quality of living for all of us.

In the doctoral thesis of Christina Lemke [88] some difficulties were pointed out, analyzed

and discussed. Can the existent common building forms be used to harness solar radiation

or there is the need for developing new forms in order to increase the solar energy

extraction and at the same time contribute for a better adaptation as well as minimizing

thermal losses.

92

Fig. IV-1 – Different possible buildings forms, all with 1000m3 of volume, this can allow the

comparison of results obtained with different forms (PhD Thesis of Christina Lemke [88])

A wide variation of building forms (sixty four shapes) was also presented and analyzed in

respect to different climatic and radiation-geometric conditions for different regions to

check if they meet today‟s conditions for harness solar radiation and thermal conditions of

houses. Also the influence of a building‟s form and its orientation is discussed in order to

check is fitness for a maximum solar potential. In sum, there is a huge challenge to

demonstrate the relation between a building‟s form and its energy consumption, solar

potential and the co-relation between these factors and the final location and orientation of

a building [88].

In another work by AlAnzi, A., et al. [89], a simplified analysis method to estimate the

impact of the building shape on energy efficiency of office buildings in Kuwait is

presented. This method, based on results obtained from a comprehensive whole building

energy simulation analysis. The simplified method is, according to the authors, suitable for

architects during the preliminary design phase, to assess the impact of shape on the energy

efficiency of office buildings. Studies have shown that the building shape can have a

significant impact on the energy costs of heating and cooling, however, no general

guidelines on the impact of the form on the energy efficiency of buildings were made

available for architects and designers. The correlation equation described in this paper was

presented as “the solution” to be utilized by architects during the preliminary design phase

to assess the impact of shape on the energy efficiency of office buildings in Kuwait.

93

Another effort, achieved numerical results to derive parametric equations as a function of

the thickness of insulation as well as the surface to volume ratio (A/V), taking into account

the effect of increasing height as well as increasing length and width. According to Bansal,

N., et al. [90], the two important parameters that affect the energy consumption in a

building are the insulation and its shape defined by a surface to volume ratio (A/V). The

architects and building practitioners, however, find it difficult to estimate energy and loads

(cooling and heating) for these recommended U-values (coefficient of heat transmission, a

measure of the rate of non-solar heat loss or gain through a material or assembly, U-values

estimate how well a material allows heat to pass through, the lower the value he U-value,

the greater a product's resistance to heat flow and the better its insulating value). It is also

found to be difficult (according to Bansal, N., et al. [90]) to run simulation programs

directly.

There are also studies that try to relate the heating consumption of the buildings with their

shape, always discussing some inherent limitations. In a particular reading, a specific

parameter has been chosen to characterize the shape of the buildings, the shape coefficient

is designed as the ratio between the external skin surfaces and the inner volume of the

building, so, fourteen buildings have been chosen according to their varieties in shapes and

their representativeness in current constructions and this precise article sets out a study on

the link between the building shape and their energy consumption. However and according

to Depecker, P., et al. [91], several parameters are missing in their work, such as

orientation or climate in order to confirm these results.

Fig. IV-2 – Definition of an elementary volume, according to Depecker, P., et al. [91]

94

The purpose of several other articles is to present a method of solving the optimization

problem of the internal partitions of a building, the shape of the building, as well as heat

sources, illustrating the example of multi-criteria optimization of the big blocks of flats and

to present rational methods of multi-criteria optimization of the shape and structure of

energy-saving buildings, as well as optimization of heat sources taking into account the

energy criteria [92] [93] [94].

Often, studies tend to provide mere guidelines that should be applied in order to minimize

energy consumption in buildings, due to the fact that this is a very difficult topic and there

was indeed an increased interest and importance for the reduction of energy consumption

in architecture and urban planning in the latest years. This kind of approach usually plans

for the season with the severest weather, often forgetting that temperatures in cities at

certain latitudes can drop below thermal comfort limits in winter and that temperatures in

cities at other latitudes often raise above thermal comfort limits in summer. Holistic

approaches to energy efficient building forms seem to be needed and sometimes offer a

systematic comparison and an evaluation of the relationships between urban built form and

energy efficiency of a few generic forms. However and according to Okeil, A. [95], some

limitations of these studies have also to be observed, like the performance at several

latitudes, or the performance reported for some optimized generic forms. In a real life

situation the form of the building will have to respond to many other forces that might lead

to a reduction in performance.

Fig. IV-3 – Flow field at a street intersection with a tall building, illustrating exchanges between the

streets and additional mixing processes due to the large building

95

Others provide a simplified analysis method to predict the impact of the shape for an office

building based on its annual cooling and total energy use. This simplified analysis method

is developed based on detailed simulation analyses exploiting several combinations of

building geometry, glazing type, glazing area and climate. The shape of the building has a

significant impact on both construction costs and energy costs of buildings and these

studies have investigated the impact of the building shape on its thermal performance for

selected climates in Europe. Therefore a simplified analysis tool is proposed in some

works, to assess the impact of building shape on total annual energy use for office

buildings. According to Ourghi, R., et al. [96], the analysis indicates that there is a strong

correlation between the shape of a commercial building and its energy consumption. These

methods were applied for several cities around the world and were found to be accurate for

cooling dominated climates.

Finally, five of the most commonly used single span shapes of greenhouses (Even-span,

Uneven-span, Vinery, Modified arch and Quonset types) have been also selected for a

comparison. The length, width and height (at the center) are kept the same for all the

selected shapes. A mathematical model for computing transmitted total solar radiation at

each hour, for each month and at any latitude for the selected geometry greenhouses was

developed for both east-west and north-south orientation. Computed transmitted solar

radiation was then introduced in a transient thermal model developed to compute hourly

inside air temperature for each shape and orientation. The selection of optimum shape and

orientation of a greenhouse can lower the heating and cooling loads of the installed

systems thereby saving a lot of operating cost. Hence, in this study, an attempt has been

made to select the most suitable shape and orientation of a greenhouse for different

climatic zones (latitudes) on the basis of total solar radiation availability and its subsequent

effect on the greenhouse air temperature. From this study important conclusions were

taken regarding greenhouse shapes and their relation to maximum solar radiation

extraction by using several different “manual simulations” (Sethi, V. P., [97]).

96

Fig. IV-4 – View of greenhouse shapes in E-W orientation

For a better understanding of how decisions are taken to improve energy performance of

office buildings in warm climates and why energy tools are not so popular among the

architects, a survey was made by Aldomar Pedrini, S. S., [98], that shows the clear

preferences of architects for using intuition and assumptions, rather than energy tools,

guidelines, rational thinking or tests of hypothesis. This study presents some

recommendations to make energy tools more suitable for early stages of the building

construction process, such as, making the geometric modeling more compatible.

Parametric analysis could be optimized if the user defined the range and the interval of

values that a specific variable can assume, then the alternatives would be automatically

created and simulated. The architects look for simple answers and the outputs should make

the comparison of different solutions easily, fast and “whenever possible through an

automated tool”.

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4.1 Answer to the Problematic: Optimal Forms - A

Global Optimization Framework

The conducted studies in the present work, aimed the creation and presentation of new

methods to integrate sustainable design methods combined with evolutionary computation

and environmental simulation tools.

The main objective of this part (applied work and the development of a global optimization

framework) was to research and develop innovative intelligent and automated design

methods that allow the creation of environmentally optimized architecture, through the use

of Euclides and JavaScript (for procedural modeling), evolutionary algorithms, simulation

tools and finally through the development of a new global optimization framework.

This work started (as seen before) with essential research on the various topics related to

architecture, optimization of buildings, simulation tools, common methods and techniques,

always collecting information relevant to this study, as well as making national and

international contacts with architects and other researchers that have a close relation with

this subject.

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4.2 Identification of Essential Forms Used in the Real

World

After an initial step (of research), a deeper look was taken on studies specifically related to

the problematic of “form follows energy” and all the different basic buildings shapes,

parameters and conclusions presented by many published studies (as seen before), this

standard and common real world building shapes were then carefully analyzed and studied.

Fig. IV-5 – 3D Models that Represent Real World Factories

It was decided to center the work on greenhouses, because they are the most basic and

suitable building forms to test the framework developed in this study and therefore it

constitutes a very straightforward case study to confirm early tests.

Three standard “real world” basic shapes (cylinder, classic with an “angled roof” and cube

shapes) were chosen as good candidates for a procedural 3D modeling, as seen in Fig.

IV-6.

Fig. IV-6 - Selected Basic Forms inspired in real world building shapes, starting from left to right: (a)

Cube, (b) Classic and (c) Cylinder) created using Euclides and Rendered using Deep Exploration

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4.2.1 Procedural Shape Generation

After analyzing the different 3D shapes, in order to extract all the fundamental form

parameters, the essential parametric equations were obtained, because they provide the

necessary associated math that is needed for creating 3D models using computer graphics

techniques (e.g. OpenGL).

The software K3DSurf and the software Maple were used at this stage, in this way it was

possible to create and visually try different parameters combinations, getting the

correspondent simplified expression code in the end.

The usage of these software‟s was essential for understanding the different shapes and to

infer the necessary parameters.

Fig. IV-7 – Tests for creating different 3D shapes (and controlling its parameters) using parametric

equations

100

As seen in Fig. IV-8, essential parts of the intended 3D shape, could actually be generated

using parametric equations (using K3DSurf and Mapple).

Fig. IV-8 – Definition of Parametric Equations in Mapple 14

In the end (please see Fig. IV-9) an optimized code was obtained. Some extra code was

added for creating the missing parts of the shape and then all the code was refactored using

JavaScript and Euclides to create a complete 3D shape.

Fig. IV-9 - Optimized Code Generation produced by Mapple 14

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4.2.2 Code Writing Using Euclides and JavaScript

The code development process started by producing the necessary JavaScript code, through

the usage of Euclides (the Fraunhofer JavaScript Procedural Shape Generator), in order to

accomplish the creation of the three standard basic shapes (please see Fig. IV-6), using a

procedural method, so that later, the desired geometric parameters of those shapes could be

dynamically controlled by the evolutionary algorithm.

Fig. IV-10 – Piece of JavaScript Code to generate the 3D Cylinder Shape (Code adapted from

parametric equations and Mapple 14)

This new JavaScript/Euclides code, originated the creation of a new application that

allowed a manual interaction with each of the shapes parameters (width, height, roof angle,

orientation), as seen previously in Fig. II-12 (Chapter II).

Fig. IV-11 – Resulting JavaScript/Euclides interface that allows the control of each shape parameter

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4.3 Simulation Tools Integration

After achieving the creation of these basic procedural types of 3D shapes, an investigation

on general simulation packages that could be used for a complete integration on the

optimization process to be developed, was conducted.

The research contemplated different simulation packages related to the building

construction business, simulation tools like, Energy Plus, DOE, Ansys, Autodesk Ecotect

or the new preview version of Autodesk Vasari (a very promising and innovative project in

this field that was also tested).

Due to the specific needs and the assumed commitment of having a complete programming

integration (rather than just using external scripts or batch files) between the chosen

simulation package and the developed global optimization platform and also after several

unsuccessful integration tests and attempts, finally it was obvious to use Autodesk Ecotect

and its powerful internal script engine based on LUA (a standard scripting language).

Autodesk Ecotect was preferred, mainly because:

 It‟s a common used simulation tool used by architects all around the world;

 It‟s was already used in past studies related to the topic of this study (better

comparison);

 Also because (as said previously) it was the simulation package more suitable for a

complete integration at this stage.

In the beginning, the procedural shapes produced using Euclides and JavaScript, were

exported to the “Wavefront” format (.obj files) and several individual and manual thermal

analysis studies (as well as other types of analysis) were performed using Autodesk

Ecotect, as a way to understand the functionalities, problems and advantages of this

powerful and widely accepted simulation tool.

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Then the Autodesk Ecotect thermal analysis parameters and conditions were manual

defined and several LUA scripts were created in order to automate this manual simulation

analyses and in order to incorporate all the parameters and conditions in a script form, in

this step it was essential to search for detailed information on how to perform correct

Autodesk Ecotect simulations and information on how to create the necessary scripting

code [99-102].

The .Net programming language, C#, was then selected for the development of a new

Autodesk Ecotect simulation library. By implementing the previously developed Autodesk

Ecotect LUA scripts, internally into this new simulation library, it was created a flexible

and comprehensive library, able to run several kinds of analysis inside Autodesk Ecotect,

in an automated way [103]. This library can now be used, not only as part of the developed

global optimization framework, but also in any other project or third party standalone

(independent) applications that may benefit from this kind of automated integration with

Autodesk Ecotect.

The next step was to create a web service [104, 105] (using NetBeans 6.9.1 and MS Visual

Studio 2010) that could be used to integrate Autodesk Ecotect and the .net simulation

library with the JavaScript Procedural Shape Generator (Euclides) and the developed Java

global optimization framework.

This web service also allows the running of simulations in a remote way through the web.

Also, in the future this web service can be installed in several web servers (together with

Autodesk Ecotect), allowing simultaneous runs and parallel simulations, as a way to speed

up the optimization process (in the future other simulation packages can also be integrated

in this newly developed framework).

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4.3.1 Simulation in Ecotect and the Admittance

Method

The CIBSE admittance method dates back from the 1960‟s when it was developed in order

to enable engineers in practice to estimate peak cooling loads using manual calculations. It

basically constitutes a simple representation of a highly dynamic process; i.e. the building,

as a physical body with thermal mass and heat capacity, under the influence of variable

outside conditions and variable internal gains. The admittance method (also known as the

“means and swings” method) uses a steady state approach for the “mean” values in

combination with a dynamic part that describes all deviations from steady state, i.e. the

“swings”. The admittance method is based on the assumption that all thermal dynamics can

be represented by the response to a sine wave with a period of 24 hours [106].

The admittance method is broadly used and has been shown to be an extremely useful

design tool. It is not as physically precise as some of the more computationally intensive

techniques, such as, the response factor or finite difference methods. However for the

purposes of design decision-making, the admittance method is considered to be the best

choice, as it provides near immediate feedback and the accuracy of results can be

progressively improved as the building model develops (Hensen, J. and M. Radošević,

[107]). Using this method there is no need for solving partial differential equations and is

also possible to use the method for manual calculations. By introducing several parameters

the admittance method expresses the building dynamics in a simple way. The parameters

are defined depending on: the type of the thermal input, thermal properties and thickness of

the construction, surface finishes and furnishings within the space (Hensen, J. and M.

Radošević, [107]).

In other words, for load or temperature calculations it is necessary to determine the mean

values either for temperature or for load and then the swing (mean to peak) for either one

or the other value. Both values are obtained from the heat balance equation. The method

involves heat balance equations for both steady state and for the deviations from the mean.

The later equation involves parameters, such as: decrement and time delay of the thermal

input from the outside towards inside due to the heat accumulation within the construction

(Hensen, J. and M. Radošević, [107]).

105

The overall calculation procedure is presented in the report Environmental Design, CIBSE

Guide A, published by the Chartered Institution of Building Services Engineers (CIBSE)

[108].

The Thermal Admittance, Y [W/m²K], can be defined as the amount of energy leaving the

internal surface of the element into the room per unit degree of temperature swing. This is

under theoretical conditions where the internal environmental temperature undergoes

periodic oscillation and the external environmental temperature is constant [109].

4.3.2 Initial Manual Workflow Tests

The developed global optimization framework is responsible for the 3D shape generation

and for the parameterization, the 3D geometry is passed to the simulation library that in its

turn passes the simulation parameters and also the 3D geometry to the analysis tool

(Autodesk Ecotect).

Fig. IV-12 - 3D shape generation in Euclides (1.000 m3 of volume); followed by Thermal Analysis; and

presentation of the analyzed shape. Other types of analysis can be performed as well.

Several manual simulations using Autodesk Ecotect, were conducted, in order to check if

the models generated by using Euclides and JavaScript were suitable for the simulation and

in order to discover all the necessary Ecotect parameters to complete the simulation

process. For this (and as an initial test), the models were manually exported to the .obj

format and manually imported into Autodesk Ecotect.

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After putting all the parts working together, this simulation results are now automatically

obtained and the values are passed back to the optimization framework, that will decide

(evolutionary algorithm) if the optimization process is over or if a new 3D shape needs to

be automatically generated with new values on the mutable parameters, (i.e., new values

for width, height, orientation of the building or the roof angle) maintaining constant, in this

case study, the total volume of the final building (e.g. 1000 m3), as seen in Fig. IV-12 and

Fig. IV-13 (please see the work conducted by Christina Lemke [88] for getting more

specific information).

Fig. IV-13 – Another 3D shape generation in Euclides (maintaining 1.000 m3); followed by Thermal

Analysis; and presentation of the analyzed shape

107

4.4 Differential Evolution

After the integration between the web service and the global optimization framework, the

attentions were focused in the optimization process, for implementing this, evolutionary

algorithms were chosen, more specifically Differential Evolution - DE (by Storn and Price

[26]).

For the inclusion of the differential evolution algorithm, the Fraunhofer/CGV Austria

“FhAFoundation” library was used. This library contains several different algorithms and

math functions. It was specifically used, the package “cgv.math” (with

“cgv.math.linalg.Matrix”, “cgv.math.linalg.Vector”, “cgv.math.linalg.DenseVector” and

“cgv.math.optimization.MultivariateFunction”). Also cgv.euclides was used, for a direct

import of the three Euclides/JavaScript 3D shapes implementation (Classic_Shape_Opt.jar,

Cylinder_Shape_Opt.jar and Cube_Shape_Opt.jar).

In the Fraunhofer implementation of the Differential Evolution (DE), the algorithm was

changed in order to ensure that it will not leave the specified domain, that is, parameters

will always be valid and also, the parameters will not be just clipped, a uniform

distribution is ensured.

Also, it‟s possible to adjust the following parameters of the DE algorithm (please refer to

the report on “Good Parameters for Differential Evolution”, published by Pedersen [31] for

the selection of good values for these parameters):

 Recombination between two chromosomes (two different buildings, i.e. two

different 3D shapes, with some different parameters, e.g. different orientation or

width…), differentialEvolutionCrossOver, value used: 0.9/0.6;

 Mutation operator randomly flips or changes genes in a chromosome, using a

weighting difference (different values on the several parameters of the building, i.e.

3D shape), differentialEvolutionWeighting, value used: 0.7.

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And the respective stop criterion parameter values:

 Minimum improvement, differentialEvolutionXTolerance, value used: 1.0;

 Total amount of individuals generation, differentialEvolutionLimit, value used:

1000 evaluations.

Essential simulation parameters also used in the optimization process:

 Volume of the shape (1000 m3), volume;

 Name of the remote machine running Autodesk Ecotect, ecotectServerName;

 Port number for web service connection, ecotectServerPort;

 Ecotect occupancy and activity level for the analyzed Zone (Shape):

ecotectOccupancy (number of persons) and ecotectActivity (please see Table

IV-1);

Table IV-1 – Activity Level in Autodesk Ecotect

Activity Level

Work

W (watts consumed)

0

Sedentary (Sitting at a desk)

70 W

1

Walking (Moving around)

80 W

2

Significant Activity

100 W

3

Significant Fatigue

150 W

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 The Ecotect weather file: ecotectWeather ("PRT_Coimbra.085490_IWEC.wea");

 The name of the (Euclides/JavaScript) file containing the 3D geometry of the

building (compiled into JAR format), shapeGenerator (e.g.

Shape_Cylinder_Opt.jar);

 The default Autodesk Ecotect material (“BrickTimberFrame”), at this stage only

the default material is being used and it is automatically assigned to the entire

building (other materials can be already manually assigned to the entire building,

before beginning the optimization process, future work can be developed to

automatically handle other types of materials).

Fig. IV-14 – Example List for Materials that can be used inside Autodesk Ecotect

The 3D Shape is read (e.g. Cylinder_Shape_opt.jar) and the respective quantity of

parameters and their valid domains are also extracted (e.g. volume of the shape, minimum

and maximum values for orientation, width and length or any other parameters passed),

this parameters and values are inside the JAR file and are automatically loaded into the

optimization framework.

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4.5 The Developed Global Optimization Framework

It‟s possible to see in Fig. IV-15 a complete overview of the global optimization

framework developed as part of this study.

Fig. IV-15 - Overview of “Optimal Forms”, the developed and proposed Global Optimization

Framework

The global optimization framework is focused on controlling at least three specific

parameters of the fundamental 3D shapes created in this work, namely, the width, the

height and the orientation of the building in a specific location (this location can be chosen

from a list of available locations and the correspondent temperature files are automatically

loaded into Autodesk Ecotect using the developed simulation library), in the particular case

of this study, it was selected the weather values for the city of Coimbra in Portugal (also,

tests using the specific weather files for other cities, e.g. for the city of Graz in Austria,

were already executed as well).

111

The length of the building is automatically calculated (by relating the width, the height and

the volume that was initially fixed on 1000 m3) for the entire building.

The 3D geometry of the shape to be analyzed in Autodesk Ecotect is passed to the web

service through an internal 3D data structure (instead of exporting it to an .obj file, like it

was done initially when using the manual process).

It will then be started a new thermal analysis in Autodesk Ecotect and in return, the newly

calculated admittance value will be returned by the thermal analysis web service call.

This admittance value is in its turn, passed back to the evolutionary algorithm (that will try

to minimize it) and the optimization process continues until a stopping criterion is met,

returning in the end, the optimum values for width, height and orientation for a specific

city location and an overall building material.

Fig. IV-16 – The Global Optimization Framework running autonomously (simulation in Ecotect

followed by 3D shape generation in order to evolve a population of new buildings with different

parameters using the differential evolution algorithm)

112

4.6 Case Studies and Presentation of Results

The results presented here, aim to demonstrate that an automated tool/platform for

procedural 3D shape generation and for autonomous optimization of building forms can be

built, by developing JavaScript code (using Euclides) and linking it with an Evolutionary

Algorithm, in this study, the Differential Evolution (DE) algorithm was used, finally

combining all of this, with standard and accepted analysis tools in the architecture field.

The intention of this work, at this stage, is to validate that procedural languages and

evolutionary algorithms can indeed greatly contribute to help architects and other entities

related to building construction, in early stages of the design process, to test and analyze

their creations in terms of thermal and energy efficiency. More parameters and form

functions can be and should be integrated into this framework in order to create a suitable

tool for supporting building projects.

The results here discussed, were already exposed to some groups of architects (nationally

and internationally), that immediately demonstrated interest in helping developing more

these methods and are willing to support their daily work on the results produced using this

newly developed optimization framework.

Also, this kind of tools (and frameworks) can achieve in a near future, a more holistic

approach to the several problems and constraints related to the green and sustainable

construction problematic.

Using an automated optimization framework, in contrast to guidelines, table of values,

thumbs of rule, manual and independent simulations, intuition and personal experience or

any other manual and tedious processes is indeed a great improvement in the building

design process, especially new ones, that must take in account environmental and energy

efficiency factors.

113

4.6.1 Case Study 1 – Classic Shape Building

Optimization

In this first case study, a proof of concept is presented, that recreates the normal decision

process of an architect when designing a new building (based on the “classic shape form”

of a home, with an angled roof), to be constructed in Coimbra city, in Portugal. It‟s

assumed that the design project is still in an early design phase. There is already a rough

idea for the final shape of the building and there are also some important project

constraints for the building dimensions (width, height, roof angle...), that are fundamental

to the future construction of the projected building in its specific future physical location.

In this specific case study the assumed future constraints of the building were:

 Width must be between 15 m (meters) and 20 m (meters);

 Height must be between 3 m and 6 m;

 Volume must be 1000 m3, in order to analyze the energy dependence between form

and volume, the shape coefficient is designed as the ratio between the external skin

surfaces and the inner volume of the building, according to Christina Lemke [88],

Bansal, N., et al. [90] and Depecker, P., et al. [91];

 Length is automatically calculated, it‟s a relation between width, height and

volume;

 Roof Angle must be between 38o and 60o;

 Orientation of the building can vary between 0o and 180o and for testing and

demonstration purposes of the optimization framework, tests were also conducted

between 0o and 360o;

 Location: Coimbra city, Portugal (specific region weather data was loaded into the

simulation tool used, i.e. Autodesk Ecotect).

114

Several runs (more specifically seven optimization tests) were conducted for this specific

case study using the “Classic Shape Form” as the assumed final building form (however

other similar tests were already conducted using the Cube Shape as well as the Cylinder

Shape).

Table IV-2 - This table shows several runs using the developed framework in order to optimize the

design of a “Classic Shape Form” building using the Differential Evolution (DE) algorithm. In this

table is also possible to see the different constrains and parameters used in the optimization process

In Table IV-2, is possible to analyze what were the project‟s constraints (domain values)

used in each optimization run (i.e. Results Order column). Also, it‟s possible to see what

were the different settings used for the evolutionary algorithm configuration. As already

seen, in the previous chapters, according to Storn and Price [26], the value of 0.9 for

crossover is always a good initial value to check if the algorithm can make a fast

convergence or not, on the given problem. Also, there were some optimizations runs,

where the project constrains were modified on purpose, although within the real project

constrains (e.g. on optimization run number four, the height was constrained between 3 m

and 4 m), to check if there were any significant changes in the global optimization times

and results. In Table IV-2, we can also visualize consequent runs utilizing the same

constrains/parameters values, this means that consecutive runs were executed in order to

analyze the consistency of the obtained results.

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Table IV-3 - Results obtained in each of the optimization runs (according to Table IV-2)

The “time needed” column, shows the total time needed for presenting a “minimized”

solution for the problem given, i.e., the time presented does not refer to times consumed by

the differential evolution algorithm alone, but instead, it refers to the total time consumed

by the global optimization framework, i.e., (Procedural Generation of each different shape

+ Differential Evolution Algorithm + Autodesk Simulation). Also, it should be noticed that

one of the stop criterion used was the maximum number of individuals to be evaluated (in

this case equal to 1000) but the number of evaluations needed was between 35 and 137

evaluations (2 to 8 generations). So the stop criterion was only given by dx, equal to 1.0,

i.e., the minimum improvement margin on four consecutive optimizations, i.e., after having

four times an improvement less than 1.0, than it was considered to have found a minimized

and optimized solution for the given problem. Analyzing optimization runs 5, 5_1 and 6

it‟s also possible to see that if the architect of this “case study” project had chosen to

constraint the roof angle only to values between 38o and 45o, than the results would be

dramatically different, in these last runs we can see that the total admittance is smaller than

the ones achieved on the initial optimization runs, but of course, that would depend on

each project constrain, the purpose of the framework is always to find the best solution

possible for the supplied constrains.

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4.6.2 Case Study 1 - Presentation of Results

On the next images, it‟s possible to check the behavior assumed by the differential

evolution algorithm on the different global optimization runs.

All the results obtained for the different optimization runs are represented in the previous

table, Table IV-3.

In Fig. IV-17, it‟s possible to observe the results obtained by using the differential

evolution algorithm on this specific problem, only in two generations of 17 individuals

each.

The algorithm avoids stopping on local minima and tries to find the best minima possible,

for the specific constrains of the supplied problem.

Fig. IV-17 – The algorithm chooses different individuals (buildings) for analysis, not stopping on the

local minima that was found along the optimization process and giving room for jumping those same

local minima

135

136

137

138

139

140

017 34

Admittance (W/oK)

Evaluations

Classic Shape Run #1

Run #1

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In Fig. IV-18, there was an individual that was selected for evaluation that was completely

different from the other selected individuals (i.e. different values for width, height,

orientation, roof angle), this could be because the evolutionary algorithm generated a

completely different individual or just because there was some “error” in the simulation

process, nevertheless, the algorithm and the framework, is able to overcome such problems

and the search for better individuals continued.

The evolutionary algorithm generated other individuals that were better adapted (smaller

admittance) and the final result was similar to run 1, but it just took more time to Autodesk

Ecotect to finish the simulation process for the generated individuals on this particular

optimization run.

Fig. IV-18 – In this run it was generated a completely different individual (building shape), but the

admittance value was really high and other better individuals were found

For the purpose of code errors debugging the Fraunhofer Evolutionary Algorithm library is

using a deterministic function for the random generation (instead of a “fully” random

function).

0

200

400

600

800

1000

1200

1400

017 34

Admittance (W/oK)

Evaluations

Classic Shape Run #2

Run #2

118

In Fig. IV-19, the results shown, refers to an optimization run, where a smaller value for

crossover was used, a less diversity of individuals was generated.

However the final result (admittance) was identical to the previous runs.

Fig. IV-19 – A new optimization run, this time using a different value for crossover (0.6)

135

136

137

138

139

140

017 34

Admittance (W/oK)

Evaluations

Classic Shape Run #3

Run #3

119

In Fig. IV-20, we can see that more tight constraints were used (the domain of the different

parameters of our problem is more constrained), the result was worse than on previous

runs.

We should always let the global optimization do is job, the results will be better than trying

to manually guess and do a fine tuning where the best solution can be, as an attempt to

achieve faster results. By doing that the result will be similar to fall on a local minimum of

a much more unconstrained problem (of course this will depend on how well our problem

is constrained or not, or on the real constrains of the intended project).

Fig. IV-20 – Other “tighter” constrains were chosen, and the results were slightly worst then the initial

attempts (runs 1, 2 and 3) where a wider domain of search was used

135

140

145

150

155

160

165

017 34

Admittance (W/oK)

Evaluations

Classic Shape Run #4

Run #4

120

Next, in Fig. IV-21 it‟s possible to observe the individuals generated on each different

optimization run and the correspondent results.

Fig. IV-21 – The optimization runs (1, 2 , 3 and 4) are plotted here simultaneously

Next, the “real” case study problem (original constrains) were supplied to the global

optimization framework, the search domain was much wider.

On the first runs (1, 2, 3 and 4) only 35 individuals were sufficient in order to find a good

result that means that the framework had to call and accomplish 35 different simulations

inside Autodesk Ecotect.

However, in this “new and more unconstrained” problem (the domain of the problem

variables is wider), the framework needed to analyze 137 different individuals (different

shapes, with different parameters) in order to find a solution (a better one) for our original

problem.

017 34

130

135

140

145

150

155

160

165

170

Evaluations

Admittance (W/oK)

Optimal Forms - Classic Shape Optimizations

Run #1 Run #2 Run #3 Run #4

121

Although, 137 different individuals (8 generations) may be considered, as a not so huge

amount of different individuals to be analyzed, these results also show that 137 different

individuals were sufficient to find a solution and also there was no need to spend a huge

amount of time on different Autodesk Ecotect simulations.

The requirement for doing more simulations, would also represent a significantly increase

on the overall time needed by the framework to come out with a solution.

Fig. IV-22 – By allowing the algorithm to generate buildings that could use a different orientation (less

constrained regarding the orientation of the building), more evaluations were needed, but a much

better result was obtained

80

90

100

110

120

130

140

150

160

170

017 34 51 68 85 102 119 136

Admittance (W/oK)

Evaluations

Classic Shape Run #5

Run #5

122

The optimization run number 5, was immediately followed by a consecutive optimization

run (5_1) with exactly the same constrains, in order to evaluate about the consistency of

the results provided by the global optimization framework on successive runs.

Fig. IV-23 – A new consecutive optimization run (using exactly the same values) in order to check if the

behavior was consistent from run to run

80

90

100

110

120

130

140

150

160

170

017 34 51 68 85 102 119 136

Admittance (W/oK)

Evaluations

Classic Shape Run #5_1

Run #5_1

123

On optimization run number 6, an optimization was conducted using the same constrains

except for the orientation of the building, now the orientation could vary from 0o to 360o.

The results show that because the building has no doors or windows yet (and because the

orientation domain is set to be between 0o and 360o), there can be another different optimal

solution.

Of course this result would be different, on a more advanced stage of the design process,

where the building would already have doors and windows represented, at that time only

one good solution would be possible (like when we set the orientation to be between 0o and

180o.

Fig. IV-24 – By allowing the algorithm to search for solutions in an orientation domain between 0o and

360o and because the building does not have doors or windows yet, the algorithm found a good solution

by orienting the building on a different direction (when compared to runs 5 and 5_1)

80

90

100

110

120

130

140

150

160

170

017 34 51 68 85 102 119 136

Admittance (W/oK)

Evaluations

Classic Shape Run #6

Run #6

124

Finally, in Fig. IV-25 the plots of optimization runs 5, 5_1 and 6 are gathered in order to

show once more the regularity of results achieved with the current optimization framework

as well as the convergence of the differential algorithm.

Fig. IV-25 – By plotting all the individuals generated by the global optimization framework for runs (5,

5_1 and 6) it’s possible to check the consistence of results obtained on these more complete

optimization runs

017 34 51 68 85 102 119 136

85

95

105

115

125

135

145

155

165

Evaluations

Admittance (W/oK)

Optimal Forms - Classic Shape Optimizations

Run #5 Run #5_1 Run #6

125

After obtaining an optimization through the use of Optimal Forms – the developed global

optimization framework, the architect can continue completing the project and perform

other types of analyzes in Autodesk Ecotect (shading and shadows, solar access/solar

exposure, lighting analysis, acoustic response…).

It must be referred, that Optimal Forms also exports the optimized shape to a file on the

simulation server, using the format .obj (Wavefront format), the user can use this file to

import the geometry to any CAD software or into another simulation package (there is also

an option in Optimal Forms, that allows the saving of each one of the evaluations to a

separate .obj file).

Fig. IV-26 – Case Study 1 (Final Classic Building Shape) – Daily and Annual Sun Paths + Shadows and

Daylight Levels at 14:00 PM, for the 8th of September (based on specific climate/weather files for

Coimbra, Portugal); Width = 20 m; Height = 6 m; Roof Angle = 60o; Volume = 1000 m3; Orientation:

122,10o

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4.6.3 Case Study 2 – Cube Shape Building

Optimization

In this second case study, a proof of concept is presented, that recreates the normal

decision process of an architect when designing a new building (this time, based on the

“cube shape form” of a home), to be constructed in Coimbra city, in Portugal. It‟s assumed

that the design project is still in an early design phase. Once again, there is already a rough

idea for the final shape of the building and there are also some important project

constraints for the building dimensions (width, height...), that are fundamental to the future

construction of the projected building in its specific future physical location.

In this specific case study the assumed future constraints of the building were:

 Width must be between 8 meters (m) and 10 meters (m);

 Height must be between 5 m and 6 m;

 Volume must be 1000 m3, in order to analyze the energy dependence between form

and volume, the shape coefficient is designed as the ratio between the external skin

surfaces and the inner volume of the building, according to Christina Lemke [88],

Bansal, N., et al. [90] and Depecker, P., et al. [91];

 Length is automatically calculated, it‟s a relation between width, height and

volume;

 Orientation of the building can vary between 0o and 180o;

 Location: Coimbra city, Portugal (specific region weather data was loaded into the

simulation tool used, i.e. Autodesk Ecotect).

127

One optimization run is here presented for this specific second case study, using the “Cube

Shape Form” as the assumed final building form.

Table IV-4 – This table shows an optimization run using the developed framework in order to optimize

the design of a “Cube Shape Form” building using the Differential Evolution (DE) algorithm. In this

table is also possible to see the different constrains and parameters used in the optimization process

In Table IV-4, is possible to analyze once again, what were the project‟s constraints

(domain values) used in each optimization run (i.e. Results Order column). Also, it‟s

possible to see what were the different settings used for the evolutionary algorithm

configuration.

As already seen, in the previous case study and according to Storn and Price [26], the value

of 0.9 for crossover is always a good initial value to check if the algorithm can make a fast

convergence or not on the given problem.

128

Table IV-5 - Results obtained in each of the optimization runs (according to Table IV-4)

For solving this optimization problem, the global optimization framework and Autodesk

Ecotect worked for 10 hours and 35 minutes (800 evaluations, 47 generations) in an old

Laptop Dell Precision M90 (Intel Core 2 Duo T7600, 2.33 Ghz) with 3 GB of memory.

Autodesk Ecotect took much more time to perform each thermal analysis on the Cube

Shape Form.

Fig. IV-27 – We can observe that at evaluation 289 the optimization framework had already achieved a

very good result (compared to the final result), but because the stop criterion used was, 1000

evaluations or dx = 1.0, the optimization continued until dx = 1.0 for four consecutive times

195

200

205

210

215

220

225

230

235

117 289

Admittance (W/oK)

Evaluations

Optimal Forms - Cube Shape Optimization

Run #1

800

129

Fig. IV-28 - Case Study 2 (Final Cube Building Shape) – Daily and Annual Sun Paths + Shadows and

Total Radiation Levels at 14:00 PM, for the 8th of September (based on specific climate/weather files

for Coimbra, Portugal); Width = 10 m; Height = 6 m; Volume = 1000 m3; Orientation: 121,86o

After obtaining this optimization through the use of Optimal Forms, once more the

architect can continue completing the project and perform other types of analyzes in

Autodesk Ecotect, in Fig. IV-28 it‟s possible to observe the shadow produced by the

building at 14:00 PM for the 8th of September, as well as the total radiation that reaches all

walls of the building (this information could also be used for a future installation of solar

panels).

131

V.

Conclusions

and Future

Work

5 Summary

Architects and engineers can use digital design information to better analyze and

understand how their projects will perform before they are actually built. Generating and

evaluating multiple alternatives simultaneously and autonomously makes the comparison

easier and faster. In the end, it allows better and more informed sustainable design

decisions, this is essential and contributes for the resolution of the green building

problematic of society. The Global Optimization Framework presented here cannot yet be

considered the solution for all optimization problems, as the evolutionary algorithm used

cannot “in principle” guarantee convergence or optimum solutions and the computation

times can be long until an acceptable solution is found and presented (depending on the

problem and on the number of parameters of that same problem the convergence can be

slow). However in tests and in the optimizations runs conducted, the results obtained were

really good and promising, the algorithm converged always, achieving interesting and

useful results in a fairly short period of time. The work developed, can be seen as an

important step towards the future development of an automated and more complete tool

that can help architects and engineers save time and money in an early stage of the project,

allowing the analysis of different “not yet known” solutions for a specific problem. When

compared to the usual solutions available to architects and engineers, where several

manual tries and empiric knowledge is employed in the attempt of finding a good or a

better solution, the current proposal presented in this work is really a huge improvement.

132

5.1 Final Conclusions

As far as it‟s known, this is the first time that a framework gathering generative modeling,

simulation tools and evolutionary algorithms is developed and presented.

The results obtained, deserved already a very positive feedback from Fraunhofer, which

considered this work as unique, highly valid and extremely promising.

It‟s really not so important, which “energy” comparison methods or simulations tools that

we are at this stage using, we could use admittance or conductance or any other method

and we could also use Ansys, EnergyPlus or any other simulation tools.

Nevertheless, there are some other conclusions to derive from the study presented, such as,

that we can indeed use evolutionary algorithms to create better tools for helping architects

and engineers when designing new buildings. This would allow the creation of more

sustainable and environmental friendly buildings, in a faster and in an independent way.

The results obtained with this newly developed optimization framework are indeed very

promising and the simple case studies presented here demonstrate the immediate

applicability for such a framework.

Also, it makes perfect sense to create an “intelligent” framework that allows the use of

different methods, simulation tools, that can work in an autonomous way, with the

objective of tackling several different real problems not only related to architecture, but

also other problems as well (medicine, financial, engineering in general…).

Of course, more tests and developments are needed and if a different problem is presented,

than one have to decide what will be the best strategy or algorithm to use as well. In the

library provided by Fraunhofer, there are several optimization algorithms available, they

can be used for attacking different problems.

More parameters or constrains can and must be added to create a more “commercial and

user friendly” software that can incorporate other more complete optimization problems.

133

5.2 Future Work

Possible future directions for research (following on from this study) can be undertaken in

five main areas:

 Incorporation of more simulation variables (different materials, hvac…) and the

creation of different and more complex 3D shapes;

 Simulation of the interaction between several buildings simultaneously (a street or

a complete city generated procedurally);

 Implementation of other algorithms for solving a different problematic;

 Integration with other simulation packages;

 Integration of procedural methods and evolutionary algorithms inside CAD tools;

 Use of the developed global optimization framework and its optimization methods

for the optimization of 3D geometry, but applied to different fields of interest (e.g.

industrial, medical, automotive…).

Also other new algorithm approaches and more specifically some new hybrid algorithm

approaches can be implemented in order to try to find better and faster ways to solve

different problems. Currently, the developed global optimization framework is based in

one of the most common used simulation tools of the market in the architecture field, i.e.

Autodesk Ecotect. This tool also works as a doorway for simulation in other tools related

to energy efficiency, like Autodesk Green Building Studio, the new Vasari project, Energy

Plus, Radiance and many other simulation tools. Still, other simulation packages (e.g.

Ansys or Abaqqus) can also be integrated into this global optimization framework, as a

way to tackle other specific problems (e.g. CFD, CFX or Flow/Motion of Persons in Urban

Environments). This work is not “the solution” of all building construction problems, but

represents a big and a major step forward in using procedural shape generation and

evolutionary algorithms in order to design a better and a much more sustainable world.

135

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Optimal Forms - A Master Thesis on Optimization of Generative Models

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