optimizing urban systems: integrated optimization of
TRANSCRIPT
SimAUD 2020 May 25-27 Vienna, Austria
© 2020 Society for Modeling & Simulation International (SCS)
Optimizing Urban Systems: Integrated Optimization of Spatial Configurations
Serjoscha Duering1, Angelos Chronis2, Reinhard Koenig3
1 AIT Austrian Institute of
Technology
Vienna, Austria
2AIT
Vienna, Austria
3AIT, Bauhaus University
Weimar
Munich, Germany
ABSTRACT
The importance of performance feedback and simulation in
early design stages has been argued for numerous times.
Therefore, the employment of generative design solutions
becomes more and more an indispensable part in the
production pipeline of architecture and space. However
various barriers prevent the integration of such tools in early
design stages, including the lack of design practice studies,
domain expertise and commonly time-consuming and
disintegrated simulation engines. In our study we
demonstrate a method and workflow on how the
interoperability and computational cost barriers can be
reduced, using an integrated simulation and analysis
approach based on machine learning and generative design.
We illustrate our approach by optimizing the spatial
configuration of a hypothetical, medium sized urban design
project in the city of Vienna for a diverse set of fitness
objectives. We conclude by showing how gained results can
also support manual design processes.
Author Keywords
Simulation; Deep Learning; Urban Design; Optimization.
ACM Classification Keywords
I.6.1 SIMULATION AND MODELING.
1. INTRODUCTION
Despite ever-growing computing power, calculation time is
yet a significant bottleneck in the field of data-driven design
and the optimization of spatial configurations of buildings -
and even more so of cities. Nevertheless, the inclusion of a
diverse array of performance indicators is crucial in the quest
to capture the factors defining the performance of urban
systems holistically.
Looking at the bigger picture, global challenges like the
climate crisis and peaking rates of urbanization in many
developing countries call for the integration of both climatic
and social aspects when drafting plans and designing cities.
At the same time, the complexity of a design task grows
exponentially with the number of factors considered,
creating new challenges for urban designers and planners.
Thus, the employment of generative design solutions
becomes more and more an indispensable part in the
production pipeline of architecture and space. Recent
advances in generative design, simulation, and
computational optimization opened up possibilities in
employing data driven workflows to achieving design
solutions of unprecedented performance. Notably, the
adoption of novel paradigms such as deep-learning appears
to have the potential to become another game-changer.
This paper showcases an attempt to optimize the spatial
configuration of urban systems through the integration of
multiple simulation engines into one framework. Most
importantly, we combine deep-learning enabled real-time
estimations of microclimate (thermal comfort and wind
speed) with graph-based mobility and accessibility models.
This setup permits previously unfeasible and more
comprehensive options when defining fitness objectives used
during an evolutionary optimization process (EO) thus
allowing a more holistic understanding of the performance
potential inherent in the design space of urban design
projects.
2. BACKGROUND
The importance of performance feedback and simulation in
early design stages has been argued for numerous times.
Despite their significance in optimizing the performance of
architecture and urban design projects, simulation and
analysis tools are still not meeting the expectation of design
practice [1]. Various barriers prevent the integration of such
tools in early design stages, including the lack of design
practice studies, domain expertise and commonly time-
consuming simulation engines. Another important barrier for
the integration of diverse sets of key performance indicators
in early design stages is the lack of interoperability in
simulation and performance feedback tools [2]. In our study
we demonstrate a method on how the interoperability and
computational cost barriers can be reduced, using an
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integrated simulation and analysis approach based on
machine learning and generative design.
The integration of different key environmental performance
indicators for urban design in an integrated and interoperable
software environment has been shown to have a significant
advantage as a digital workflow to achieve higher
environmental performance [3]. Generative design has also
been used widely for optimizing urban form scenarios, using
a variety of environmental simulations as performance
indicators [4]. In our study the aim is to include a variable set
of KPIs, including not only environmental performance, but
also accessibility and other spatial metrics.
Studies that integrate other urban metrics, such as combining
financial and energy goals show the benefit of a more holistic
performance driven approach [5]. In our case, the focus is
both on spatial metrics which are easily computable but also
on environmental metrics, using a deep learning simulation
prediction framework that makes their integration feasible.
The description of the deep learning framework used is out
of the scope of the paper and it is based on previous research
by the authors [6]. The focus on this paper is mostly on the
coupling of the environmental performance prediction with
a number of other spatial performance indicators and within
a generative design framework in order to evaluate the
potential of the integration of these diverse performance
goals and metrics.
3. METHODOLOGY
3.1. Overview
The approach followed here can be described by the concept
of Cognitive Design Computing [7]. which emphasizes the
role of human designers within a computer-augmented
design process. For instance, in our case, the designer guides
the optimization and design space exploration by both
defining design-altering parameters as well as the fitness
objectives, each generated solution is evaluated upon. The
automatized generation of thousands of solutions provides
an overview of what is possible for a project area - in terms
of its spatial configuration and performance. Next, the
computed solutions can both be used for inspiration and as a
benchmark of design proposals manually created by the
designers. Due to its high level of flexibility and wide
distribution among practitioners, Rhino/Grasshopper was
chosen as the central platform for the case-study. Its visual
programming interface, along with the large developer
community, makes it easy to adapt and extend models by
additional analytical modules. However, next to these
benefits, Grasshopper comes with the disadvantage of being
computationally relatively inefficient and slow, which is
especially problematic for applications of multi-criteria EO.
Therefore, we implemented several parts of the framework
in python scripts, some of which run as external processes
parallel to grasshopper.
Below, the framework used for our case-study is presented
step by step. We start by briefly describing the generative
module and it’s parameters, followed by an introduction of
our analytical models and the performance metrics employed
for the optimization module.
3.2. Framework
Our design pipeline consists of three basic modules: the
parametric generation of a design solution, its quantitative
evaluation, and a genetic optimization engine (see Fig. 1).
All parts are connected through a recursive loop, in which
the optimization algorithm systematically adjusts the
parameters of the generator intending to maximize the
performance of a design. Below each module is briefly
described.
3.3. Generation
The generative part of the model was designed to be able to
generate street-networks, blocks and basic building volumes
resembling an open perimeter block typology. For the street
network and block generation, the plugin DecodingSpaces
was used [8].
3.4. Evaluation
Accessibility: The model for accessibility-related analysis is
based on a weighted, multi-modal graph network that is
combined with an extended version of the Huff-model (a
gravitational model, see [9] and [10]), which stochastically
distributes trips. It enables to simulate interaction effects
between land-uses, associated sociodemographic properties,
and points of interest through a transportation network. This
set-up allows computing performance metrics, especially
Figure 1. Structure of the proposed workflow
Figure 2. The accessibility and mobility model. Data is structured
and aggregated to nodes of a multi-model transportation graph. A
gravitational model stochastically distributes trips between defined
origins and destinations.
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regarding mobility and accessibility-related measures. More
details on this module can be found in [11]. Fig 2 provides
an overview of the structure.
Microclimate: For analyzing solar radiation and wind flows,
we employ a novel, deep learning (DL) based method
developed by [6], and based on [12]. It allows reducing
computation time by the order of several magnitudes, thus
making it feasible to include microclimate related indicators
into iterative planning and optimization processes. In the first
step a DL model is trained with thousands of image pairs of
building heightmaps (Fig. 3, A) and analysis results gained
through traditional simulations (B), in this case conducted
throughout six months using the grasshopper plugin
Ladybug [13] and openFOAM [14]. A trained model can
then be employed to predict simulation results based on
building heightmaps. While the preparation of training data
took months, and the training a day, a pre-trained model
makes predictions in milliseconds.
3.5. Optimization
For the optimization process, we use the Octopus EO engine
[15], a plugin available for grasshopper. It allows us to take
multiple fitness objectives into account and provides a basic
Interface to analyze results. Especially for the class of
complex problems, urban design is typically part of, where
different goals are not directly comparable with one another,
multi-criteria optimization appears to be the preferable
approach to take. Instead of weighting and subsuming all
indicators into one aggregated fitness function, multi-criteria
optimizers are useful in finding solutions that potentially
work well as compromises between all fitness objectives.
This implies that the algorithm cannot return a single best
solution but collections of relatively well-performing
solutions a designer hast to choose from or can be inspired
by in the end.
3.6. Case Study
To try our framework, we test an urban design intervention
in Vienna. The hypothetical project covers roughly the size
of six typical blocks. However, the area of analysis is much
larger, covering more than four square kilometers of the
surrounding context. In this way, interaction with, and
impact on the whole area can be included in the analysis. Fig.
4 gives an overview of the site. The newly generated blocks
are highlighted in black while the metric reachable people
within ten min. walking is visualized on the surrounding
building geometries on a gradient from red (low) to green
(high). Next to building volumes, a street network (street
center lines) is modeled for the context.
In order to define both parameter and evaluation criteria of
the parametric model, we first need a scenario to base our
decisions on. The new quarter should:
• Provide around 600 residential units
• Promote walkability of the whole area, act as a new local
center and be well integrated in its surrounding quarters
Along with the construction of the quarter itself, an
additional pedestrian railway overpass shall be added to
improve connectivity between areas on both sides and to the
new center. As we are in an early design stage, it is sufficient
to generate a basic street network, block arrangement,
building volumes, and switch between several possible
locations for the bridge
Figure 3. In and outputs of the deep learning based prediction of
solar radiation (top) and wind flows (bottom). Based on the
building height map (A) the model estimates the results for solar
radiation and wind speeds (C). B shows results of the physical
simulation using ladybug and openfoam.
Figure 4. The Case-study area with context. Generated buildings are highlighted in black.
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3.7. The Parametric Model
Foremost, we must set up the parametric model that shall
help us to explore the design space within the project. It is
important to find a compromise between the number of
Based on the scenario, our generative module consists of
three parts that are described below and illustrated in Fig. 5
Street network and Blocks: For the parametric generation of the
streets and blocks, we employ components from the
DecodingSpaces toolbox. The only parameter is the rotation angle
of a guiding street segment in the middle of our project site. The
network generation starts from this segment and tries to connect to
incoming surrounding streets (see the first image in Fig. 5). We
chose this approach as the orientation of the street network has an
impact on both pedestrian flows as well as thermal comfort (see
below). For the sake of computational efficiency, the number
possible angles to choose from is reduced to forty (40 parameter).
Bridge: Four possible locations for another railway overpass are
preselected (4 parameter).
Buildings: We chose an open perimeter block typology for the
building generation. A custom definition creates multiple buildings
along the block perimeter. Each building can vary in size and height
while maintaining the same overall GFA. Each Block has five
buildings on average and five height configurations to choose from
(150 parameter).
3.8. Evaluation Criteria and Fitness Objectives
The next step is to translate the stated quantitative and
normative goals into a strategy we can act upon. measurable
performance metrics the computational model can work
with. Additionally, we also need to think about which
parameters of the generative module do impact the proposed
indicators. Fig. 6 gives an overview of this process for the
presented scenario.
Based on these indicators, we started to define our final
fitness objectives for the EO. The results are shown in Fig.
7. It is noteworthy that we combined both simulation
modules employed in this example, for instance, by
weighting the value computed for thermal comfort in streets
with the estimated pedestrian frequency.
3.9. Optimization
Having everything setup, the optimization process can be
started. As mentioned above, we use the Octopus multi-
criteria solver. Figure 8 shows snapshots taken during the
optimization process. One complete iteration, including the
generation of a solution and its evaluation through the
presented indicator system, took around three and a half
seconds. This allows to quickly gain an overview of the
solution and performance space of a design problem.
Figure 5. Parameter of the generative model. From left to right: Angle of central street segment, bridge location, building volumes.
Figure 7. Defining fitness objectives.
Figure 6. From a design vision to measurable performance
metrics.
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4. RESULTS
We stopped the optimization process after two hours and
approx. 2000 iterations, as we saw clear patterns of
convergence. Figure 8 provides an overview of the Octopus
interface (A), several solutions computed during the process
(B) and the trajectory of fitness objectives throughout the
optimization process (C). Regarding the explored solution
and performance space, all selected fitness objectives
showed significant variance (Fig. 8, C) indicating their
relevance. Especially visitor potential to the new quarter
showed a high variance (computed by the addition of the
indicator footfall through quarter and visitor potential to
quarter) with values ranging from 1900 to 3738. The
observed values in avg. difference to target temperature
varied between 1.8 and 3.1 °C, while the share of dangerous
areas measured by the Lawson Wind Comfort Criteria varied
between 1.76 and 0.1%. All five FO’s showed clear patterns
of convergence. For Instance, better performing solutions
ended up choosing option C for the pedestrian overpass (see
Fig. 5) and a street orientation connecting most directly to it.
Besides the ability of exploring the generated designs for
factors and parameters that can be attributed to either well or
poorly performing solutions, the generated data also allows
to put new variants into perspective. For instance, manually
designed ones (see Fig. 9). Having the design space explored,
Figure 8. Results of the optimization process. Interface (A), several generated solutions (B) and trajectory of the fitness objectives
throughout the optimization process (C).
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it is now possible to relate any new design variant to the
previously observed performances potentials. Thus, allowing
for a visualization and comparison on normalized scales,
which greatly increases readability. Since, the re-
computation of all metrics takes about 3.5 seconds, a manual,
yet data-driven and iterative design process is made possible.
5. CONCLUSION
In this paper, we showcased the coupling of multiple
simulation engines into one integrated optimization
framework for the spatial configurations of urban systems.
We combined traditional simulation techniques and spatial
performance indicators (here a graph-based mobility model)
with a novel, deep learning-based approach to simulate the
environmental aspect of spatial configurations (here thermal
comfort, wind speeds). The main goal was to evaluate the
potential of the combination of this diverse set of
performance metrics with generative modeling of a medium-
sized quarter and multi-criteria evolutionary optimization.
First of all, the greatly reduced computation time of
microclimate related metrics made their inclusion possible in
the first place: Instead of minutes, the full calculation of one
design variant (including generation and evaluation) took 4
seconds on average. A short computing time allows us to
quickly explore the solution and performance space of a
design problem. Thus, increasing the potential of similar
workflows to be integrated into practice. Secondly, the
different KPI’s where successfully combined to define more
meaningful fitness objectives compared to an separated
employment of different simulation engines. For instance,
the integration allowed us to weight the indicator thermal
comfort on the street level with the estimated flow of
pedestrians through a street.
Even though a diverse set of fitness objectives was used in
this example, their number and individual design still
resemble a normative and incomplete selection of possible
and important criteria in the quest of measuring and
optimizing the performance of urban morphologies.
As a too large number of fitness objectives render the
application of evolutionary optimization algorithms as
ineffective, the most important FO for a design problem
should be identified and supplied to the EO engine.
Alongside, additional performance indicators can,
nevertheless, be computed for use later on. In this sense, the
optimization is not the end of the design process. Rather the
results gained can be regarded as an exploration of the design
space and a pre-selection of solutions that need be further
analyzed in detail.
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