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In order to support exploration in the early stages of the design process, researchers have proposed the use of population-based multi-objective optimisation algorithms. This paper focuses on analysing the resulting population of design variants in order to gain insights into the relationship between architectural features and design performance. The proposed analysis method uses a combination of k-means clustering and Archetypal Analysis in order to partition the population of design variants into clusters and then to extract exemplars for each cluster. The results of the analysis are then visualised as a set of charts and as design models. A demonstration of the method is presented that explores how self-shading geometry, envelope materials, and window area affect the overall performance of a simplified building type. The demonstration shows that although it is possible to derive general knowledge linking architectural features to design performance, the process is still not straightforward. The paper ends with a discussion on how the method can be further improved.
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Analysing Populations of Design Variants Using Clustering
and Archetypal Analysis
Kian Wee Chen1, Patrick Janssen2, Arno Schlueter3
1Future Cities Laboratory, Department of Architecture, ETH Zurich, Switzerland
2Department of Architecture, National University of Singapore 3Institute of Tech-
nology in Architecture, Department of Architecture Zurich
In order to support exploration in the early stages of the design process,
researchers have proposed the use of population-based multi-objective
optimisation algorithms. This paper focuses on analysing the resulting population
of design variants in order to gain insights into the relationship between
architectural features and design performance. The proposed analysis method
uses a combination of k-means clustering and Archetypal Analysis in order to
partition the population of design variants into clusters and then to extract
exemplars for each cluster. The results of the analysis are then visualised as a set
of charts and as design models. A demonstration of the method is presented that
explores how self-shading geometry, envelope materials, and window area affect
the overall performance of a simplified building type. The demonstration shows
that although it is possible to derive general knowledge linking architectural
features to design performance, the process is still not straightforward. The paper
ends with a discussion on how the method can be further improved.
Keywords: K-means clustering, Archetypal analysis, Design optimisation,
Performance-based design, Computational design
The early architectural design stages tend to be ill-
defined and explorative in nature. Architects will typ-
ically explore the project brief and design propos-
als simultaneously, with the problems and solutions
feeding into each other to define the boundaries of
what is possible (Harfield 2007, Lawson 2004).
In order to support this exploratory process,
researchers have proposed the use of population-
based optimisation algorithms to search for a set of
well-performing design variants (Caldas 2008; Flager
et al. 2009; Janssen et al. 2011; Lin and Gerber,
2014; Turrin et al. 2011). Such algorithms optimise
a population of design variants based on a set of
performance objectives. In an optimisation process,
the design variants are generated from a parametric
model based on different input parameters (Wood-
bury, 2010). Objective functions are then used to cal-
culate the performance scores for each design vari-
ant. Once a population of optimised design vari-
Design Tools - Exploration - Volume 1 - eCAADe 33 |251
ants has been created, architects need to be able to
analyse this population. Ideally, the analysis results
should give architects a better understanding of the
relationship between architectural features and de-
sign performance.
Common techniques used for analysing the op-
timised design variants include sorting, filtering, and
Pareto ranking. Essentially, these techniques filter
the population, in order for architects to select a small
number of design variants for further development.
However, even after the filtering process, there will
typically still be a large number of design variants
that remain. The selection of design variants is not a
straightforward process. As a result, more advanced
techniques such as Multiple Criteria Decision Analy-
sis (MCDA) (Mela et al. 2012; Pohekar and Ramachan-
dran 2004) and Knowledge-Based Design Support
System (KBDSS) (Singhaputtangkul et al. 2013) are
used to support the architects in narrowing down the
selection to a manageable number of design variants
for further design development.
There are also techniques that extract design
principles through analysing the design variants
(Chichakly and Eppstein, 2013; Deb et al., 2014; Deb
and Srinivasan, 2006). With these techniques, de-
sign principles are derived from the relationship be-
tween the input parameters and performance scores.
These techniques are proposed for engineering de-
sign where the input parameters have a direct rela-
tionship with the performances. In early architec-
tural design stages, architects are exploring design
not only in terms of performances, but also qualita-
tive aspects such as aesthetics. Some input param-
eters are modelled for the qualitative aspects of the
design and have an indirect relationship to the per-
formances. Thus, these techniques are not appropri-
ate for the early architectural design stages.
This paper proposes a method for analysing pop-
ulations of design variants through the use of Clus-
ter Analysis and Archetypal Analysis. Cluster Analy-
sis (Everitt and Hothorn 2011; Han et al. 2012) par-
titions a set of data into subsets of data or clusters
such that the individual units of data in each cluster
are similar to each other, while differentfrom those in
the other clusters. It is used to gain insights into the
distribution of a set of data, observes characteristics
unique to each cluster, and helps identify clusters of
interest for further analysis. Archetypal Analysis iden-
tifies extreme values on the boundary of a data set
or archetypes to represent a set of data (Cutler and
Breiman 1994). An overview of the data can be ap-
proximated based on studying the archetypes.
The proposed method aims to enable architects
to discover relationship between architectural fea-
tures and design performance. The next section will
describe the proposed method, and the demonstra-
tion section will present an example in which the
method is applied to a case study. Finally, the con-
clusions section briefly discusses future research.
The proposed method consists of two stages: cluster-
ing design variants and extracting exemplars. In the
first stage, the population of design variants is hierar-
chically clustered into groups of design variants with
distinct characteristics. Once the clusters have been
created, exemplars are then extracted for each clus-
ter using both Cluster Analysis and Archetypal Analy-
sis. The design clusters and exemplars are then visu-
alised in order to give architects insights into the re-
lationship between architectural features and design
Clustering Design Variants
The aim is to partition the population of design vari-
ants into clusters with distinct characteristics. A basic
Euclidian distance-based clustering algorithm is suf-
ficient. For this research, k-means analysis (Hartigan
1975) is used. It is one of the most common Euclidean
distance-based algorithm used in data mining. k-
means analysis starts with a random initial cluster-
ing using random selected centroids and then iter-
ates through the data set searching for the best clus-
ters. At each iteration, the quality of the cluster is
measured using the within-cluster-variance measure.
The smaller this variance, the more compact is a clus-
252 |eCAADe 33 - Design Tools - Exploration - Volume 1
ter. The analysis stops when there is no change in the
within-cluster-variance for a number of iterations.
Clusters are created in two stages. In the first
stage, the population of design variants is clustered
according to performance scores. In the second
stage, these clusters are then sub-clustered accord-
ing to a set of selected architectural features derived
from the design variants. These features can be any
type of metrics that can be calculated to describe
general characteristics of design variants. This two-
stage clustering approach allows architects to un-
derstand different combinations of architectural fea-
tures that can result in similar performances.
For the proposed method, the architect needs to
specify the attributes to be used for clustering and
the total number of resultant clusters. For the lat-
ter, a heuristic called the "elbow method" can be
used (Everitt and Hothorn 2011). This method is
based on the observation that an increase in the
number of clusters is associated with a diminishing
improvement in the quality of those clusters. This is
because by splitting clusters that are already high-
quality into finer clusters will have marginal reduc-
tion in the within-cluster sum of square measure. The
"elbow method" can be used to find the turning point
when additional clusters no longer result in any sig-
nificant improvements in cluster quality.
Extracting Exemplars
Once the design variants are partitioned into clus-
ters, a manageable number of representative design
variants are extracted. This facilitates architects in
qualitatively assessing the relationship between ar-
chitectural features and performance scores. This is
done by analysing the input parameters of the de-
sign variants of each cluster to find a set of parame-
ters that best represent the cluster. Archetypal Anal-
ysis extracts archetypes of the clusters, which are ex-
treme values located at the boundary of the cluster.
k-means analysis extracts the centroids that are lo-
cated in the centre of each cluster. Together, the
archetypes and centroids give a good sampling of the
design variants in the cluster. They form the exem-
plars of the design cluster.
Note that the exemplars are not created by se-
lecting design variants in the cluster. Instead, they
are new design variants that are reconstructed by
analysing the input parameters for all design variants
in the cluster. Thus, they need to be validated by en-
suring that their performances and architectural fea-
tures are within the range of the design cluster. For
example, for a design cluster that has a daylight per-
formance of 500-1000 lux and a shape factor of 0.2-
0.5, the exemplars need to fall within these ranges to
be valid. The proposed analysis method is illustrated
in Figure 1.
The method is demonstrated on an abstract building
type. The demonstration explores how self-shading
geometry, envelope materials and window area will
affect the overall performance of a simplified build-
ing located in the Singapore climate. The design
schema is illustrated in Figure 2. The schema is based
on two 4x2 grids stacked on top of each other (Figure
2a). There are four options for the location of the ver-
tical core of the building: columns 1 and 5, columns
2 and 6, columns 3 and 7, or columns 4 and 8 (Figure
2b). One grid is chosen from each remaining column
to create the building form (Figure 2c). By staggering
volumes on top of each other it is possible to create
self-shading geometries. There are a total of 256 pos-
sible building forms that can arise. All the external
walls have windows, the heights of which range from
1.2 m to 3.6 m (Figure 2d). The walls and windows are
assigned a material (Figure 2e). The building can be
rotated 360º (Figure 2f). The design schema can gen-
erate 752,640 possible design variants. The example
Figure 1
Procedure of the
proposed analysis
Design Tools - Exploration - Volume 1 - eCAADe 33 |253
Figure 2
Design schema of
proposed building
explores various forms, materials and window areas
that can lead to a better performance.
The design schema is evaluated in terms of ther-
mal transfer through the envelope, the envelope
cost, and the daylight level. The thermal transfer
measures the solar heat gain through the envelope
in the tropical climate, and is a good performance in-
dicator of the cooling performance of a building. It
is calculated using a simplified method, as the sum
of Envelope Thermal TransferValue (ET TV) (Chua and
Chou, 2010) and Roof Thermal Transfer Value (RTTV)
(BCA, 2013). The envelope cost is calculated by mul-
tiplying the envelope area with the cost per square
metre of the material. The better-insulated materials
are more costly. The daylight level is calculated as the
ratio of the floor area receiving at least 300 lux to the
gross floor area. The overall thermal transfer and the
envelope cost are to be minimised, while the daylight
level is to be maximised.
A Non-Dominated Sorting Genetic Algorithm 2
(NSGA2) (Deb et al. 2000) is used for the optimisa-
tion process. The settings of the algorithm are an ini-
tial population of 100, crossover rate of 0.9, and mu-
tation rate of 0.01. A total of 5000 design variants are
generated from 50 generations.
The population of 5000 design variants are analysed
using the proposed method. In the clustering stage,
two levels of clustering are performed. First, k-means
clustering is used to cluster the population accord-
ing to their performance scores. Three performance-
based design clusters are produced, labelled Perfor-
mance Clusters A, B and C. Cluster A achieves a bal-
ance between all three performance objectives, Clus-
ter B focuses on low overall thermal transfer, and
Cluster C focuses on low cost.
Each performance cluster is then sub-clustered
based on two architectural features: the shape factor
(CEN 2007) and Window Wall Ratio (WWR). The shape
254 |eCAADe 33 - Design Tools - Exploration - Volume 1
Figure 3
Feature clusters A1,
A2 and A3 with its
Design Tools - Exploration - Volume 1 - eCAADe 33 |255
factor and WWR describes the building form and
envelope of the design variants, by using these at-
tributes for the second stage of clustering, one will be
able to identify the relationship between the build-
ing form, envelope design and performance scores in
the feature-based clusters. This results in a total of 9
feature clusters.
In the exemplar extraction stage, the exemplars
are extracted by running an Archetypal Analysis and
k-means analysis on the input parameters. The de-
sign clusters are then visualised in two forms. The
performances and architectural features will be vi-
sualised as Parallel Coordinate Plots (PCP) and the
exemplars as 3D models, as shown in in Figures 3
to 5. The exemplars are arranged in three rows,
the centroid is located in the middle row while the
archetypes are at the first and last row. The descrip-
tion on the top of each exemplar indicates its wall and
window material. Figure 2e shows the legend of the
wall and window materials.
Performance Cluster A
Performance cluster A achieves a balance between all
three performance objectives. Cluster A2 and A3 (Fig-
ure 3) are the most balanced design clusters. They
achieve an acceptable overall thermal transfer and
daylight level while maintaining a low cost, relative to
the other design clusters. The design variants in clus-
ter A3 have a lower shape factor, which is illustrated
by its exemplars with their compact forms. These
compact forms have lesser over-hangs and shadings.
Cluster A3 has a smaller daylight performance range
than cluster A2 because of the lesser shadings: the
WWR needs to be low to maintain the overall ther-
mal transfer, and a lesser window area leads to lower
daylight performance. Lastly, the long façades of
the exemplars mainly face north-south to avoid the
east-west sun. Cluster A2 has a high shape factor,
and its exemplars have building forms that self-shade
themselves with over-hangs. Due to the shading, the
exemplars are able to afford higher WWR and thus
achieve a higher maximum daylight level of 40.43%,
compared with the 35.48% of cluster A3.
The design variants in cluster A1 (Figure 3) have a
shape factor higher than that of cluster A3 but lower
than that of cluster A2, and higher WWR than both
design clusters. Cluster A1 is able to achieve similar
overall thermal transfer and daylight performance to
cluster A2, with a higher envelope cost. This higher
cost is due to the high WWR, as better glazing mate-
rial is required to maintain the overall thermal trans-
fer performance with the increased window surface
Performance Cluster B
Performance cluster B consists of design variants with
low overall thermal transfer but high envelope ma-
terial cost. Cluster B3 (Figure 4) has the best over-
all thermal transfer performance and the worst day-
light performance. The envelope cost is higher than
cluster A2. Most of the exemplars have their long
façade facing north-south, and a low WWR, as with
cluster A3. Most of the exemplars use highly insu-
lated materials, which is reflected in the higher en-
velope cost. The combination of good orientation,
low WWR, and good envelope materials contributed
to the best overall thermal transfer performance. The
trade-offs are a higher envelope cost and the worst
daylight performance among the nine clusters.
Overall thermal transfer and cost performance
similar to cluster B3 can be achieved with an architec-
tural design of lower shape factor and bigger range of
WWR, as shown in cluster B2 (Figure 4). Design vari-
ants in cluster B1 (Figure 4) have a similar shape fac-
tor to that of cluster B3, but a higher WWR range, and
the high WWR requires good insulated window ma-
terials to maintain the overall thermal transfer; as a
result, the design cluster has the worst cost perfor-
mance. The advantage of increasing the WWR is bet-
ter daylight performance, but the daylight improve-
ment is only 0.59-8.71%, compared with cluster B3.
Performance Cluster C
Performance cluster C consists of design variants
with low envelope cost, high daylight level, but high
overall thermal transfer performance. Cluster C2 (Fig-
ure 5) has the best envelope cost performance of the
256 |eCAADe 33 - Design Tools - Exploration - Volume 1
Figure 4
Feature clusters B1,
B2 and B3 with its
Design Tools - Exploration - Volume 1 - eCAADe 33 |257
Figure 5
Feature clusters C1,
C2 and C3 with its
258 |eCAADe 33 - Design Tools - Exploration - Volume 1
nine design clusters. Its shape fac tor and WWR range
are similar to those of cluster B2. The main difference
between the two design clusters is the envelope ma-
terial cost, as the design variants use envelope con-
structions of lower thermal qualities, as shown by the
cluster's exemplars. It achieves similar daylight per-
formance to that of cluster B2, but due to the low-
quality envelope materials the overall thermal trans-
fer performance is much worse than that of cluster
Overall thermal transfer and daylight perfor-
mances similar to those of cluster C2 can be achieved
with an architectural design of higher shape factor, as
shown in cluster C1 (Figure 5). The increase in shape
factor increases the surface area of the envelope, and
as a result the cost is 13.3k higher than that of clus-
ter C2. Cluster C3 (Figure 5) has the best-performing
daylight, while having the worst-performing over-
all thermal transfer performance. The exemplars are
characterised as having high shape factor and high
WWR with low-quality glazing materials.
The demonstration shows how k-means clustering
and Archetypal Analysis can be used to partition de-
sign variants into clusters and to extract exemplars.
The PCP of the design clusters and 3D geometry of
the exemplars facilitate the analysis of a large num-
ber of design variants generated from the optimisa-
tion process. The clusters are able to provide a visual
summary of the 5000 design variants.
The demonstration shows that although it is pos-
sible to derive general knowledge linking architec-
tural features to design performance, the process is
still not straightforward. It is not easy with which
such knowledge can be derived depends on the spe-
cific clusters being compared. For example, com-
paring feature cluster A2 and A3 reveals how differ-
ent architectural designs can achieve similar perfor-
mances. One can either have a low shape factor with
low WWR, or a high shape factor with a bigger range
of WWR. Other comparisons are much less reveal-
ing. For example, when comparing cluster A1 and
A2, it is difficult to identify any clear relationship be-
tween architectural features and performance scores.
In this case, the two sets of exemplars do not seem to
have any distinct architectural features, which in turn
makes it difficult to conclude anything with regards
to performance.
Future research aims to improve on the current
method by supporting a more interactive approach.
Rather than automating the whole analysis proce-
dure as discussed in this paper, this approach will
allow architects to analyse populations of design
variants by interactively applying various techniques
such as clustering, archetypal analysis, and filtering.
This interactive approach will allow architects to en-
gage in an iterative process in which the analysis
techniques are repeatedly tweaked in order to home
in on specific relationships between architectural fea-
tures and design performance.
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260 |eCAADe 33 - Design Tools - Exploration - Volume 1
... Those algorithms might not generate an optimal solution but they help to understand the possible outcomes of the rules defined through the algorithm … Similarly, Chen et al. (2015) suggest that optimization methods should not only find high-performing solutions, but "give architects a better understanding of the relationship between architectural features and design performance." The framing of optimization as a generator of insights also is supported by (Bradner et al. 2014): ...
... … the aim is less on optimization per se and more on exploration: the results from optimization are about changing one's way of thinking more than choosing a single design and then realizing it. Schaffranek (2012), Chen et al. (2015), Bradner et al. (2014), and Stouffs and Rafiq (2015) emphasize that, for ADO, understanding optimization problems, i.e., fitness landscapes, is more important than finding "optimal" solutions. Johnson ...
... ure-found by a GA-according to 18 characteristics. Their analysis yields 80 clusters, i.e. 80 archetypal design candidates(Figure 4.1). However, due to this considerable number, a human designer might struggle to select a design for further development or to understand the characteristics of these 80 candidates in relationship to their performance.Chen et al. (2015) define a twelve-dimensional parametric model of an abstract building geometry, resulting in a space of 752,640 design candidates. They search this space with a multi-objective GA that aims to minimize the building envelope's thermal transfer and cost and to maximize the available daylight, yielding 5,000 evaluated candidates. They then ...
Full-text available
Increasing applications of parametric design and performance simulations by architectural designers present opportunities to design more resource and energy efficient buildings via optimization. But Architectural Design Optimization (ADO) is less widespread than one might expect, due to, among other challenges, (1) lacking knowledge on simulation-based optimization, (2) a bias towards inefficient optimization methods—such as genetic algorithms (GAs)—in the building optimization literature, (3) lacking state-of-the-art, easy-to-use optimization tools, and, perhaps most importantly, (4) the problematic integration of optimization with architectural design. This problematic integration stems from a contrast between “wicked” or “co-evolving” architectural design problems, which exhibit vague and changing problem definitions, and optimization problems, which require problem definitions to be explicit and unchanging. This thesis presents an interdisciplinary study of ADO that draws on design theory, building optimization, mathematical optimization, and multivariate visualization. To address the first three challenges, the thesis (1) surveys existing optimization methods and benchmark results from the mathematical and building optimization literatures, (2) benchmarks a representative set of optimization methods on seven problems that involve structural, energy, and daylighting simulations, and (3) provides Opossum, a state-of-the-art, easy-to-use optimization tool. Opossum employs RBFOpt, a model-based optimization method that simultaneously “machine-learns” the shapes of fitness landscapes while searching for well-performing design candidates. RBFOpt emerges as the most efficient optimization method from the benchmark, and the GA as the least efficient. To mitigate the contrast between architectural and optimization problems, the thesis (4) proposes performance-informed design space exploration (DSE), a novel concept that emphasizes selection, refinement, and understanding over finding highest-performing design candidates, (5) presents Performance Maps, a novel visualization method for fitness landscapes, (6) implements Performance Maps in the Performance Explorer, an interactive, visual tool for performance-informed DSE, and (7) evaluates the Performance Explorer through a user test with thirty participants. The Performance Explorer emerges as more supportive and enjoyable to use than manual search and/or optimization from this test. In short, the thesis offers tools for ADO and performance-informed DSE that are more efficient and that better acknowledge the “wickedness” of architectural design problems.
... Chen et al. define a twelve-dimensional parametric model of an abstract building geometry, resulting in a space of 752,640 design candidates [4]. They search this space with a multi-objective, Pareto-based GA that aims to minimize the building envelope's thermal transfer and cost and to maximize the available daylight, yielding 5,000 evaluated candidates. ...
... For example, Nagy et al. optimize an office layout in terms of six performance objectives [16]. Similarly to [4], they cluster and visualize a set of 10,000 design candidates, but have difficulty with identifying meaningful trends ( Figure 3). ...
Conference Paper
Full-text available
This paper introduces performance-informed design space exploration (DSE) to question the relationship between explicit, quantitative optimization problems and "wicked", co-evolving architectural design problems and to support the reframing of architectural design optimization as a medium for reflection. The paper proposes selection, refinement, and understanding as key aspects of performance-informed DSE and surveys current approaches to performance-informed DSE: (1) Clustering and Pareto-based optimization support selection by reducing large numbers of parametric design candidates into smaller and more meaningful sets of choices. (2) Surrogate modelling supports refinement by approximating time-intensive simulations in real-time, which is important for interactivity. (3) Multi-variate visualizations and statistical analyses support understanding by providing insights into characteristics of design spaces and fitness landscapes. Finally, the paper discusses a novel tool for visual and interactive, performance-informed DSE, Performance Explorer. Performance Explorer combines the real-time feedback afforded by surrogate models with a multi-variate visualization of fitness landscapes. A user test of Performance Explorer uncovered several performance-informed DSE strategies followed by the participants. Consisting of different combinations of selection, refinement, and understanding, these strategies illustrate and-to some extent-validate the proposed framework for performance-informed DSE.
... Experiments examining the resulting population of alternate designs and providing insight into the relationship between architectural features and design performance were conducted by (Chen, Janssen and Schlueter, 2015). The experiments show that it is possible to gain general knowledge by linking architectural features to design performance. ...
Conference Paper
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Every time an object is built, it creates a relationship with the ground. Architects have a full responsibility to design the building by taking the ground into consideration. In the field of architecture, using data mining to identify any unusual patterns or emergent architectural trends is a nascent area that has yet to be fully explored. Clustering techniques are an essential tool in this process for organising large datasets. In this paper, we propose a novel proof-of-concept workflow that enables a machine learning computer system to cluster aspects of an architect's building design style with respect to how the buildings in question relate to the ground. The experimental workflow in this paper consists of two stages. In the first stage, we use a database system to collect, organise and store several significant architectural precedents. The second stage examines the most well-known unsupervised learning algorithm clustering techniques which are: K-Means, K-Modes and Gaussian Mixture Models. Our experiments demonstrated that the K-means clustering algorithm method achieves a level of accuracy that is higher than other clustering methods. This research points to the potential of AI in helping designers identify the typological and topological characteristics of architectural solutions and place them within the most relevant architectural canons
... Increasingly, researchers are exploring ways to implement data analysis and modeling techniques during the architectural design process. [32][33][34] One prominent application is data visualization for high-dimensional design spaces. 35,36 Others have proposed using statistical methods to consider relationships between variables and objectives, including Bayesian inference. ...
Many architectural designers recognize the potential of parametric models as a worthwhile approach to performance-driven design. A variety of performance simulations are now possible within computational design environments, and the framework of design space exploration allows users to generate and navigate various possibilities while considering both qualitative and quantitative feedback. At the same time, it can be difficult to formulate a parametric design space in a way that leads to compelling solutions and does not limit flexibility. This article proposes and tests the extension of machine learning and data analysis techniques to early problem setup in order to interrogate, modify, relate, transform, and automatically generate design variables for architectural investigations. Through analysis of two case studies involving structure and daylight, this article demonstrates initial workflows for determining variable importance, finding overall control sliders that relate directly to performance and automatically generating meaningful variables for specific typologies.
... 7 One approach to elevate optimization to understanding comes from unsupervised machine learning. Chen et al. 8 search a space containing 752,640 design variants with a multi-objective genetic algorithm that aims to minimize the building envelope's thermal transfer and cost and to maximize the available daylight, yielding 5000 evaluated (i.e. simulated) variants. ...
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This article presents a method to visualize high-dimensional parametric design spaces with applications in computational design processes and interactive optimization. The method extends Star Coordinates using a triangulation-based interpolation with Barycentric Coordinates. It supports the understanding of design problems in architectural design optimization by allowing designers to move between a high-dimensional design space and a low-dimensional Performance Map. This Performance Map displays the characteristics of the fitness landscape, develops designers’ intuitions about the relationships between design parameters and performance, allows designers to examine promising design variants, and delineates promising areas for further design exploration.
... Recently, ADO has received new understandings both as a generative design tool that provides starting points for further design exploration (Bradner et al. 2014) and as a representational tool that aids the understanding of design problems (Wortmann et al. 2015). Chen et al. (2015) attempt to group large numbers of design variants -found with a genetic algorithm-with a clustering method to better understand the relationship between design features and performance. Their effort is symptomatic of the need for human-understandable representations of fitness landscapes. ...
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This paper presents a novel method to visualize high dimensional parametric design spaces with applications in computational design space exploration. Specifically, the visualization method presented here supports the understanding of design problems in architectural design optimization by allowing designers to move between a high dimensional design space and a low dimensional "performance map". This performance map displays the characteristics of the fitness landscape, develops designers' intuitions about the relationships between design parameters and performance, allows designers to examine promising design variants and delineates promising areas for further design exploration.
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Evolutionary developmental design (Evo-Devo-Design) is a design method that combines complex developmental techniques with an evolutionary optimisation techniques. In order to use such methods, the problem specific developmental and evaluation procedures typically need to be define using some kind of textual programming language. This paper reports on an alternative approach, in which designers can use Visual Dataflow Modelling (VDM) instead of textual programming. This research described how Evo-Devo-Design problems can defined using the VDM approach, and how they can subsequently be run using a Distributed Execution Environment (called Dexen) on multiple computers in parallel. A case study is presented, where the Evo-Devo-Design method is used to evolve designs for a house, optimised for daylight, energy consumption, and privacy.
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Architecture, Engineering, and Construction (AEC) professionals typically generate and analyze very few design alternatives during the conceptual stage of a project. One primary cause is limitations in the processes and software tools used by the AEC industry. The aerospace industry has overcome similar limitations by using Process Integration and Design Optimization (PIDO) software to support Multidisciplinary Design Optimization (MDO), resulting in a significant reduction to design cycle time as well as improved product performance. This paper describes a test application of PIDO to an AEC case study: the MDO of a classroom building for structural and energy performance. We demonstrate how PIDO can enable orders of magnitude improvement in the number of design cycles typically achieved in practice, and assess PIDO's potential to improve AEC MDO processes and products.
Multivariate data arise when researchers record the values of several random variables on a number of subjects or objects or perhaps one of a variety of other things (we will use the general term “units”) in which they are interested, leading to a vector-valued or multidimensional observation for each. Such data are collected in a wide range of disciplines, and indeed it is probably reasonable to claim that the majority of data sets met in practise are multivariate. In some studies, the variables are chosen by design because they are known to be essential descriptors of the system under investigation. In other studies, particularly those that have been difficult or expensive to organise, many variables may be measured simply to collect as much information as possible as a matter of expediency or economy.
One of the problems with a lot of sets of multivariate data is that there are simply too many variables to make the application of the graphical techniques described in the previous chapters successful in providing an informative initial assessment of the data. And having too many variables can also cause problems for other multivariate techniques that the researcher may want to apply to the data. The possible problem of too many variables is sometimes known as the curse of dimensionality (Bellman 1961). Clearly the scatterplots, scatterplot matrices, and other graphics included in Chapter 2 are likely to be more useful when the number of variables in the data, the dimensionality of the data, is relatively small rather than large. This brings us to principal components analysis, a multivariate technique with the central aim of reducing the dimensionality of a multivariate data set while accounting for as much of the original variation as possible present in the data set. This aim is achieved by transforming to a new set of variables, the principal components, that are linear combinations of the original variables, which are uncorrelated and are ordered so that the first few of them account for most of the variation in all the original variables. In the best of all possible worlds, the result of a principal components analysis would be the creation of a small number of new variables that can be used as surrogates for the originally large number of variables and consequently provide a simpler basis for, say, graphing or summarising the data, and also perhaps when undertaking further multivariate analyses of the data.
A building design team has faced several decision-making problems when assessing building envelope materials and designs for a private high-rise residential building in the early design stage. This study developed an automated fuzzy Knowledge-based Decision Support System Quality Function Deployment (KBDSS-QFD) tool to facilitate the team to mitigate such problems. A case study of the design team comprising an architect, a civil and structural (C&S) engineer and a mechanical and electrical (M&E) engineer was selected as the research design of this study. Results from the qualitative data analysis showed that the tool has the potential to mitigate the decision-making problems. The contributions of using this automated tool include not only achieving better design management but also raising the level of productivity in the construction industry.
With contributions from Brady Peters, Onur Yuce Gun and Mehdi Sheikholeslami Design is change. Parametric modeling represents change. It is an old idea, indeed one of the very first ideas in computer-aided design. In his 1963 PhD thesis, Ivan Sutherland was right in putting parametric change at the centre of the Sketchpad system. His invention of a representation that could adapt to changing context both created and foresaw one of the chief features of the computer aided design (CAD) systems to come. The devices of the day prevented Sutherland from fully expressing what he might well have seen, that parametric representations could deeply change design work itself. I believe that, today, the key to both using and making these systems lies in another, older idea. People do design. Planning and implementing change in the world around u one of the key things that make us human. Language is what we say; design and making is what we do. Computers are simply a new medium for this ancient enterprise. True, they are the first truly active medium. They are general symbol processors, almost limitless in the kind of tool that they can present. With much craft and care, we can program them to do much of what we call design. But not all. Designers continue to amaze us in with new function and form. Sometimes new work embodies wisdom, a precious commodity in a finite world. To the human enterprise of design, parametric systems bring fresh and needed new capabilities in adapting to context and contingency and exploring the possibilities inherent in an idea. What is the new knowledge and skill designers need to master the parametric? How can we learn and use it? That is what this book is about. It aims to help designers realize the potential of the parameter in their work. It does so by combining basic ideas of parametric systems themselves with equally basic ideas from both geometry and computer programming.
Publisher Summary This chapter presents the basic concepts and methods of cluster analysis. The requirements of clustering methods for massive amounts of data and various applications are studied. Several basic clustering techniques are discussed organized into the following categories: partitioning methods, hierarchical methods, density-based methods, and grid-based methods). Evaluation process for clustering methods is also discussed. A cluster is a collection of data objects that are similar to one another within the same cluster and are dissimilar to the objects in other clusters. The process of grouping a set of physical or abstract objects into classes of similar objects is called clustering. Clustering is the process of grouping a set of data objects into multiple groups or clusters so that objects within a cluster have high similarity, but are very dissimilar to objects in other clusters. Dissimilarities and similarities are assessed based on the attribute values describing the objects and often involve distance measures. Clustering as a data mining tool has its roots in many application areas such as biology, security, business intelligence, and Web search. Cluster analysis has extensive applications, including business intelligence, image pattern recognition, Web search, biology, and security. Cluster analysis can be used as a standalone data mining tool to gain insight into the data distribution, or as a preprocessing step for other data mining algorithms operating on the detected clusters.
Multidisciplinary design optimization (MDO) has been identified as a potential means for integrating design and energy performance domains but has not been fully explored for the specific demands of early stage architectural design. In response a design framework, titled Evolutionary Energy Performance Feedback for Design (EEPFD), is developed to support early stage design decision-making by providing rapid iteration with performance feedback through parameterization, automation, and multi-objective optimization. This paper details the development and initial validation of EEPFD through two identified needs of early stage design: 1) the ability to accommodate formal variety and varying degrees of geometric complexity; and 2) the ability to provide improved performance feedback for multiple objective functions. Through experimental cases the research presents effective application of EEPFD for architectural design.
In this paper, multiple criteria decision making methods are studied in the context of building design. The approach is to compare the functionality and the results provided by different methods on three test problems that represent various design situations. The number of criteria in the test problems are two, three and four. Multicriteria optimization is applied to generate the alternatives, among which a preferred solution is to be searched by the decision making methods. Six methods have been selected for comparison: the weighted sum method, the weighted product method, VIKOR, TOPSIS, PROMETHEE II, and a procedure based on the PEG-theorem. The numerical study on the test problems indicate that in most cases, the methods provide different solutions. The PEG-procedure tends to find a well-balanced solution, where none of the criteria is emphasized. While the “best” MCDM method is not discovered in the study, information about the performance of the methods in building design problems is presented.