Project Discover: An Application of Generative Design for
Architectural Space Planning
Danil Nagy, Damon Lau, John Locke, Jim Stoddart,
Lorenzo Villaggi, Ray Wang, Dale Zhao and David Benjamin
The Living, an Autodesk Studio
New York, NY USA
life@thelivingnewyork.com
ABSTRACT
This paper describes a flexible workflow for generative design applied to architectural space planning. We describe this
workflow through an application for the design of a new office space. First, we describe a computational design model
that can create a variety of office layouts including locating
all necessary programs and people using a small set of input
parameters. We then describe six unique objectives that evaluate each layout based on architectural performance as well
as worker-specific preferences. Finally, we show the use of
a multi-objective genetic algorithm (MOGA) to search
through the high-dimensional space of all possible designs,
and describe several visualization tools that can help a designer to navigate through this design space and choose good
designs. We conclude by discussing the future of such computational workflows in design and architecture. Our hope is
that they go beyond basic automation to create an expanded
role for the human designer and a more dynamic and collaborative interaction between computer design software and
human designers in the future.
Author Keywords
Parametric modeling, simulation, genetic algorithms, multiobjective optimization, evolutionary design, generative design, architecture
ACM Classification Keywords
I.6.5 SIMULATION AND MODELING - Model Development
1
INTRODUCTION
Computers and computer-aided design (CAD) software have
had a dramatic impact on architectural practice since the
emergence of computers in academia in the 1950s, and especially since the introduction of personal computing in the
1980s. Although early researchers envisioned a wide-ranging future interaction between computers and human designers [10], the first computer tools to be widely adopted by architectural designers were computerized versions of traditional drafting and rendering tools. While they allowed designers to produce content much faster than with traditional
methods, they did not fundamentally change the process of
design.
1.1 Parametric design
In the past decade, a new type of design software has
emerged which is fundamentally changing the way designers
use computers to develop and refine their designs. Known as
parametric design software, these tools allow the designer to
not only define a final geometric solution, but to describe the
entire system behind how a design is generated. Within this
larger system description, the designer can expose specific
parameters, or values that drive different variations of the design.
Although such a model takes more work initially to describe,
it offers the designer many advantages. First, the parametric
approach makes it easy to create variations and custom adaptations of a design. Instead of manually creating multiple
versions for different applications, the designer can expose
the critical parameters that drive different variations and automatically generate different versions by changing those parameters. Second, a well-structured parametric model is
more adaptable to change in the future. Since it is defined by
a series of operations, the design can be easily adapted to
changing conditions instead of rebuilding the model from
scratch each time.
Most importantly, the parametric approach allows the designer to think through design solutions in a deeper and more
dynamic way than possible with traditional methods. In a traditional approach, the designer studies the design problem,
internalizes all of its constraints and objectives, and then uses
their skill and experience to craft a single design solution, or
a handful at most. With the parametric approach, the constraints and goals of the design problem can be directly embedded within the parametric model, which can then be used
to automatically generate a variety of solutions. Instead of
designing a single solution, the designer can now think of
designing a multi-dimensional ‘space’ of design. Each dimension of this design space represents one of the critical
parameters exposed by the parametric model, and each individual design variation can be found somewhere within this
hyper-dimensional space.
1.2 Beyond parametric
While the parametric approach has broadened the possibilities of design and pushed the boundaries of human-computer
interaction in the design process, the exploration of the design space is still limited by the abilities of the human designer. Although some parameters may be set by the constraints found explicitly in the design problem, for the most
part the human designer must investigate different design options by manually varying individual parameters and evaluating each option using their own criteria and intuition in a
way not much different than with traditional design methods.
applied similar optimization methods to a variety of architectural problems. However, their optimization criteria are similarly constrained to well-known and easily simulated physical objectives such as structural and environmental performance. In contrast, we propose a more flexible workflow that
can accommodate a diversity of optimization criteria, including those dealing directly with how space is used and experienced at the occupant level
The concept of generative design, as described in this paper,
addresses this limitation by tasking a computer with exploring the design space semi-autonomously, and then reporting
back to the designer which options it considers promising for
further analysis. Because a computer can process information much quicker than a human, such a system allows a
much deeper exploration of complex design spaces. Traditionally, such an approach has been used to optimize a given
model to achieve maximum possible performance based on
concrete objectives [8]. With a model of sufficient complexity, however, a generative design system can also be used to
reveal interesting parts of the design space and discover
novel design solutions that would otherwise be hidden to the
human designer.
The quantification of spatial experience has also been explored by a variety of authors. Hillier, et al. [4] proposed a
variety of analytical tools for studying spatial configurations
which they called ‘space syntax’. Peponis, et al. [11] extend
this work by proposing a universal method for understanding
plan topology through linear representation. Turner, et al.
[12] propose a view-based ray tracing technique for understanding and analyzing spatial configurations. While the proposed methods can help the designer derive quantitative data
about their designs, they are only offered as tools to aid a
traditional design process. In contrast, we extend these methods and show how they can be used as measures of spatial
performance to guide an automated optimization process.
To take advantage of the possibilities of generative design,
the basic parametric model must be extended in two ways.
First, the model must include concrete metrics by which each
design option can be evaluated. Since the computer does not
have any inherent intuition about design, the human designer
must explicitly describe to the computer how to determine
which designs perform better than others. Second, the model
needs to be connected to a search algorithm that can control
the input parameters of the model, get feedback from the
metrics, and intelligently tune the parameters to find high
performing designs while also exploring the full possibilities
of the design space. One of the most promising of these algorithms is the multi-objective genetic algorithm (MOGA),
which uses principles of evolution to create sequential generations of designs and evolve them to contain higher performing designs over time [9].
Our proposed workflow of generative design for architecture
is organized into four steps: (1) the design of a geometric
model which can create many design variations, (2) the design of a series of performance metrics which can be used to
measure the performance of a single design, (3) the exploration of the model’s design space through a MOGA, and (4)
the investigation of the resulting design data through statistical analysis. Furthermore, we propose this method as only
one component within a broader design process. Thus, there
are several steps that must be taken both ‘before generative
design’ in order to establish a design concept to drive the geometric model and collect necessary data for the performance metrics. Similarly, there are a variety of steps that
must be taken ‘after generative design’ in order to achieve
other criteria and develop the selected design solution to the
level of a final constructible design.
The remainder of this paper describes our development of a
custom workflow for generative design specifically geared
towards architectural space planning, and our application of
this workflow to the design of a new office space.
2
RELATED WORK
The application of multi-objective optimization towards
solving complex mechanical design problems is well-known
in the field of engineering. Marler and Arora [8] provide a
good overview of various applications. However, being constrained to the goals of engineering problems, these applications are limited to using only structural performance as optimization criteria.
Liggett [7] provides a thorough historical overview of automated methods for space planning in architecture, including
the use of genetic search algorithms. Derix [2], Keough and
Benjamin [6], Chronis et al. [1] and Gerber et al. [3] have
3
METHODOLOGY
3.1 Before generative design
As with any architectural design project, the process begins
by studying the design problem, understanding its goals and
constraints, and formulating a vision and concept for the design. The vision of the project was to create a dynamic and
highly functional new office space for Autodesk in Toronto.
Some of the constraints included:
1.
The outline of the three floors of an existing new building where the office would be located
2.
The programmatic requirements, including specific
numbers of shared amenities such as meeting rooms
3.
Occupation by up to 300 workers
4.
Diversity of different departments, project teams, and
workstyles that the office needed to accommodate
Based on the vision of the project and these constraints (the
goals of the project were established in a subsequent step),
we developed an architectural concept around breaking up
the floorplan into a series of individual ‘neighborhoods’. In
this concept each neighborhood is a work-area for an individual department or project team. The neighborhoods are
divided by shared amenity spaces, which are contained
within standalone rooms. These rooms create visual variation
within the office space as a whole, while providing a degree
of privacy and uniqueness to each neighborhood.
Once this basic concept was established, the design problem
became the arrangement of neighborhoods within the building floorplan, the location of shared amenity spaces, and the
assignment and placement of teams and individual workers
in the neighborhoods. In architecture, this type of problem is
known as space planning, and deals with the optimal arrangement of programs and spaces within a fixed plan. Because there are so many possible variations, this type of problem is traditionally difficult to solve for a human designer,
and typically relies heavily on intuition and rules of thumb,
along with iterative design and testing of a large variety of
solutions before finally choosing the best one. Due to the
complexity of this problem, it was actually the subject of one
of the first applications of computing to architectural design
[5]. For us it was the perfect problem to test the possibilities
of the generative design process previously described.
Besides exploring many design options, another advantage
of the generative design approach is that we can evaluate designs at a much higher level of detail than possible with traditional approaches, including evaluating some aspects of the
design which are often ignored or abstracted in typical space
planning projects. In this case we wanted to judge each design not only on global architectural goals such as maximizing the amount of light in the space, but also on local goals
having to do with the individual preferences of each of the
office’s future occupants.
To get information about these preferences we distributed
surveys to all individuals and teams in the office, asking their
preferences in terms of which amenities they want to be close
to, which other teams or individuals they often work with,
and the office conditions they prefer. Based on this information, we were ready to construct the generative design
model that could generate unique design solutions and evaluate each one based on specific performance metrics.
3.2 Geometric model
The first step was to create a geometric model that could define a set of neighborhoods within the two main floors of the
office building, position shared amenity zones between
neighborhoods, and then locate specific programs within the
amenity zones and individual workers within the neighborhoods. To create each individual design, our geometric
model applies the following algorithm (see Figure 1):
1.
2.
3.
4.
5.
6.
Locate a seed point for every neighborhood
Draw neighborhood boundaries based on edges equidistant from the neighborhood seeds (similar to a voronoi diagram)
For each neighborhood, choose one of the edges along
which to place a shared amenity zone
Place shared programs within amenity zones based on
a greedy fill algorithm
Assign teams to neighborhoods, also based on a greedy
fill algorithm
Assign people to specific desks in neighborhood based
on list order.
To establish the neighborhood seeds, a linear spine is drawn
over the plan and the seeds are distributed evenly along this
spine. Then, each seed’s exact location is refined by two individual parameters – the first defines the distance to move
along the spine from the initial point, and the second defines
the distance to move away from the spine in the perpendicular direction. A third unique parameter chooses the edge
along which to place the amenity zone by specifying its normalized distance along the neighborhood boundary. The
placement of individual amenity programs, teams, and individuals is not parameterized, but is instead directly determined according to the geometry of the neighborhood
boundaries.
and complex goals, some of which are difficult if not impossible to quantify such as beauty, fairness, quality of space,
elegance, and novelty. To deal with this potential difficulty,
we divide the set of all possible architectural performance
metrics into three groups:
•
Those that can be easily quantified and calculated using existing tools (e.g. daylight analysis)
With 15 neighborhoods controlled by 3 unique parameters
each, the model is completely described by 45 unique parameters. Currently, there are no theories or rules for how many
individual parameters a model should contain to ensure that
a robust search of the design space is both feasible and complex enough to create a wide variety of design options. In
general, the current best practice is to make this number as
small as possible, while ensuring that each critical aspect of
the design is controlled by a unique, continuous variable.
The uniqueness of each parameter is important so that the
algorithm can directly control each aspect of the design independently while searching for the best combinations. The
continuity of each parameter is important because the algorithm should be able to fine-tune the parameter settings by
predicting future results based on past experiences. If each
setting of a parameter yields completely different results, it
will be far more difficult for the algorithm to search through
the design space.
•
Those that can theoretically be quantified but cannot be
computed using existing tools, for which new computation tools must be developed (e.g. employee work style
preference and activity hotspots)
•
Those that cannot be quantified and must be addressed
through other means outside of generative design (e.g.
beauty)
Finally, in order to take advantage of learning within the automated search process, the entire model needs to be completely deterministic, relying only on the input parameters
exposed to the algorithm to generate each design. No noise
or random parameters should be utilized in the geometric
model.
3.3 Design metrics
To allow the search algorithm to automatically measure the
performance of each design generated, we also defined a set
of unique goals, or metrics, which rate the relative performance of each design along a set of criteria. These metrics
form the set of output values that the search algorithm can
use to evaluate how well each design option performs, and to
guide its search of the design space toward discovering
higher performing designs.
While this classification addresses the current limitations of
the generative design workflow, the conclusion of this paper
outlines some ideas for future research that suggests machine
learning as a way to quantify and evaluate goals that are challenging to compute using direct calculation. In our case, our
analysis of the project goals along with discussions with the
managers and individual workers yielded six discrete design
metrics to evaluate each design (see Figure 2):
1.
Adjacency preference, which measures the travel distance from each employee to their preferred neighbors
and amenities
2.
Work style preference, which measures the suitability
of an assigned neighborhood’s daylight and distraction
measurements to the assigned team’s surveyed preferences
3.
Buzz, which measures the amount and distribution of
high-activity zones
4.
Productivity, which measures concentration levels at
individual desks based on sight lines to other desks and
other noise sources
5.
Daylight, which measures the total amount of natural
daylight entering the space throughout the year.
6.
Views to outside, which measures the ratio of workspaces with an unobstructed view to the exterior glass
façade
One apparent limitation of the generative design process is
that all performance criteria for a given design system must
be exposed to the search algorithm as a numeric quantity.
Thus, any performance metric that we want the algorithm to
consider must be both quantifiable and computable in a reliable and efficient way for all solutions within the design
space.
One of these – daylight – is well understood and can be calculated using existing analysis tools. The other five were either novel or highly specific to our design goals. For these
we developed our own custom analysis tools which we built
directly into the generative design model.
In engineering applications where similar optimization
workflows have been explored for a number of years, the
metrics are relatively straight forward. For example, the
strength of a structural component is easy to compute using
standard finite element analysis (FEA) software. An architectural design problem, however, often has many competing
Each new design project potentially brings with it a unique
set of goals and performance requirements, which will never
be fully described in any given design software. Thus, part
of the responsibility of the designer in the generative design
workflow is to be able to use computational tools such as
parametric modeling and custom scripting to describe their
unique design goals to the computer. Although this sometimes makes the design task more difficult, it also has the
potential to expand the role of the human designer while
opening up new opportunities for design though enhanced
human-computer interaction.
Along with the geometric model, the design metrics constitute the second half of the full generative design model. This
model is a closed system that (1) takes in a discrete set of
input parameters, (2) creates a unique design solution based
on those parameters, (3) evaluates the design along a set of
unique metrics, and (4) outputs those metrics as a set of discrete values. When this system is connected to a search algorithm, it can be automatically explored for good design solutions. However, although the algorithm can explore many
more designs than possible through traditional manual
means, it can only evaluate them based on the specified metrics output by the model. Thus it is crucial that the chosen
metrics sufficiently capture the priorities of the design problem, and accurately describe the relative performance of each
design according to those metrics.
3.4 Design evolution
Once we have defined the generative design model, we can
use a search algorithm to automatically explore the space of
possible designs and discover novel and high performing design options. A search algorithm is a subset of a general optimization algorithm, which is concerned with discovering
optimal settings of input parameters of a function which
maximizes the value of one or more outputs. Although many
search algorithms exist, the one of particular interest to us is
the multi-objective genetic algorithm (MOGA).
This algorithm generates designs in groups called generations. The first generation is composed of a set of initial designs either randomly or evenly sampled from the design
space. Subsequent generations are then produced by either
directly taking high performing designs from the previous
generation (a process called elitism), or randomly mixing the
parameters of two high performing designs to create a single
new design (a process called cross-breeding). Each new design’s input parameters may also be slightly modified before
it enters the population (a process called mutation). This process is then repeated for multiple generations, either until the
target number of generations is reached, or performance fails
to improve for a certain number of generations. In this way,
a MOGA uses concepts found in natural evolution to generate new designs based on the input parameters (genome) of
previous high performing designs, thus gradually promoting
the best options (survival of the fittest) and ‘evolving’ higher
performing designs over time.
This type of algorithm has many advantages in the context of
generative design. As the name implies, the MOGA can optimize designs along any number of output metrics. Furthermore, the user does not need to prioritize or weight the individual metrics beforehand. This is because the MOGA determines relative performance based on the idea of dominance
rather than the absolute difference in metric values. A design
is considered better performing than another if it dominates
or performs better in one or more of the metrics. Thus the
algorithm will continue to produce designs that are dominant
in as many of the metrics as possible, and the user can later
decide how to prioritize the metrics.
Another advantage of the MOGA is that it works stochastically by sampling designs from the design space, and trying
to learn optimal configurations of the input parameters
through experimentation. Other optimization algorithms
such as gradient descent rely on computing gradients for
each objective with respect to each input parameter. This is
not possible with most parametric design models, which are
defined by a large number of geometric functions, none of
which can be easily differentiated. Thus, such model can
only by optimized through a stochastic experimental process.
Finally, genetic algorithms have also been shown to be exceptionally good at finding the overall best performing designs within a design space (the global optimum) while
avoiding locally high-performing areas that may not be the
best overall. By recombining high-performing designs from
different areas of the design space, and slightly mutating designs over time, genetic algorithms can avoid local optimums
more effectively than simpler, more deterministic algorithms
such as gradient descent.
As with any optimization algorithm, the MOGA has hyperparameters that need to be set before beginning the search
process. These hyper-parameters have a significant impact
on how the algorithm behaves and thus are an important aspect of generating good results. However, these settings also
depend on the nature of the problem, so their tuning is often
a product of heuristics and previous experience. The MOGA
hyper-parameters include:
•
The sampling method or the starting population
•
The size of the starting and subsequent populations
•
The termination criteria of the process (run for a set
number of generations, or continue until no new better
designs are found for a number of generations?)
•
Cross-over rate, which dictates how many of a generation’s designs are created by combining two designs
from the previous generations
•
Mutation rate, which dictates the rate at which a design’s parameters are slightly modified before entering
the next generation
In our case, we used generations of 100 designs each and ran
the process for 100 generations creating 10,000 designs. The
starting population of 100 designs was generated by randomly sampling from the design space. Through experimentation we settled on settings of 95% for cross-over, and 0.2%
for mutation. The entire process ran over 5 days on a single
MacBook Pro with a 2.60GHz Intel Core i7 processor and 16
GB RAM.
3.5 Data analysis
This process generated a data set containing 10,000 designs,
including the input values for each design and its score along
the six metrics. One approach at this stage would be to filter
the dataset by the metric scores and directly select a few
high-performing designs for further analysis. However, depending on the complexity of the design problem such a selection can be challenging for a number of reasons.
First, the various metrics might be directly competing with
each other, which means that there is actually no single best
design but a range of equally high performing designs along
the trade-off between competing metrics. For example, when
designing an industrial component there is typically a tradeoff between the part’s weight and its strength. In this case,
unless there is a specific weight or strength target, it would
be difficult to select a single ‘best’ design without first understanding how this trade-off works.
Second, as previously mentioned, the hyper-parameters of
the MOGA have a significant effect on how the search
works, and proper tuning of these settings depends on the
particularities of each generative design model (including
how many and what type of input parameters and output metrics are used). Thus, it is rarely enough to run only a single
search process, and it is helpful if the results of every search
are studied in depth to determine how the hyper-parameters
may be tuned for future runs.
Finally, one of the advantages of a learning-based process
such as MOGA is that it not only finds high-performing designs but also performs the search in a structured, semi-intelligent manner. By investigating the search process itself,
more can be learned about the nature of the problem as a
whole. In order to investigate this process and gain a deeper
understanding of the design space, we developed a series of
data analysis tools to aid the designer in exploring the dataset
of designs generated by the MOGA.
Inheritance analysis
In addition to the input and output data for each design, the
MOGA also outputs a history of how these designs were generated. Figure 3 shows a plot of this data, with each point
representing a design, and each column of points representing a generation of designs. Two colored lines entering a
point from the left indicates that the design was formed
through cross-breeding of those two designs. A thin black
line indicates that the design was carried over directly into
the next generation.
In this plot you can see an instance where a newly formed
design is high performing and thus is consistently carried
over into future generations (A), as well as a case where a
new design gets carried over one generation but then dies out,
likely due to the fact that it was not as high-performing as
others in its generation (B). Studying such plots helps us understand how the algorithm explored the design space, how
dominant design lineages are formed, and helps locate potential blind spots in the design space missed by the algorithm.
Input space analysis and clustering
To analyze how the sampled designs are distributed within
the design space, we can use principal component analysis
(PCA) to transform the 45-dimensional input space into a
new 45-dimensional space where the dimensions are now ordered according to the extent to which they describe the variance in the data. Then we can use the first two PCA components to create the best-possible two-dimensional projection
of the high-dimensional design space and see how the sampled designs are organized within that space.
To further study the distribution of designs in the design
space we can cluster them based on Euclidean distance in the
full 45-dimensional design space using the K-means algorithm (see Figure 4). Intuitively, this gives a representation
of different design typologies or strategies that share similar
input parameters. Once we have assigned the clusters to each
design we can study how these design typologies relate to
performance in the output metrics. For example we can see
if certain design types perform better in some metrics than
others. Such tools can help us understand the design problem
in general and reveal potential design strategies, rather than
simply picking the single best design.
Metric space analysis
Once we have understood the distribution of designs in the
input space, we can study how the designs perform along the
six performance metrics. Since there are usually less output
metrics than input parameters, the space of outputs is not typically as high-dimensional as the input design space. Nevertheless, if there are more than 3 or 4 metrics it can be difficult
to represent the results on a single plot. Our typical approach
is to do a pairwise plot of all the output metrics to find combinations of metrics that have an interesting relationship or a
clear trade-off. We can then study the tradeoffs in greater detail by plotting them against each other on a scatter plot (see
Figure 5).
Once we have studied the performance of the whole set of
designs, we can select a subset for further manual analysis.
As a baseline the MOGA will provide us with a set of designs
which are statistically dominant called the Pareto designs. To
narrow it down further we can look for designs that occur at
different points along the trade-offs, which can help us to see
the effect of those trade-offs on the design solution. We can
also use the cluster information generated earlier to identify
cases where similar performance was achieved by different
typologies of designs.
3.6 After generative design
or novelty that are crucial to good design but have traditionally been difficult to relate to a computer.
Once a set of interesting designs is selected, they can be further analyzed by the human designer, discussed with the
stakeholders, and developed into a final design. It is important to note that since the MOGA follows a stochastic process based on sampling a limited number of designs from the
design space, the overall optimal design will not necessarily
be found through the search process. Furthermore, as discussed previously, not all aspects that are important to an architectural design can necessarily be represented as a metric
in the generative design model. Some aspects, such as
beauty, cannot be quantified, and thus need to be considered
once the generative design process is complete.
As these types of workflows continue to develop in the future, it is our hope that they not only allow designers to develop high performing design options, but also help them understand their design problems better through a more collaborative human-machine design interaction. This will allow
us to move far beyond the basic automation of tasks evident
in early CAD tools, and leverage the full potential of true
computer-aided design.
Finally, most generative design models including the spaceplanning model presented in this paper are fairly abstract and
oversimplified, providing only rough geometry, boundary,
and location information. After a basic space-planning strategy is selected, there is still much refinement and design
work to be done, including selecting architectural materials
and designing connection details, to get it to the level of a
final constructible design.
2. Derix, Christian, “In-Between Architecture Computation”. International Journal of Architectural Computing
7, 4 (2009), 565-585.
Therefore, the process does not end with choosing one of the
designs found by the algorithm. Instead, a deep analysis of
various high performing designs and their trade-offs should
suggest potential design strategies that the designer can further explore to achieve a final best design.
4
CONCLUSION
This paper described our development of a generative design
workflow for architecture, and our application of it for the
design of a new office space for Autodesk in Toronto.
Although the results of this investigation have been very encouraging, the process also has some limitations. Currently,
the placement of programs and individual people in the plan
depends on the neighborhood geometry, and thus cannot be
directly controlled by the MOGA. To get a better and more
targeted search we would need to develop methods to directly parameterize this placement and expose those parameters to the algorithm.
Another limitation is that the calculation of each design is
still relatively slow – about one minute for each design –
which limits the amount of exploration we can do. Automatically analyzing 10,000 designs already dramatically improves the capacity of a human designer, but is relatively
small considering it is sampled from a 45-dimensional design
space. Distributing the execution of designs within a single
generation over several computers in a network would allow
many more designs to be evaluated.
Finally, the workflow can be improved by integrating other
types of modelling, particularly machine learning, for quantifying aspects of the designs that are difficult or impossible
to compute through direct calculation. This is particularly interesting because it might allow the computer to develop
knowledge of various design factors such as comfort, beauty,
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