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Structuring of Teaching and Learning Situations in Architectural Education

Conference Paper

Structuring of Teaching and Learning Situations in Architectural Education

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The paper describes and discusses the possible integration of gradient analysis into the learning environment, as a tool to optimize design-objects in regards to their proportion-complexity. To create a responsive system to be used within a design- or learning process the gradient analysis has been integrated in an interactive genetic algorithm (IGA). Within the IGA it serves to determine the fitness value of a given object evaluating the degree of proportion complexity and to create variations with an optimized degree of proportion complexity.
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Structuring of Teaching and Learning Situations in
Architectural Education
Using and Integrating Digital Analysis within Interactive Genetic
Algorithms
Matthias Kulcke1, Wolfgang E. Lorenz2, Gabriel Wurzer3
1Hamburg University of Technology/HafenCity University Hamburg 2,3TU Wien
1matthias.kulcke@tuhh.de 2,3{lorenz|wurzer}@iemar.tuwien.ac.at
BACKGROUND
In the search for a deeper understanding of aesthetic
quality in architecture, complexity of a design pro-
vides a possible area of comparison. In fur ther detail,
varying degrees of complexity may serve as compa-
rable characteristics regarding different design pro-
posals, either observed in the design as a whole or
over different scales (fractal characteristics). Thus,
using different methods to determine complexities
can lead to a deeper insight concerning which role
each of them plays in the overall aesthetic quality of
a given design. One well established method to de-
scribe complexity represented as density and distri-
bution of significant lines of a design across differ-
ent scales is the fractal analysis method box-counting
(Lorenz 2012). The authors propose an additional
method, called gradient analysis, as part of a greater
palette to aid design aspect-analysis.
Gradient analysis
Gradient analysis is an additional method for archi-
tects, designers as well as students and scholars to
analyze and subsequently influence the degree of
proportion-complexity of a design (Kulcke et al 2015,
2016). This method is based on the assumption that
redundancy reduces the complexity of an objects’
appearance. However, the reduction of complexity is
not a quality per se; the matter at hand is for design-
ers to achieve a balance between redundancy, which
provides readability of form, and complexity to sus-
tain interest in it. Moreover, the authors are aware
that, concerning this balance, many different levels
of comparison exist, including, among others, the va-
riety of material and colour combinations. However,
for the moment the authors decided to focus on pro-
portion, expressed by a gradient which is defined by
two points (diagonal of a rectangle).
The basic idea of redundancy-analysis by
gradient-comparisons led to a computer program
called ‘gradient analysis, which pairs every signifi-
cant point of a design with each of the other points,
comparing the gradients of the lines defined by each
pair. By doing so emphasis is put on similar relations
rather than specific relations (e.g. Wagner, 1981).
Moreover, relations are not weighted depending on
distance and/or visual highlighting e.g. in form of
material edges, since Gestalt perception does not
necessarily require edges (Kulcke et al, 2015). The
weighting of different perceptual relations will be
subject of future studies.
The result of the measurement is a list of pairs
of points (coordinates of corners or intersections of
lines) and their gradients, given as angles. In addi-
tion to the angle redundancy quotient a length re-
dundancy quotient is also determined. The angle-
redundancy quotient Rαis defined by the number
of different angles Crdivided by the total number of
angles C(formula 1; Kulcke et al, 2015):
Rα=
Cr
C(1)
Draft - eCAADe 35 |1
Figure 1
Scan and retrieving
rectangle
measurements
with rbeing all repetitions and Cbeing calcu-
lated as follows (formula 2; Kulcke et al 2015):
C=(nn1
2)(2)
with nbeing all single connections. The length re-
dundancy quotient is calculated in an analogue fash-
ion. In both cases, a tolerance coefficient takes into
account that slightly differing angles or lengths may
be perceived as similar and that the drawing of the
object may not be accurate. The tolerance coefficient
includes all angles inside a certain range, i.e. angles
that stay within that range are counted as a repetition
of the angle they are compared to.
Interactive genetic algorithm
In order to create a responsive system to be used
within a design- and/or learning process the gradi-
ent analysis has been integrated in an interactive ge-
netic algorithm (IGA). Within the IGA it serves to de-
termine the fitness value of a given object evaluat-
ing the degree of proportion complexity and to cre-
ate variations with an optimized degree of propor-
tion complexity. In principle, the algorithm consists
of the following steps:
1. Generating the first population of n�parents:
Currently two different designs each of which
is defined by significant points.
2. Encoding: The coordinates of all significant
points are translated into binary code.
3. Crossover: Two encoded parents are cut at a
definite point and their parts are exchanged.
4. Mutation: Digits are swapped at a certain po-
sition.
5. Fitness�values: Angle redundancy quotient
and length redundancy quotient are calcu-
lated to determine the segment�size on Gold-
bergs weighted roulette wheel (step 7).
6. User-choice: The user can favour a result for
Goldbergs weighted roulette wheel.
7. Generating the next population of n�parents:
The next generation depends on the result
of Goldbergs weighted roulette wheel (Coates
2010, König 2010) and another user-choice.
Some of the steps above are determined by users’
choice and/or including randomization. The process
is repeated until a satisfying solution is reached.
IGA WITH GRADIENT ANALYSIS IN THE
WORKSHOP
In the workshop participants extensively test the in-
teractive genetic algorithm and thus also the inte-
grated gradient analysis in a given framework. Since
the software is undergoing a process of permanent
evolution, particular attention is paid to the analysis
and discussion of the results. Another focus is laid on
the discussion of possible integrations into learning
environments.
The presented method of analysis to evaluate a
certain layer of design quality is used exemplary to
test the integration of design analysis via an IGA into
a design learning context. Alternative methods for
evaluating design complexity, like easy access fractal
analysis (Wurzer and Lorenz 2017), may also be inte-
grated into a similar design process. Participants are
held to adapt the process to their analytical focus or
expertise within their own teaching environment.
The process
The design process as considered in the workshop
consists of three phases:
2|eCAADe 35 - Draft
1. Freehand form development by sketchingob -
jects into a given rectangular field plus im-
age enhancement and measurement extrac-
tion (Figure 1).
2. Evaluating the intuitively chosen proportions
by using the gradient analysis IGA (Figure 2)
and semi-automatically producing alternative
versions on the basis of proportion analysis
(Figure 3).
3. Comparing and choosing the final version,
thus optimizing or confirming the initial de-
sign gained by sketching.
Figure 2
IGA user Interface
and first population
of n�parents (n=2;
showing just one of
the two parent),
displaying fitness
values
The workshop content
As preparatory steps the participants are familiarized
with the general theoretical approach of proportion
complexity and proportion optimization. Then they
are taught to use scanning and an image enhanc-
ing process allowing to feed the intuitively reached
object into the web application to apply the IGA for
proportion optimization. The actual task is presented
as a design task to develop a prefabricated concrete
façade module with two openings, especially focus-
ing on a pleasing design considering the placement
and size of the windows or doors. The height and
width of the desired element are given, participants
are asked to choose the openings boundaries as par-
allels to the rectangular boundary of the overall ob-
ject. The participants are advised to draw a mini-
mum of six variations and encouraged to optimize
a favoured first design by iteration i.e. by sketch-
ing it again with alteration of measurement, but pre-
serving the general idea of the previous design. Af-
ter choosing two favourite designs from the sketch-
ing phase, these are scanned (Figure 1) and opened
within the image enhancing software. The sketched
and digitalized rectangles receive an overlay of dig-
ital rectangles as a means to retrieve the measure-
ments of the openings. These are chosen by the par-
ticipants who visually control the process, especially
regarding if the overlay rectangles still represents or
even betters their design aim. The measurements of
the favoured two variants can now be entered into
the user interface of the gradient analysis IGA to serve
as parents (Figure 2), as the starting point for the in-
teractive genetic algorithm. Again in several cycles
the proportions of the openings within the facade el-
ement are optimized, chosen by the numerical gra-
dient quotient and visual appearance (Figure 3). Af-
ter several cycles the participants receive an output
of the favoured solution of each cycle out of which
again two favoured designs can be chosen. These
are then put to comparison to the top design solution
from the first phase of sketching. Finally each partic-
ipant presents his or her work and voices the choices
he or she has made on the way toward the final de-
sign.
Advanced Task
The integration of user choices remains an important
focus as the developer intend the tool to be utilized
as a cognitive and analytic aid during the design pro-
cess and not as an automatic design generator. For
the workshop the software has been developed fur-
ther in order to allow users to manipulate elements
of the IGA themselves while producing design varia-
tions e.g. implementing their own cross over masks
(Coates 2010, König 2010).
To gather and evaluate the feedback (of stu-
dents) a qualitative approach is in development. The
status quo is using narrative interview technique and
computer aided qualitative content analysis. It is part
Draft - eCAADe 35 |3
of the workshop to discuss the optimization of and
alternatives to this feedback-process.
Figure 3
Two examples of
first cycle and user
choice (bottom) for
next cycle,
displaying fitness
values
PREVIOUS TEST CASES
The implementation in the learning envi-
ronment
The approach has undergone first testing with stu-
dents in the winter semester 2016/17. The students’
feedback is taken into account to optimize the pro-
cess and the interactive algorithm (Poirson et al 2010)
for further implementation in design education. A
crucial point is the carefully adjusted use of differ-
ent media (sketching in combination with analyz-
ing/choosing within the GUI of the IGA). On the basis
of testing the IGA within the learning environment
changes have been made to the algorithm to opti-
mize the design process. The comparability of the
task is significantly changed by seemingly small al-
terations, respectively given rules and constraints, re-
garding the task.
To gather and evaluate student feedback a qual-
itative approach is in development. The status quo
is using narrative interview technique and computer
aided qualitative content analysis. It is part of the
workshop to discuss the optimization of and alterna-
tives to this feedback-process.
DISCUSSION
Allowing participants to use edges that are not paral-
lel to the boundaries of the rectangle, using rounded
corners or organic freeform is of course possible. But
this should be done in a way that every participant
is aware of that possibility, to ensure comparability
for later discussions of the results. A possible varia-
tion is also to allow for teamwork and to propose to
participants to marry their own favourite design from
sketching to one chosen from a colleague, thus build-
ing the initial pair for the IGA.
REFERENCES
Burke, A 2008 ’Reframing “Intelligence” in Computa-
tional Design Environments’, First International Con-
ference on Critical Digital: What Matter(s)?, Cam-
bridge (USA), pp. 359-366
Coates, P 2010, The Programming of Architecture, Rout-
ledge, New York
Herr, CM and Karakiewicz, J 2008 ’Towards an Under-
standing of Design Tutoring: A grounded study
of presentation materials used in tutorial conver-
sations’, Proceedings of CAADRIA 2008, Chiang mai,
Thailand, pp. 321-327
Kulcke, M and Lorenz, WE 2015 ’Gradient-Analysis:
Method and Software to Compare Different Degrees
of Complexity in the Design of Architecture and De-
signobjects’, Proceedings of eCAADe 2015, Volume 1,
Vienna, pp. 415-424
Kulcke, M and Lorenz, WE 2016 ’Utilizing Gradient Anal-
ysis within Interactive Genetic Algorithms’, Proceed-
ings of eCAADe 2016, Volume 2, Oulu, pp. 359-364
König, R 2010, Simulation und Visualisierung der Dy-
namik räumlicher Prozesse, VS Verlag für Sozialwis-
senschaften, Wiesbaden
Lorenz, WE 2012 ’Estimating the Fractal Dimension of
Architecture: Using two Measurement Methods
implemented in AutoCAD by VBA’, Proceedings of
eCAADe 2012, Prague, pp. 505-513
Poirson, E, Petiot, J, Aliouat, E, Boivin, L and Blumenthal,
D 2010 ’Study of the convergence of Interactive Ge-
netic Algorithm in iterative user’s tests: application
to car dashboard design’, Proceedings of IDMME - Vir-
tual Concept 2010, Bordeaux
Wagner, FC 1981, Grundlagen der Gestaltung – plastis-
che und räumliche Gestaltungsmittel, Kohlhammer,
Stuttgart
Wurzer, G and Lorenz, WE 2017 ’Cell Phone Application
to Measure Box Counting Dimension’, Proceedings of
CAADRIA 2017, Suzhou, China, pp. 725-734
4|eCAADe 35 - Draft
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Conference Paper
Full-text available
There are two kinds of algorithms: those that are 'better' with respect to accuracy and those that are 'faster'. In the past, fractal analysis by means of box-counting-including both, binary and greyscale analysis-has been focused on the former. In our work, however, we want to aim at the second category: algorithms that are fast and easy to use, without losing view on significance. To this end we have devised a cell phone application which let users grasp and analyse photographs regarding the box-counting dimension of e.g. facades. The application includes two measurement methods for binary images, based on threshold conversions, and one for greyscale images. Accuracy has been tested on deterministic fractals with known fractal dimension. As a matter of fact we are able to produce what was formerly constraint to scientific implementations or discourse on every day's hardware.
Conference Paper
Full-text available
The paper describes and discusses the possible integration of gradient analysis, as a method and tool for architects and designers to analyze the degree of proportion-complexity of a design, into the process of designing an object utilizing interactive genetic algorithms (IGA). A VBA implementation for AutoCAD has been developed by the authors, enabling to test the usability of genetic algorithms (GA) for minimizing the angle-redundancy and length-redundancy quotient. The gradient analysis itself has been developed on the basic assumption that the complexity of an objects appearance is reduced by redundancy, which can be measured focussing on different levels of comparison; among others e. g. variety of material, colour-combinations and proportion. The latter comes under scrutiny if the method of gradient analysis is applied. The complexity of a given architectural object or object arrangement can be measured on the level of gradient relations, the gradients itself being the representations of the proportion of each rectangle that is defined by each pair of geometric points. Using the gradient analysis means regarding the frequency of repetition of gradients, comparing every significant point with all the other points in a 2D CAD representation of the object one by one and successively listing pairs of points and their gradients (Kul-cke and Lorenz 2015). In addition the distance between points is taken into account; the gradient analysis returns an angle quotient and a length quotient, each of which can be used as a fitness-value within a genetic algorithm. This is tested by the authors on simple facade elements with fixed main outlines and two openings with varying corner-points. The coordinates of the corner-points are translated into binary DNA (1st step: encoding). After using crossover (2nd step; with or without fitness check beforehand) and mutation (3rd step) on a population of n-parents (chromosomes), angle quotient and length quotient are used as fitness-values (4th step) to determine the segment-size on Goldbergs weighted roulette wheel (see Coates 2010, König 2010). By digitally spinning the roulette wheel, members for the succeeding breeding population are picked to be proposed (element of chance); out of these the user choses 1 or 2 favourites. Taking this choice into account the next breeding population goes through the algorithm (see flowchart, Figure 1). SHAPE, FORM AND GEOMETRY | Grammars and Concepts-Volume 2-eCAADe 34 | 359
Conference Paper
Full-text available
The aim of the research presented in this paper is to provide an additional method and tool for architects and designers as well as students and scholars to analyze the degree of complexity of a design. Fractal analysis (box counting) e.g. is one of these methods already used in architecture to measure the degree of complexity of an architectural design, for example of the elevation of a building. The method of semi-automated gradient-analysis described here focuses on the repetition of gradients and thus of proportion-repetition in a given design as one of several aspects of complexity reduction by redundancy.
Reframing "Intelligence" in Computational Design Environments' , First International Conference on Critical Digital: What Matter(s)?, Cambridge (USA)
  • A Burke
Burke, A 2008 'Reframing "Intelligence" in Computational Design Environments', First International Conference on Critical Digital: What Matter(s)?, Cambridge (USA), pp. 359-366
Towards an Understanding of Design Tutoring: A grounded study of presentation materials used in tutorial conversations
  • Coates
Coates, P 2010, The Programming of Architecture, Routledge, New York Herr, CM and Karakiewicz, J 2008 'Towards an Understanding of Design Tutoring: A grounded study of presentation materials used in tutorial conversations', Proceedings of CAADRIA 2008, Chiang mai, Thailand, pp. 321-327
Study of the convergence of Interactive Genetic Algorithm in iterative user's tests: application to car dashboard design' , Proceedings of IDMME-Virtual ConceptCell Phone Application to Measure Box Counting Dimension
  • E Poirson
  • J Petiot
  • E Aliouat
  • Boivin
  • Blumenthal
  • Stuttgart Kohlhammer
  • Wurzer
  • Lorenz
Poirson, E, Petiot, J, Aliouat, E, Boivin, L and Blumenthal, D 2010 'Study of the convergence of Interactive Genetic Algorithm in iterative user's tests: application to car dashboard design', Proceedings of IDMME-Virtual Concept 2010, Bordeaux Wagner, FC 1981, Grundlagen der Gestaltung – plastische und räumliche Gestaltungsmittel, Kohlhammer, Stuttgart Wurzer, G and Lorenz, WE 2017 'Cell Phone Application to Measure Box Counting Dimension', Proceedings of CAADRIA 2017, Suzhou, China, pp. 725-734
Study of the convergence of Interactive Genetic Algorithm in iterative user's tests: application to car dashboard design
  • E Poirson
  • J Petiot
  • E Aliouat
  • Boivin
  • D Blumenthal
Poirson, E, Petiot, J, Aliouat, E, Boivin, L and Blumenthal, D 2010 'Study of the convergence of Interactive Genetic Algorithm in iterative user's tests: application to car dashboard design', Proceedings of IDMME -Virtual Concept 2010, Bordeaux