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Performance-Based Parametric Design: A Framework for Building Envelope Design

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Existing performance-based design exploration methods typically suffer from a lack of real-time feedback and a lack of actionable feedback. This paper proposes a hybrid design exploration method that overcomes these issues by combining parametric modelling, surrogate modelling, and evolutionary algorithms. The proposed method is structured as a mixed-initiative approach, in which parametric modelling is the key to creating a synergistic relationship between the architect and the computational system. Surrogate-based techniques will address the issue of real-time feedback, the evolutionary exploration techniques will address the issue of actionable feedback. As a first stage in developing the PEX method, this paper reports on two experiments conducted to identify an appropriate surrogate modelling technique that is efficient and robust.
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Y. Ikeda, C. M. Herr, D. Holzer, S. Kaijima, M. J. Kim. M, A, Schnabel (eds.), Emerging Experience in
Past, Present and Future of Digital Architecture, Proceedings of the 20th International Conference of the
Association for Computer-Aided Architectural Design Research in Asia CAADRIA 2015, 000000. ©
2015, The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong
Kong
PERFORMANCE-BASED PARAMETRIC DESIGN
A framework for building envelope design
THIAN-SIONG CHOO1 and PATRICK JANSSEN2
1,2 Department of Architecture, National University of Singapore
1 choothiansiong@yahoo.co.uk; 2 patrick@janssen.name
Abstract. Existing performance-based design exploration methods
typically suffer from a lack of real-time feedback and a lack of action-
able feedback. This paper proposes a hybrid design exploration meth-
od that overcomes these issues by combining parametric modelling,
surrogate modelling, and evolutionary algorithms. The proposed
method is structured as a mixed-initiative approach, in which paramet-
ric modelling is the key to creating a synergistic relationship between
the architect and the computational system. Surrogate-based tech-
niques will address the issue of real-time feedback, the evolutionary
exploration techniques will address the issue of actionable feedback.
As a first stage in developing the PEX method, this paper reports on
two experiments conducted to identify an appropriate surrogate mod-
elling technique that is efficient and robust.
Keywords. Performance-based design, parametric modelling, surro-
gate modelling, evolutionary algorithms
1. Introduction
This paper proposes a performance-based parametric design exploration
method to enhance the design workflow of the architect. In general, the de-
sign exploration process of many architects includes two cyclic interlinked
loops of divergent and convergent design exploration (Cross, 2008; Janssen
et al., 2011), as shown in Figure 1 (left). The divergent design exploration
loop embodies the idea of developing alternative design concepts that are
appropriate to the design scenario. The convergent design exploration loop
embodies the idea of repeatedly exploring design variants based on the same
underlying design concept.
Y. Ikeda, C. M. Herr, D. Holzer, S. Kaijima, M. J. Kim. M, A, Schnabel (eds.), Emerging Experience in
Past, Present and Future of Digital Architecture, Proceedings of the 20th International Conference of the
Association for Computer-Aided Architectural Design Research in Asia CAADRIA 2015, 603–612. © 2015,
The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong
2 T. S. CHOO AND P. JANSSEN
For a parametric exploration process, design concepts are translated by
design modelling techniques into digital design models. The diagram in Fig-
ure 1 (left) can be modified to explicitly represent the design modelling
techniques, as shown in Figure 1 (right). Two main types of design model-
ling techniques are identified: direct modelling and parametric modelling. In
the divergent design exploration loop, the architect uses direct modelling
techniques to analyse different design concepts. Once the architect is satis-
fied with the selected concept, they proceed to the convergent design explo-
ration loop. The design concept is then translated into a parametric model.
This model can be used to explore the design space by iteratively generating
design variants. Performance-based design exploration techniques allow the
exploration process to be guided by key performance criteria relevant to the
design problem.
Figure 1: The two cyclic interlinked loops of the divergent-convergent design exploration
process. Left: The non-computational process using traditional media. Right: The computa-
tional process using direct and parametric modelling.
This paper proposes a hybrid design exploration method that combines
parametric modelling, surrogate-modelling and evolutionary algorithms.
Section 2 gives a brief overview of alternative performance-based design
exploration techniques. Section 3 describes the proposed method. Section 4
describes a set of experiments that aim to identify an appropriate surrogate
modelling technique to be used within the proposed method. Finally, the
conclusions section summarises the findings.
2. Performance-based design exploration techniques
For the convergent design exploration loop, four types of exploration tech-
niques that could be used for performance-based design are identified:
knowledge-based exploration, simulation-based exploration, surrogate-based
exploration, and population-based exploration.
Knowledge-based exploration techniques include expert system-based
exploration techniques and the fuzzy logic-based exploration techniques.
These exploration techniques emulate the human expert. They consist of a
knowledge base which has domain-specific knowledge and an inference en-
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PERFORMANCE-BASED PARAMETRIC DESIGN 3
gine that uses the embedded knowledge to determine the solution for a user-
specified problem.
Simulation-based exploration techniques use performance simulation
models to evaluate key performance criteria relevant to the design problem.
Simulation models are constructed from first principles and aim to imitate
real-world process or system over time.
Surrogate-based exploration techniques use approximation models to re-
place accurate but time consuming performance simulations. Surrogate mod-
els are typically constructed using a data-driven, bottom-up approach and
aim to emulate the behaviour of complex simulations while being computa-
tionally cheap to evaluate (Gorissen, 2010).
Population-based exploration techniques use search algorithms to explore
complex design spaces with varying multiple conflicting performance crite-
ria. They include evolutionary algorithms, ant colony algorithms and particle
swarm algorithms (Engelbrecht, 2007).
2.1. ISSUES
The application of these performance-based design exploration techniques
are hindered by two key issues: a) the lack of real-time feedback and b) the
lack of actionable feedback.
Real-time feedback refers to an immediate feedback on design perfor-
mance. The feedback should help the architect to evaluate the effect that de-
sign changes are having on the performance of the design. The speed of the
feedback should be fast enough in order to ensure that it does not hinder the
fluid design process. For example, if the architect is interactively changing
the size of a window, the feedback should inform the architect on the effect
those changes are having on daylight levels.
Actionable feedback refers to feedback that helps the architect decide on
an appropriate course of action. The feedback should help the architect to
decide how the design can be modified in order to improve the performance
of the design (Huang et al., 2008). For example, if the amount of daylight on
the desk is too low, then the feedback should suggest how improved day-
lighting might be achieved.
A summary of the issues addressed by the different performance-based
design exploration techniques is shown in Table 1. Real-time feedback for
knowledge-based techniques and for surrogate-based techniques have a tick
with an asterisk because an additional process is required. For knowledge-
based techniques, the knowledge base needs to be developed. For surrogate-
based techniques, the surrogate model needs to be trained. The time required
605
4 T. S. CHOO AND P. JANSSEN
to develop a knowledge base or train a surrogate model depends on the com-
plexity of the design problem.
Table 1: Summary of performance-based design exploration techniques
Performance-based design
exploration techniques
Real-time
Feedback
Actionable
Feedback
Knowledge-based
9*
9
Simulation-based
x
x
Surrogate-based
9*
x
Population-based
x
9
3. Proposed PEX Method
To overcome the two key issues of a lack of real-time feedback and actiona-
ble feedback, a hybrid design exploration method is proposed. It combines
parametric modelling, surrogate modelling, and evolutionary algorithms.
The proposed method is structured as a mixed-initiative approach, in which
parametric modelling is the key to creating a synergistic relationship be-
tween the architect and the computational system. Surrogate-based tech-
niques will address the issue of real-time feedback and the evolutionary ex-
ploration techniques will address the issue of actionable feedback. The
proposed method is referred to as the performance-based exploration (PEX)
method.
One of the main challenges to achieving real-time feedback is the long
processing time of computationally expensive simulation models. Surrogate
modelling offers a solution. For each computationally expensive simulation,
a surrogate can be created. However, in order to be able to create such surro-
gate models, the variability of possible design scenarios needs to be restrict-
ed. The PEX method proposes to create surrogate models that are specific to
the site context for a particular design project and to the parametric model
developed by the architect. The parametric model inherently limits the range
of possible design variants that can be generated, thereby making the crea-
tion of the appropriate surrogate models more feasible.
3.1. PEX EXPLORATION LOOP
In the PEX method, the surrogate model is created and applied during the
convergent design exploration loop, as shown in Figure 2. The loop consists
of four computational steps: 1) create a performance-based parametric model,
2) replace simulation models with surrogate models, 3) generate, evaluate,
and select design variants, 4) consider modifying the parametric model.
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PERFORMANCE-BASED PARAMETRIC DESIGN 5
x In step 1, a performance-based parametric model for design exploration is
created for the selected design concept. This model will incorporate simula-
tion models to evaluate key performance criteria.
x In step 2, surrogate models are automatically generated for each of the simu-
lation models that are computationally expensive. The parametric model is
then updated to use these surrogate models that are fast to execute.
x In step 3, the architect uses the surrogate-based parametric model to generate,
evaluate, and select different design variants. These variants are generated by
the same parametric model and therefore share the same underlying design
concept.
x After numerous iterative cycles of exploring different design variants, in step
4 the results are analysed and possible modifications to the parametric model
are considered. Such modification will constitute a change to the design con-
cept.
Figure 2: Proposed PEX method
Steps 3 forms an important iterative sub-loop in the exploration process.
This sub-loop uses the parametric model from step 2, where the computa-
tionally expensive simulations have been replaced by fast surrogate models
that can be executed in real-time. The sub-loop can either be performed
manually by the architect or can be semi-automated using evolutionary algo-
rithms. The manual mode addresses the issue of real-time feedback, while
the semi-automated mode addresses the issue of actionable feedback. In the
manual mode, the architect uses the parametric model to interactively ex-
plore design variants, with performance being evaluated using the surrogate
models. In the semi-automated mode, the architect is provided with partial
607
6 T. S. CHOO AND P. JANSSEN
maps of the design space being explored, which can be used to support deci-
sion making. These partial maps are created using evolutionary algorithms.
The proposed PEX method requires a computational support system that
we refer to as the PEX system. Three key components for this system are the
surrogate modelling component in step 2, the evolutionary algorithm com-
ponent in step 3, and the design analysis component in step 4. In this paper,
we focus on the surrogate modelling component. The first step in developing
such a component is identify an appropriate surrogate modelling technique
that is robust and efficient. The following section describes a set of experi-
ments that compare a number of surrogate modelling techniques.
4. Selecting a Surrogate Modelling Technique
Two experiments were developed to compare selected surrogate modelling
techniques for the PEX method. The comparison of the different surrogate
modelling techniques is based on how efficient and robust they are. A surro-
gate modelling technique is considered more efficient than another if it can
be trained faster with the least number of samples. It is considered robust if
the model can be trained with a RMSE (Root Mean Squared Error) of equal
or less than 10% for both experiments. A target accuracy of 10% RMSE is
within the range recommended by Forrester et al. (2008).
Four surrogate modelling techniques were selected based on a literature
review (Jin et al., 2001; Villa-viallaneix et al., 2012; Gorissen, 2010; For-
rester et al., 2008). They are: a) Radial Basis Function (RBF), b) Kriging
(KG), c) Support Vector Machine (SVM) and d) Artificial Neural Network
(ANN). They were selected based on their ability to model non-linear rela-
tionships between the design variables and the system response. A detailed
explanation of these surrogate modelling techniques is beyond the scope of
this paper. Ryberg et al’s (2012) summary is recommended for further read-
ing.
Two experiments were then conducted to compare the four selected sur-
rogate modelling techniques. Experiment 1 consists of 2 design variables and
experiment 2 consists of 6 design variables. The increase in design variables
aims to investigate whether all four types of surrogate modelling techniques
are efficient and robust enough to handle larger numbers of design variables.
The performance output measure used is daylight autonomy which is simu-
lated with DAYSIM (Reinhart, 2010). Daylight autonomy is the percentage
of working hours where there is a minimum level of illuminance with the
room for the entire year or a defined period of time. DAYSIM is a Radiance-
based daylight analysis software that simulates annual daylight performance
for building design (Reinhart, 2010; Ward and Shakespeare, 1998).
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PERFORMANCE-BASED PARAMETRIC DESIGN 7
For these two experiments the DAYSIM simulation software is coupled
with Houdini (Sidefx, 2014) and Dexen (Janssen et al., 2012) to generate a
dataset of daylight autonomy calculations for 10,000 design variants. The
surrogate models are then trained separately with SUMO Toolbox (Gorissen
et al., 2010) with input from the sample dataset created.
Houdini is a procedural modelling software (Sidefx, 2014). A custom
node within Houdini which links to the DAYSIM is created. Dexen allow
simulations to run on multiple cores and computers in parallel (Janssen et al.,
2012). This dramatically speeds up the execution time required to generation
10,000 design variants. SUMO Toolbox (Gorissen et al., 2010) is a flexible
global surrogate modelling software with adaptive sampling capability
(Gorissen et al., 2010).
4.1. EXPERIMENT 1
The parametric model used for this experiment represents a typical office
room with a width and depth of 4m and a height of 3m. The model consists
of two design variables, as shown in Figure 3. The first design variable is the
height of spandrel 1 varies from 0 to 1.5m along the top edge of the façade
represented by variable 1 in Figure 3. Similarly, the height of spandrel 2 var-
ies from 0 to 1.5m along the floor edge of the facade, is represented by vari-
able 2 in Figure 3. The sensor to measure daylight autonomy is set at the
centre of the room, 0.85m from the floor. The wall is assigned a reflectance
of 50%, the ceiling with a reflectance of 80%; the floor is assigned a reflec-
tance of 20%. The glass of the window is assigned with a visible light trans-
mittance of 88.4%. Daylight autonomy is calculated at a point 0.85m above
the floor at the centre of the room, as shown in Figure 3.
Figure 3: Office space as test case for experiment 1.
Each surrogate model is trained by iteratively adding a new sample until
it reaches a target accuracy of 10% or below. Each sample in the dataset
609
8 T. S. CHOO AND P. JANSSEN
consists of the two design variables used for generating the design variant
and the daylight autonomy score.
4.2. EXPERIMENT 2
The parametric model used for this experiment represents a typical office
space with a façade facing north. The dimensions of the space are 4m deep,
16m wide, and 3m high. The model has 6 design variables as shown in Fig-
ure 2. The façade can vary from 1 bay to 4 bays. Each bay has a window that
varies in width and height. The position of the windows also shifts in a verti-
cal and horizontal direction from the centre of the façade. If there are multi-
ple bays, variables 1 to 5 (see Figure 4 (right)) are applied similarly to all of
them.
Figure 4: Office space as test case for experiment 2. Left: The north facing facade varies from
1 bay to 4 bays of windows and has horizontal shading. Right: Detailed parametric model of
a single bay.
The surface properties are similar to those used in experiment 1. There is
one sensor in front of each window, at the centre of each bay of the office
space, at 0.85m from the floor. If there is more than one window or bay, then
the daylight autonomy will be calculated as an average value.
4.3. DISCUSSION
For experiment 1, all surrogate modelling techniques were able to reach the
target accuracy (see Table 2). KG was most efficient, it used 24 samples and
completed the run in 1 sec. This was followed by ANN which took 4 sec
with 24 samples. SVM took 1 min 2 sec with 36 samples. Coming in last is
RBF which took 1 min 5 sec and used 40 samples.
For experiment 2, ANN is the only one that manages to reach the target
accuracy without stagnating (see Table 3). It is the only one able to fit the
design space of 6 design variables with an accuracy of less than 10% as rec-
ommended by Forrester et al. (2008). It completed its training within 3 min
with 94 samples.
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PERFORMANCE-BASED PARAMETRIC DESIGN 9
From the two experiments, we could see that not all surrogate modelling
techniques are efficient and robust. Design problems vary and the relation-
ship between the design variables and the performance measure is not known
prior to the creation of the surrogate model. Hence, an efficient and robust
surrogate modelling technique like ANN may be required for a range of de-
sign problems.
Previous research has found that KG, RBF and SVM can be time con-
suming to train and may become intractable when the number of design var-
iables increases (Jin et al., 2001; Villa-viallaneix et al., 2012). This maybe a
reason why KG, RBF and SVM stagnate for more than 2 days with more
than 1000 samples. More experiments need to be conducted to determine if
ANN is efficient and robust for a wider range of building design problems.
Table 2: Comparison of four surrogate modelling techniques used to fit the daylight autonomy
simulation with target accuracy of 10% RMSE for experiment 1.
Model Types
RMSE (%)
Execution Time (H:M:S)
RBF
9.85
00:01:05
KG
6.05
00:00:01
SVM
9.58
00:01:02
ANN
7.29
00:00:04
Table 3: Comparison of four surrogate modelling techniques used to fit the daylight autonomy
simulation with target accuracy of 10% RMSE for experiment 2.
Model Types
RMSE (%)
No of Samples Used
Execution Time (H:M:S)
RBF
-
Stagnated with >1000
> 2 days
KG
-
Stagnated with >1000
> 2 days
SVM
-
Stagnated with >1000
> 2 days
ANN
9.16
94
00:03:00
5. Conclusion
A PEX method has been proposed for addressing the two key issues in a de-
sign exploration process highlighted in Section 2. It is a hybrid method
which combines parametric modelling, surrogate modelling, and evolution-
ary algorithms. The PEX method is structured as a mixed-initiative approach,
in which parametric modelling is the key to creating a synergistic relation-
ship between the architect and the computational system. The surrogate-
based exploration technique will address the issue of real-time feedback,
while the evolutionary technique will address the issue of actionable feed-
back for a multi-objective design problem. As highlighted in Section 3, the
611
10 T. S. CHOO AND P. JANSSEN
surrogate model can be used during the convergent design exploration cycle
of PEX method. The surrogate models replace the computationally expen-
sive simulation models, thereby allowing the architect to use the parametric
model to interactively explore design variants.
The PEX method requires a computational support system. A key com-
ponent of this PEX systems is a surrogate modelling component. In order to
identify an efficient and robust surrogate modelling technique, two experi-
ments were conduct. These experiments showed ANN to be the most effi-
cient and robust among the other types of surrogate modelling techniques.
In the next stage of the research, the automation of the surrogate model-
ling processes with performance-based simulation processes will be imple-
mented as part of the development for the PEX system.
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