Generative Design - Lake Flato R&D

generative design Adam Heisserer WELL AP

Research and Development Projects 2016 - 2017

PROJECTS

2018 - 2019

NEUROSCIENCE Neuroscience and Architecture Margaret Sledge

GENERATIVE DESIGN Site-Responsive Prototyping Adam Heisserer

BASELINE 311 Understanding How We Work Anne Herndon, Estefania Barajas

RADIANT COOLING Radical Comprehension Through Experience Sam Rusek

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TEAM

2018- 2019

R&D CORE TEAM Heather Holdridge, EIT, Associate AIA, LEED Fellow Bill Aylor, AIA Bob Harris, FAIA, LEED Fellow Lyanne Guarecuco Steven Campbell Margaret Sledge, AIA, LEED AP BD+C Adam Heisserer, WELL AP Anne Herndon, RA, LEED AP Estefania Barajas Sam Rusek, Associate AIA Tenna Florian, AIA, LEED AP BD+C Corey Squire, AIA, LEED AP O+M Ryan Yaden, AIA, LEED AP BD+C Michael Britt, AIA, LEED AP BD+C Graham Beach, AIA Darby Prendergast, WELL AP

SELECTION COMMITTEE Heather Holdridge, EIT, Associate AIA, LEED Fellow Bill Aylor, AIA Bob Harris, FAIA, LEED Fellow Lyanne Guarecuco Steven Campbell

CONTRIBUTORS Margaret Sledge, AIA, LEED AP BD+C Adam Heisserer, WELL AP Anne Herndon, RA, LEED AP Estefania Barajas Sam Rusek, Associate AIA

EDITORIAL TEAM Adam Heisserer, WELL AP Katelyn Sector Lyanne Guarecuco Hillary Starkey

GENERATIVE DESIGN Site-Responsive Massing Options Adam Heisserer

2018-2019

Can we use generative design tools to create building massing options that perform well in their context?

ABSTRACT This project explores how designers might use generative design tools to create site-responsive building massing options. A series of tools were created on the Rhino/Grasshopper platform that use optimization algorithms to generate and evaluate several massing options and program layouts based on specified performance criteria. This process can be used to formalize design constraints and performance requirements, as well as generate ideas parallel to the manual design process. This is an exploratory step towards taking on a more collaborative and mutual beneficial relationship with computation as a supplement to design.

KEYWORDS Optimization algorithms, site analysis, generative spatial layout.

INTRODUCTION: DESIGN AS A SYSTEM An architectural model can either be thought of as a manually produced object or as a system governed by parameters and performance. Thinking of a model as a system allows the designer to spend less time manually modeling changes, and more time exploring design options. Whether they occur in nature or in architecture, all systems are defined by parameters and driven by performance. Design parameters such as the pitch of a roof or the spacing of columns dictate the form of the building, which in turn affects the performance.

If these parameters and performance criteria are measurable and described numerically, architects can utilize computers to generate several more design

options than they could produce otherwise. This process is not about conceding authorship to the computer, but about allowing the computer to do the tedious heavy lifting of modeling and calculation while the designer spends more time orchestrating the broader design process.

RESEARCH SUMMARY This project is a series of parametric design tools that can be used sequentially or independently at any given point during the design process. These parametric design tools are primarily Grasshopper scripts used to manipulate geometry in Rhino. Each tool uses an optimization algorithm to optimize certain aspects of the design.

Systems described in terms of parameters and performance criteria. Lake Flato’s Big Bend Fossil Discovery Exhibit, and ocotillo plants.

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Original aerial image of the site.

Estimated locations of trees automatically tagged.

Estimated locations of grass automatically tagged.

Estimated locations of roads and paths automatically tagged.

SITE LABELING The first tool is a script that takes an aerial photo of a site, and automatically tags features of the site.

Based on color and pattern, the script can identify the location of trees, grass, roads, paths, existing buildings, and other data that is relevant to the site design. The user first uploads a scaled aerial photo of their site, in this case, a Google Earth image with an eye altitude of 1 kilometer. The example site shown is a randomly selected rural location outside of San Antonio. The script tags probable locations of all relevant objects. This process is most successful when each object (such as trees, roads, or buildings) is a uniform color. If the result is not accurate, there is an option for the user to override the location tags and manually tag the site in Rhino.

The importance of this initial step is that the site is embedded with data about the location of each type of object or surface. This creates a data-rich environment that will be used later for evaluating how the generated designs perform in their context.

Generative tools are most successful when they are not placeless, but built to consider the context of the building.

POTENTIAL OF SITE SCANNING The data-tagging of the site can be performed with any method. The ultimate objective is that the site is described as a point cloud with a universal format, such as: (object type, x position, y position, and z position). The benefit of a universal format for describing site data is that the method for 7

collecting data is interchangeable, and the site data only needs to be as detailed as is necessary. Tagged point clouds can be any degree of density, from a very thin point cloud that was tagged manually, to a very dense point cloud that was collected through on-site laser scanning or photogrammetry. The following image is a dense point

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cloud of the Pearl District in San Antonio, collected with on-site drone photography that was converted into a point cloud with COLMAP, a general-purpose Structure-fromMotion software. The more detailed the site analysis is, the more refined the generative process can be.

Dense point cloud of the Pearl Discrict in San Antonio. This site information was captured from a drone scan and reconstructed with photogrammetry software.

GENERATING A FOOTPRINT After the site has been described in terms of usable data, the designer can begin generating building masses. For each phase of design, the design should be described as a concise number of parameters. In this case, simple building masses are generated with nine parameters. The chosen topology is of two bars of equal width. The parameters include: the X and Y position of the center of the building on the site (X,Y), the length of each of the four ends of the bars (A,B,C,D), the rotation of both bars (R1,R2), and the offset distance between the center of the bars (O). By adjusting these nine

Four different building footprint options, all generated with the same typolgy.

parameters within a defined range, limitless design options can be generated.

With a generative design method, defining the constraints of the system becomes a critical task.

In this case, the human designer’s contribution is to define the typology of the entire system, or to consider the range of all potential solutions. Thoughtful authorship of the possible range of each parameter will influence the success of whatever options are generated.

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EVALUATING THE FOOTPRINT The next step is to create a process for evaluating the performance of the masses being generated. This part of the process is a set of Grasshopper scripts that take a given building mass and evaluate the design in terms of useful space-making, building area, perimeter to area ratio, solar orientation, access to roads and pathways, quality of views, and obstacle avoidance/topography.

Again, the value of the designer is their ability to write the tests that evaluate performance. This is the most critical step to any generative design process. Without a robust and trustworthy evaluation process, the optimal result wont be meaningful.

Each performance test is done with a different algorithm, for example, the building footprint is evaluated for space-making by creating a grid of test points within a certain radius of the building, calculating the isovist (field of view) of each point, and averaging the results for all points. The footprints that have the highest average of enclosed space receive the highest space-making score.

OPTIMIZATION ALGORITHMS At this point, the design problem has been set up with two critical pieces; numerical design parameters that describe the design, and numerical performance criteria that evaluate the success of each design. A benefit of this groundwork is that the designer can use an optimization algorithm to take full advantage of the computer’s strengths. An optimization algorithm Performance tests for space-making (left) and proximity to roads and paths (right).

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is a cyclical process that tests several solutions until an optimum solution is found. Typically, the first few solutions are selected at random, and the algorithm begins to rationalize why certain sets of parameters performed better than others. After several cycles, the algorithm constructs a predictive model, or a fitness function, of how successful all solutions are across a range of variables. There are several types of optimization algorithms, such as genetic algorithms, simulated annealing, CMA-ES, and RBFOpt. Different types of algorithms have different benefits.

This process used one of two algorithms depending on the type of problem. The RBFOpt algorithm was used in cases in which each iteration took more than a few seconds to run. RBFOpt is best at arriving at a good result with as few iterations as possible. In a daylight model, for example, each iteration may take several seconds or a few minutes to run. In order to find a reasonable solution quickly, the designer may only have time to run about 20 or 30 iterations for each problem.

For cases with a very short iteration time (less than a second) a simulated annealing evolutionary algorithm was used as a second opinion.

A diagram of the optimization algorithm cycle, represented with simplified Grasshopper components. Paramters are selected, a parametric model evaluates the design, and an optimization algorithm takes the resulting performance and strategically selects the next set of variables.

Opossum is a Grasshopper component that uses the RBFOpt algorithm, and Galapagos is a Grasshopper component that uses the Simulated Annealing algorithm. Both have a similar interface.

This is the dashboard for Opossum as the algorithm runs. The user selects whether the goal is to minimize or maximize the result, and which algorithm to use. The graph indicates the bast value achieved after a certain number of iterations.

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MULTIPLE OBJECTIVES AND PENALTY FUNCTIONS The optimization algorithm needs a single number to describe the overall performance of a design.

Because several different objectives are being evaluated at once (eight in this case), these eight performance numbers need to be combined into a single number. Each performance outcome is described on a scale of zero to one. Zero indicates the best possible performance for each of the metrics, and one indicates the worst possible performance. All eight scores are combined as a weighted average, with the most critical criteria being prioritized at the designer’s discretion. This average falls somewhere between zero and one, and the algorithm tries to find a solution as close to zero as possible. This method is an example of a penalty function, in which the algorithm is tasked with minimizing a penalty, and minimizing the distance to a theoretically perfect solution.

There are some optimization algorithms that are more efficient at optimizing design problems with multiple objectives, such as RBFMOpt, or NSGA-II. It becomes difficult to optimize a problem when two many objectives are being prioritized at once.

This is why the designer needs to sequence the design process, for example; deciding on the building placement, building footprint, program layout, and then fenestration in separate rounds of optimization.

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INTERPRETING RESULTS After a certain amount of time, the user can stop the optimization algorithm when enough satisfactory options are available. There is no clear-cut time to stop the algorithm. It is typically stopped when the algorithm is no longer discovering better solutions at a reasonable pace. When the algorithm is done, it logs a record of all results, which includes a list of parameters and a list of performance results.

Parallel coordinate plot for the performance of each design option. Each line represents the performance of a different option with a score for each of the eight different performance tests.

Results from a comparison of six different optimization algorithms solving the same views optimization problem. The x axis indicated number of iterations, and the y axis indicates distance from the best possible solution. In this case, RBFOpt was the most efficient algorithm, arriving at the best solution after about 200 iterations.

A portion of the program spreadsheet that the user provides before the program layout. This includes performance criteria such as ideal light, views, and adjacencies.

Public Potential

Daylight Potential

Quality Views Potential

Before rooms are placed, the foor area is evaluated for its potential for public/private, daylight, and quality views. This data is used later to evaluate each program layout.

PROGRAM LAYOUT The first phase of design was to arrive at a simple massing model strategically placed and oriented on a site. The next phase is to adopt the optimal building mass from the first phase and then consider how the building program might fit into the given mass. The user enters the program requirements into a spreadsheet, which is then accessed in Grasshopper. This spreadsheet includes typical program information such as room names, area, and occupancy, as

well as performative requirements such as public vs. private, daylight, and exterior views.

Before placing any rooms, each point on the footprint is evaluated for its potential for each of these three requirements.

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FLOOR PLAN GENERATION The next script is for creating a rough program layout within the given footprint. Several points are placed evenly within the floor plan, each representing the center point of each room in the program. The performance of the program layout is determined by how well the performance requirements of each room align with the performance potential of the part of the footprint it is assigned to. Each layout is also evaluated by the efficiency of the adjacencies of particular rooms.

For example, a layout with an entry room closer to the

An example result of a program layout, with exact room boundaries being fine tuned.

These room boundaries start to indicate where circulation may occur.

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perimeter will have a lower penalty than if the entry room were in the middle of the footprint. Master beds are incentivized to be close to master baths, porches are close to entries, dining rooms close to kitchens, and so on. These adjacencies are prescribed in the program spreadsheet that the user completes. Similar to the previous steps, the optimization algorithm runs through several iterations until the program is distributed within the footprint in a reasonable way.

After the rooms positions are loosely defined, another script is run to refine their positions and consider where partitions and circulation paths may occur. The room centers are used to create a simple voronoi diagram in which the footprint is partitioned into cells with each point indicating the center of each room. These room centers are adjusted within a certain range, and the cells adjust their boundaries accordingly. In this script, the areas of the rooms are being optimized so that the area of each room closely resembles the specified area in the program.

Public Potential

Daylight Potential

influential factors on this site were solar orientation, daylight autonomy, and view sheds towards a nearby mountain range.

The typology for this massing was three bars of identical width and varying length. Each bar had the option of being designated as one or two stories high. The resulting mass was mostly east-west oriented for ideal solar orientation, with a narrow courtyard allowing daylight into the deepest part of the floor plate, and optimized for views.

Quality Views Potential

APPLICATION TOWARDS BUILDING MASSING As an example of this process applied to a real project in design, this is a massing study for a university building in Utah. This process was run in parallel with our typical design process as a supplementary source of idea generating and evidence-based analysis. The most

Quality views simulation, in which blue lines indicate unobstructed lines of sight towards the most appealing views in the valley, and red lines indicate unobstructed lines of sight towards the least appealing views. A score is generated based on the ratio of good views to bad.

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APPLICATION TOWARDS DAYLIGHT OPTIMIZATION This optimization process can also be applied at the scale of detailing or fenestration. This is an example of a much more concise optimization problem in which the height and depth of both an interior light shelf and an external shade for a few different classrooms in a school in Houston. The performance criteria is an average of continuous daylight autonomy (cDA: the percentage of 16

time the room is above 300 lux) and useful daylight index (UDI: the percentage of time the room is between 200 and 2,000 lux). This criteria is a good balance of getting enough light to work with, while mitigating glare. The results show the best possible configuration of light shelves, regardless of practical constraints like design or cost. A second model was run to find the optimum length of an interior and exterior shelf, assuming both are at a fixed height on the mullion.

CONCLUSION Optimization algorithms are a concept that can be applied to almost any architectural design problem.

It is an extremely valuable practice to be able to consider any problem in terms of parameters and performance. It gives clarity to the whole process, and provides an evidence-based approach that can balance or supplement any traditional design process.

Generative design is not a concession of decision making from human to computer, instead, it is a deliberate reorganizing of tasks and strengths between the two. There is no theoretical limit to the complexity of parametric models, and no limit to the performance evaluation that computers can perform. As authorship of this process becomes more and more accessible to designers, the results of these process will become more and more impressive. It is important for many designers to have at least some understanding of these processes, and the ability to communicate design goals in terms of parameters and performance. This democratizes the wave of design automation that is likely to impact the practice of architecture.

Optimizing Spatial Adjacencies Using Evolutionary Parametric Tools: Using Grasshopper and Galapagos to Analyze, Visualize, and Improve Complex Architectural Programming - Perkins+Will Research Journal Christopher Boon, Portland State University, Corey Griffin, Assoc. AIA, Portland State University, Nicholas Papaefthimious, AIA, LEED AP BD+C, ZGF Architects LLP, Jonah Ross, ZGF Architects LLP, Kip Storey, ZGF Architects LLP

DreamCatcher, MaRS Generative Design, Generative Design for Architectural Space Planning - The Living / Autodesk

Architectural Layout Design Optimization - Jeremy J. Michalek, Ruchi Choudhary and Panos Y. Papalambros Carnegie Mellon University

VOCABULARY Optimization Algorithm - a procedure which is executed iteratively by comparing various solutions until an optimum or a satisfactory solution is found. With the advent of computers, optimization has become a part of computer-aided design activities.

RBFOpt - Radial Basis Function Optimization uses advanced

APPENDIX

machine learning techniques to find good solutions with a small number of function evaluations

RELATED PROJECTS Building energy optimization: An extensive benchmark of global search algorithms - Christoph Waibel, Thomas Wortmann, Ralph Evans, Jan Carmaliet

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