Systemic Formations: Multi-Agent Algorithms for Architecture

Systemic Formations Multi-Agent Simulations for Architecture Richard Hardman Master of Architecture University of Cincinnati

Richard Hardman Chair: Christoph Klemmt Committee: Stephen Slaughter Design Architecture Art and Planning University of Cincinnati Master of Architecture Spring 2019

Systemic Formations Multi-Agent Simulations for Architecture

In Memory of

Richard Hardman Sr. 1943-2018

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Acknowledgment I would like to thank everyone who has contributed to this thesis document. Specifically, I would like to thank my advisor Christoph Klemmt who provided a great amount of knowledge in computational design and provided his own algorithm for this thesis. I would also like to thank Stephen Slaughter for his support and rigorous design feedback. The invaluable feedback from both of these individuals greatly elevated the merit of my work.

I would like to thank my family and friends for their support over the past eight years as I have pursued my design endeavors. To all those who have critiqued , challenged and supported my designs ideas, thank you for your help, in particular Benjamin Blake, Justin Brown, Kevin Goldstein, and Colin Martin.

I have grown as a designer thanks to all of your feedback.

Lastly, I would like to dedicate this paper to my grandfather Richard Hardman, Sr. who provided unceasing support in all my endeavors. His positivity and generosity are greatly missed.

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CONTENT 14 | Introduction 20 | Related

Work

Generative Design Swarm Simulation Growth Simulation Structural Simulation 30 | Methodology

Particle Systems Swarm Simulation Growth Simulation Structural Integration Multi-Agent Algorithm 46 | Case

Study: Installation

Design Intent Unit Design Mold Design Strength Curves Installation Design Case Study Evaluation 68 | Case

Study: Transit Hub

Smart Cities Design Intent Algorithmic Design Train Station Design Case Study Evaluation 88 | Evaluation 94 | Conclusion 97 | Appendix

Related Work Glossary Table of Images Bibliography

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xiii

14

Introduction

15

Introduction

“An environment is missing that integrated representation and simulation-based approaches. It could, for example connect modeling with physicsbased behavior, scripted elements and the generated structure could still communicate as whole to external environment.” 1

‘Thinking’ and ‘doing’ are usually terms that are seen independently of the other. One thinks of a problem in order to find a solution. However, there does not need to be a solution in order to do something. Sometimes it is just for the sake of doing.

The interesting consecution is when the doing is a direct result of the thinking. Now imagine that these could both be done simultaneously and in rapid succession. This understanding of a problem is the ability that algorithmic design affords. Through the process of constructing an installation and designing a building using this computational design method, its usefulness in the future will be assessed to determine its viability as a design method rather than a form generative tool. This thesis attempts to explore the generative design process to develop an integrated use for structural analysis and particle simulations to derive purposeful architectural forms.

Algorithmic design is a logic that can be used for a specific method2. While there are numerous computational design tools, this thesis will focus on the uses of an integrated structural analysis system within agent-based algorithms. In this form of design, agent refer to points in space that are created via a computational algorithm to calculate in a three-dimensional environment through which data can be visualized. This data is the illustration of the agents as calculated by the algorithm. This form of design is unique in that it is able to concurrently analyze and simulate to produce a solution that is not only unique but it was produced as a solution to the criteria provided to the algorithm.

1. Tamke, 2011 2. Kwinter, 2008 3. Reynolds, 1987

16 Introduction

This thesis uses two Agent-based design strategies: swarm and growth. Both of these methods emulate a characteristic found in nature that is utilized for a specific purpose in the simulation. Swarm simulations refer to that of birds or other organisms that self-organize and move with intent3. This type of simulation keeps the agents together and allows them to communicate with one another in order to address a specific

problem as defined by the parameters. The simulation runs numerous calculations and simulates many possible scenarios to generate the most informed outcome. This has an array of use scenarios where the most informed outcome from a problem is desired. Swarm simulations can be used at an urban scale for master planning or at a smaller scale to generate building façades.

The other type of simulation is a growth algorithm where the algorithm mimics the process of cellular division. In this simulation, there are a group of agents that can also be used for problem solving tasks but they are not all necessarily working together to accomplish this. The algorithm allows for the ability to create multiple types of agents. The use of algorithms in design can be applied to urban scale problems or to a facade system. This is all being done in parallel to produce a solution that satisfies criteria from both the building as well as the crowd. The granular and complex input is the inherent strength that agentbased design affords the user to generate a specific solution to a problem using multi-layered analysis and data driven results.

This thesis explores the integration of multiple agent types with specific requirements and constraints and how these can be applied to a building form. Through the incorporation of structural analysis, the growth algorithm will be able to receive real-time feedback on the form as it is generated.

The agents responsible for the shell will also receive feedback from the crowd agents mimicking the movement of people.

This movement drives the deformation and manipulation of the shell. Lastly, the algorithm will calculate the placement of programmatic elements that are predetermined based on the parameters input. By using multiple agent types concurrently, the algorithm is able to process these different influences which will ultimately inform the shape of the shell that is being generated.

As a means of exploration with this type of agent-based design, two case studies were used: a small scale installation and a transit hub. The architectural-installation project was used to test the simulated results to conclude the viability and usefulness of the structural analysis. By constructing the scale model, the simulation was tested to establish if the structural simulation is producing viable results. This determination is important moving into the building design portion of this project that utilizes the same underlying algorithm to design a transit hub. The agent-based simulation will be scaled up to test the 4.  Preisinger, 2014

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novelty of this computational design method as a solution to generative building structure6 using multi-agent design methodology. The analysis will need to perform calculations on the building form in conjunction with the crowd of users moving through the site to create a system that is mindful of the architectural elements such as programming and circulation while also performing structural calculations to ensure that the form is stable.

This thesis looks at an array of scales and applications for agent-based growth algorithms with the incorporation of a structural-analysis system6. From the small scale installation to the transit hub, the algorithm is generating a substantial amount of data to address a defined problem that is only possible through this multi-agent computational design process.

The agents were able create a shell space that is structurally analyzed and drive the form of the building with movement patterns from the crowd agents. The programmatic agents still need a better understanding of programming and adjacencies within a building. The incorporation of multiple agents into the design process produced informed shell design driven by the agents. This aggregation of elements into the algorithm aims to find a use in the complexity of design today.

18 Introduction

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20

Related Work

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Related Work Generative Design The process of generation to meet a certain criteria is what is referred to as generative design. This thesis will focus on particle systems and their usefulness in generative design. A particle system consists of a large number of individually addressable points that are programmed to simulate specific actions. The general point characteristics include velocity and position in three-dimensional space. These points also have the ability to determine and share information with their neighboring points.

This communication between them give them a self-organizing quality. The particle system utilizes an algorithm which loops through all of the points in the system to perform calculations based on the intent for the simulation. This process happens rapidly each second performing the calculations until the cycle is stopped.

Figure 1 generative design adaptation

The use of generative systems, such as the ones described in this thesis, designers have the ability to test an abundance of solutions to a given problem. Particle systems and selforganizing bodies processes through which a problem can be tested through unconventional conceptualizations and working design. This causes a shift in the design process and is a paradigm shift for generative tools5. This contemporary practice integrates novel design solutions to difficult or impossible to achieve scenarios6. In design specific problem solving, genetic algorithms have been used due to their usefulness in solving complex optimization problems7.

The integration of performance-based generative design tools into a traditional work flow allows designers to utilize geometric freedoms that were unrealized. Currently, the geometry which designers create is defined after the creation and there are no further refinements unless manual manipulations are applied. With generative design, the algorithm will continually calculate the fittest outcome as compared to the criteria provided. In theory, this process could continue indefinitely as there are an infinite number of possible solutions. Using this intelligence, designers can leverage the computational power to solve complex problems.

5. Kuhn, 1996 6. McCormack, 2004 7. Renner, 2003

22 Related Work

The architectural design process, in nature, is evolutionary but contrarily computational design is not. A system is traditionally only told what to do in order to find a solution7. By using genetic algorithms, a hierarchy of tasks can be set up in order to evaluate a solution by referring to its fitness or how well it

satisfies the criteria7. Today, discrete particle systems are being used to simulate granular properties of matter for an array of properties such as fluid dynamics, electric fields, and cellular division8.

The use of computational simulations has allowed for the ability to replicate complex particle systems. Fluid dynamics simulate the movement of water particles over free surfaces.

Matthias Müller uses particle simulations to recreate fluid hydrodynamics as they pertain to motion due to interaction with solid surfaces9. In architecture, Darren Chang has used these computational fluid dynamics to simulate the aerodynamic performance of a skyscrapers form in a form-generative evaluative process10. Alisa Andrasek uses particle systems to simulate influences of electromagnetic fields which are then translated to linear elements to construct a series of shell structures11.

The usefulness of generative design is apparent when using performance-based criteria as a benchmark for the algorithm.

Using this system, Kristina Shea was able to use generative tools to develop meaningful geometry through a benchmarking criteria12. Sivam Kirsh explores the increase in usefulness with the combination of multiple algorithm problem solving that each have their own relative goals13. This ideology has been implemented by the automotive industry as a way to enhance the design based on performance standards that must be met14.

This thesis will explore the potential of generative design using design criteria to benchmark the resulting geometry. By using an evaluative process, the results will be tested for viability and usefulness in design.

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Zhu, 2007 Müller, 2003 Chang, 2013 Andrasek, 2007 Shea, 2005 Kirsh, 2011 Bodein, 2013

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Swarm Simulation

Figure 2 bird swarm

This particle system uses swarm agents to simulate the movement of people. There are precedent projects for this type of simulation in which use the swarm agents for design on an urban scale15. The agent-based swarm algorithm is based on the work of Craig Reynolds16. In this structure, the particle system he refers to is described as boids. These are bird-like agents that simulate the characteristics of flocks in the simulation. Reynolds was able to create an algorithm in which the boids are selforganizing16. These early simulations were an initial exploration in three-dimensional movement in space. Reynolds’ model is primarily using ‘flying’ particles but he talks about the term banking which refers to an agents ability to move in an applicate direction16.

Alisa Andrasek researches swarm algorithm in an attempt to produce what she refers to as “high resolution architecture.”17 Through swarm algorithms, Andrasek is able to inform formal characteristics through data.. Roland Snooks explores using swarm algorithms for control over the generative process through the use of specific design-oriented parameters18. Satoru Sugihara uses the self-organizing characteristics to generate structures using cabling19. The swarm algorithm serves as a form of optimization for these installations.

Swarm algorithms have implications on a range of scales. At an urban scale, swarms simulate the development of an urban system identifying major urban elements while generating path systems15. Designers are using these algorithms to solve planning problems such as the impact of climate change. Rob Roggema developed a swarm algorithm to evaluate spatial planning in response to the unexpected impact of climate change and use the flexibility in designing through generative design tools20.

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Snooks, 2014 Reynolds, 1987 Andrasek, 2012 Snooks, 2014 Sugihara, 2014 Roggema, 2015 Coates, 2000 Tsiliakos, 2012

24 Related Work

At a building scale, swarm algorithms can be used to generate architectural forms. Paul Coates uses swarm algorithms to learn and adapt to environmental. The swarm interacts with the site conditions learning and differentiating spacial patterns to form spatial awareness21. These algorithms become more intelligent when multiple agents systems are merged into a single system to generate a coherent result based on a unified goal. Marios Tsiliakos uses this multi-agent approach on a building system. By using the swarm algorithms, the system is able to understand the material organizations

necessary for construction therefore generating self-organizing design outputs22.

Agent-based simulations are able to not only inform the formal characteristics of a building but can be used to improve the fabrication process by using the inherent self-organizing properties of swarm simulations to create a digital fabrication system. Evangelos Pantazis and David Gerber worked on an algorithmic optimization for architectural design incorporating not only structural form finding but a digital fabrication process23.

Using the material properties, the assembly method was able to be simulated which enables the emergence of patterns to influence the work flow.

The application of self-organizing bodies has the potential to be used for more than form-finding. These project mentioned have pushed the research beyond the traditional application and this thesis will continue to explore the usefulness of multi-agent systems in design.

Figure 3  Kokkugia swarm urbanism

23.  Pantazis, 2014

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Growth Simulation The simulation that mimics the division of biological cellular materials is a growth particle system. The algorithm synthesizes the behavior of these organisms to generate a growth-based simulation24. The individual agents, or points, are given properties and behaviors which are a determinate factor in the way the algorithm functions. A growth algorithm is reliant on the basic cellular principle that biological organisms grow or reproduce through division.

Figure 4 Neri Oxman growth generated form

Neri Oxman creates genetically grown structures inspired by natural growth behaviors25. These produce complex shapes and patterns synthesized from the underlying growth algorithm.

This nature inspired design approach studies the principles of biomimesis to take inspiration from the various patterns of bacterial growth. Saleh Kalantari uses growth algorithms to generate bacterial-inspired growth models in the search of new fabrication processes as well as unique spatial structures derived from these growth simulations26.

Andy Lomas uses growth algorithms to generate abstract shapes through cellular division27. These forms are generated through the interconnected cells as well as the rules for accumulated nutrient networks. David Gerber works with structural systems and the integration of analytic processes, such as environmental and structural, to create informed generative design tools28. Through Karamba and Ladybug, Gerber is able to perform analysis of generated forms based on criteria with structural and environmental data. Alisa Andrasek focuses on the increased resolution with the computational strength available. She references a ‘resilient and adaptive’ architecture through the use of growth algorithms. The underlying algorithm for this thesis is based off of the work of Christoph Klemmt. Klemmt explores the venation structure through growth simulations29.

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Broughton, 1997 Oxman, 2015 Kalantari, 2017 Lomas, 2014 Gerber, 2016 Klemmt, 2016 Tamke, 2013

26 Related Work

Particle systems allow for dynamic adaption to the environment being simulated. Martin Tamke and David Stasiuk use growth simulations to create an installation based on material performance and behavioral constraints within the algorithm. These integrations of digital simulation techniques provide a hierarchical generative design approach30. These precedent projects provide a fundamental building system for this thesis. The algorithm mimics the growth of structures made from thousands of individual agents that are relative to

that of the cells in the scientific experiment where each agent is unique and therefore can be addressed individually. This thesis will look to incorporate the genetic mimicry process with an analytical process, such as structural simulations, to generate a form that is conscientious of building elements and provides an incorporated, iterative design process.

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Structural Simulation The integration of structural design and architecture is imperative. Using generative design, it is possible to create performance-based design that must be satisfied by the structural simulation. This unique relationship leads to formal implications in the architecture that are inherent because of the structural requirements. The balance between performance and design is the interesting intersection of this integration. Sean Ahlquist uses this integrated process to produce a lightweight structure using cables. By defining structural limitations, a highly articulated system is derived based on these parameters31.

Particle systems allow for loop feedback within a system.

This work flow allows structural analysis to be incorporated through progressive iterations in the simulation. Jane Burry and Martin Tamke use particle systems to simulate a small network of structural components that allow for a high level of complexity in the overlapping and interrelated components.

By using a particle system to simulate these connections, the network is able to adapt and provide real time performance feedback which can be analyzed and corrected32. Designing with physic-based algorithms, Sean Ahlquist and Achim Menges use computational processes to simulate physical and material behaviors to create an interrelated architectural system of material, spatial and fabrication properties.

Figure 5 Zaha Hadid Ciab Pavilion

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Ahlquist & Menges, 2011 Burry & Tamke, 2012 Ahlquist & Menges, 2012 Ciab Pavilion, 2013 Baharlou, 2013

28 Related Work

The use of existing structural analysis software allows for integration with the digital design tools. The plug-in Karamba offers a variety of structural analysis tools for geometry. By implementing these tools in generative design, the algorithm is able to use the analysis provided by Karamba to develop complex structural systems that inform the design and fabrication techniques. Using Karamba, Zaha Hadid Architects were able to develop a hypar shell that is highly structural while expressing the sculptural nature. Through computational design, the structural system was able to analyze the form and create a secondary tectonic logic allowing for both structural efficiency as well as construction techniques34. Ehsan Baharlou and Achim Menges propose using agent-based systems to develop a generative system in which performative design creates a computational framework35.

Karamba is useful in that it provides real-time information on models through the software plug-in. Generally, the models analyzed in this software are static or changing at a very slow

pace. The usefulness of this structural analysis is apparent with the introduction of generative design and the process of performing these structural calculations on an ever-changing model. These calculations can provide feedback to the algorithm and create informed design decisions.

This thesis will explore the introduction of real time structural analysis into a generative design process to evaluate the application of performance driven forms in design. Using a multiagent system, the computational processes will use an iterative feedback loop in order to generate responsive geometry.

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Methodology

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Methodology Particle Systems Generative computational tools, such as the ones in this thesis, use algorithms to simulate particle movement. The particle system is comprised of individually addressable agents each with its own properties. The computational tools are able to perform these algorithmic calculations to develop the simulations discussed. The algorithms are constructed using Java programming language. The primary use for Java is to create the framework for the simulation and allows for manipulation of global variables. Once the algorithm has been developed and is ready for simulation, the code is compiled and sent to Processing. This is a graphical programming interface that allows the Java code to be displayed in a three dimensional environment. In Processing, all of the graphics are controlled for the simulation. These include the colors, lines, and shapes that are seen.

Fundamentally, both the swarm and growth algorithms work the same. An agent, or point in space, is constructed.

During this process, the agent is assigned properties that dictate constraints within the algorithm. Once the agent has been constructed, it can be placed in space and the simulation loop begins to run through either the growth or swarm algorithm.

32 Methodology

Swarm Simulation This process mimics the natural movements of flocks through what Reynolds refers to as ‘self-organizing bodies.36’ These agents are able to communicate with one another in order to form a cohesive movement pattern which is used in this thesis to simulate the movement of people.

While the swarm simulation still uses ‘agents’, as described in the last section, the swarm based agents are referred to as boids. This term is referring to the agents as ‘bird-like.’ The boid is constructed with similar properties to the growth agents: //BOID CONSTRUCTOR Boid(Position, Max Speed, Max Velocity) Acceleration = new Vector; Angle = random; Velocity = new Vector; Position = pos; Max Speed = speed; Max Force = force;

The constructor creates a boid with the global properties set in the algorithm. The new parameters that differ from the growth are the ‘max force’ and ‘max speed.’ The speed parameter controls the maximum overall speed that the agent can obtain. The force parameter is the constraint that the neighbor imposes on the boid. This is the first instance of the self-organizing aspect for the boids. The neighbor has a velocity in a specific direction which influences the movement of the neighbor. This force parameter allows for the ability to control the amount of influence that the neighbor’s movement has.

Once the global properties are assigned to the boids, they can then be constructed. As in agent creation, the boids are given random coordinates in space and then a number of them are placed. //BOIDS randomSeed(2); for (boid(0)) to (boid(249)) pos = random(85.0,90.0), random(100.0, 105.0); Boid.add fixedZ;

Similarly to the agents, boids have the ability to move in three-dimensions as a bird does. However, for this project, the boids are emulating human characteristics which confines them to two-dimensional movement on the ground plane. While

36. Reynolds, 1970

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the global parameters can control the maximum speed and force, there are individual parameters for each boid that provide a refined control for the self-organizing bodies. As Reynolds describes, there are three parameters that will drive the movement of the swarm: separation, alignment, and cohesions. //SWARM MOVEMENT sep = Seperation(Boid); ali = Align(Boid); coh = Cohesion(Boid); sep.scale(1.5f); ali.scale(1.0f); coh.scale(0.2f); ApplyForces();

These three parameters influence the communication between the neighboring boids. In order to create a hierarchy for these, a scale is applied to each the forces. As shown in the example above, the separation has the highest strength while the cohesion has the lowest. This allows for the ability to emphasize certain properties of swarms as well as reduce undesirable ones as well.

The separation parameter gives the ability to control the distance between the agents. The higher the value, the more separation there is from the neighbors. The alignment parameter controls the directionality of the boids. Each of them have a vector directionality that is then communicated with the neighbor. The higher the number, the more likely that the neighbor will match the vector direction. The last parameter is the cohesion of the boids as a swarm. This is the likelihood that a boid will make its own decision rather than follow the neighbors. In the example above this is the lowest to allow for some variability in the movement of the swarm and allow them to break away and form smaller swarms.

The communication between the boids happens as the algorithm loops through the calculations each cycle. The boids are all calculated and then the information from these calculations are passed to the neighboring boids. This sharing of information is what gives the boids the ability to self-organize.

34 Methodology

Growth Simulation There is an abundance of computational design strategies all using data driven processes for a specific purpose. This thesis will focusing on the use of agent-based design, specifically growth and swarm simulation. While fundamentally the both mimic characteristics of natural processes, they function differently. The growth algorithm emulates cellular division while the swarm algorithm mimics flocking. To understand these algorithms, this will breakdown the workings of the base script37 that is processing the two simulations.

The fundamental idea of agent-based design is using an Agent to simulate a specific behavior or to solve a singular problem. In the algorithm, an agent is referring to a point in threedimensional space. Each of the agents have specific properties that are unique to that specific point. In order to store these properties within the agent, it must be constructed: // AGENT CONSTRUCTOR Agent(Position, Velocity, Type, List) Position = pos; Type = type; Age = age; List = agents; Velocity = vel; Normal = new Vector; Displacement = new Vector;

Figure 6  growth simulation

This constructor creates an agent and assigns it specific properties for each of the categories listed. The construction of a point must start with coordinates so the agent is assigned to a position in three-dimensional space. The point is then assigned a random vector or movement direction once constructed. This vector allows the point to move within the virtual environment.

Finally, the point is assigned an age of zero. This will be used by the algorithm to slow the movement of the agent as it grows older in cellular division. Each agent is also assigned a type that represents its function that allows for categorization later. The agent is placed in a list so it can be referenced in the future. At the time of construction, temporary vectors are assigned for both the normal and the displacement values. These will both be calculated further on in the algorithm.

Once the agent has been given a list of properties during the construction, it is now possible to create multiple agents using this specific constructor method: 37.  Klemmt, 2016

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Figure 7  initial setup, growth algorithm

// AGENTS randomSeed(0); for (agents(0)) to (agents(9)) pos = random(-5.0, 5.0), random(-5.0, 5.0), random(0.0, 5.0); vel = random(-3.0, 3.0), random(-3.0, 3.0), random(0.0, 2.0); Agent a = new Agent(pos, vel, type 1, agents)

The algorithm creates these ten agents (agents(0) to agent(9)) that are placed randomly within the limits in this portion of the code. In the example, the agent is placed and assigned a velocity in the vector limits given. The vector is a line that is formed from the position of the agent to the position given by the velocity. The distance between the agent and the velocity point determines the strength of the vector on the agent. All of these agents will be stored in the Agent list so they can be referenced.

After the initial ten agents are created, the algorithm can now start the process of division. This is done through a set of functions that perform specific calculations to determine the properties of the agents produced during the division:

36 Methodology

INITIALIZE agents List CONSTRUCT new Agent for division BEGIN Loop move() findNeighbors();

normal = findNormals();

acceleration acceleration acceleration acceleration

= = = =

add(unary) add(attractors) add(voxel) add(dispalcement)

if (Agent age > minimum age) Agent a = new Agent(pos, vel, type, agentsNew) age = 0; update() vel = add if (Agent vel age = age

acceleration; age > 250) = scale velocity; + 1;

END Loop

The pseudocode above shows the order in which the algorithm calculates. It first creates a list to put the agents in, then constructs the initial agents before running each agent in the list through the loop to determine if it should divide the point into two new agents. The loop function is run on every point in the agent list.

During the division of the agent, the algorithm first runs a function to locate the neighbors of a cell. It is an important part of the script to have an understanding of the neighboring cells for calculations. The number of neighbors as well as their proximity to the current point are part of the criteria that must be met for an agent to divide. These variables are able to be set and give different spacing and growth rates depending on the variables.

The next function analyzes the agent in order to determine the normal for the point or in other words, the up/down facing direction of the point.

Figure 8  neighbors located within radius of agent

As described earlier, agents receive a velocity parameter during their construction. This is a constant movement in a direction set by the vector. However, during calculations, accelerations are calculated and have an effect on the velocity for the agent. In the example above, there are a number of accelerations being calculated that will influence the velocity of the agent. All of these variables are applied prior to running the script and can be input by the user. The first acceleration being

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Figure 9  agent-based algorithm diagram

Figure 10  agent characteristics

38 Methodology

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applied is a unary force. This is simply a force in a direction such as gravity. The next is an attractor force which is a point or object that influences the nearby agents to move towards it by shifting their vectors.

Figure 11  attraction point

The next acceleration is a voxel grid movement. When the algorithm is running, the agents are arranged in threedimensional space with a variety of different point coordinates.

For example, an agent may have the coordinates (3.19, 5.74, 8.48). The agents do not have rounded fractional digits which means they do not have common spacing. Within the script, there is the ability to apply a voxelization to the points.

This means that each point will be snapped to its nearest grid intersection in a three-dimensional space. In the agent example, this means that the point coordinates will be rounded in order to align it with the other agents resulting in the coordinates (3,6,8).

This voxel acceleration is the movement of these points to their grid position.

The last acceleration that is applied is the displacement force. As part of this thesis project, adding a structural analysis element to the algorithm was important as an exploration of the potential uses for the script. Within the agent-based simulation, Karamba3D is running structural calculations.

Karamba3D is an open-source structural analysis platform.

This analysis is available as a plug-in for other programs such as Rhinoceros and Grasshopper. In the case of this thesis, the source files were taken and incorporated into the algorithm as another function in the script. // KARAMBA getDisplacement() Material

double E = 210000;

double G = 80000;

double gamma = 78.5;

double A = 0.05;

double Iyy = 0.00001;

double Izz = 0.00001;

double Ipp = 0.003;

double ky = 0.0;

double kz = 0.0;

crosec = new CroSec(A,Iyy,Izz,Ipp,ky,kz); Agents a = new Node; for (Agent list)

agents = node; for (node(0) && node(1))

beam = nodePair; if (z < 1)

node = support;

40 Methodology

This pseudocode is an example of the displacement function for the agent. The function takes the material characteristics input by the user and calculates a structural system based on these properties. Earlier in the script when the neighbors were defined, the agents were sorted by their proximity with the specified agent. In the structural function, these sorted agents are used to form a framework for calculations. The initial agent as well as its closest neighbor from the stored list create a pair of points or a node pair. Between this pair of points a line is drawn which represents a beam. The beam is given the properties of the input material which then allows the function to run a structural calculation between these two points. The amount of displacement in the beam is then stored for use in the acceleration force.

Figure 12â&#x20AC;&#x192; closest neighbor diagram

The acceleration applied to the agent based on this displacement has a strength curve which means those points with a larger displacement will result in a larger acceleration while the smaller values will have much less acceleration.

// DISPLACEMENT FORCE forceDisplacement() magnitude = displacement; Exponential strength = ((0.2(magnitude) - 0.8) + 1)1.1 acceleration = add(strength);

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The displacement function uses an exponential strength curve in order to apply the correct acceleration to the agents.

The effect of this displacement acceleration causes agents with large structural displacement to be forced towards the ground plane and thus reduce the cantilever in the structure. This displacement function is run as a loop through which each point is analyzed and assigned a strength based on the displacement calculation.

Now that all of the accelerations have been applied, the agent division takes place if the age criteria is met. In order to allow agents enough time to move after division, a minimum age is set. The division of the agent creates two categories: parent and child. The parent is the agent that is being divided which creates two children. The child agent takes the properties of the parent before dividing and becoming an agent.

Figure 13â&#x20AC;&#x192; agent division diagram

42 Methodology

Once all of these functions take place, there is an update at the end of the loop before moving to the next agent. In this update() function, the accelerations calculated earlier are applied to the agent and then given an age of one. This concludes one loop through the growth algorithm. As stated earlier, this is a loop which means that each point in the agent list is sent through this process continuously until the simulation is stopped. The process described in this section is for a growth algorithm but there are similar principals that are applicable to the swarm algorithm as well.

Structural Integration Taking the generic growth algorithm, the primary goal was to incorporate a structural analysis software that allows the algorithm to perform real-time structural evaluations. This enables the algorithm to make informed design decisions based off of the feedback from the structural calculations.

As described in the earlier section, the growth algorithm is controlled by a set of parameters. One of these parameters is the displacement value from the Karamba structural analysis. In the loop cycle, the structural analysis calculates the displacement load. This load calculation is done by creating a beam between the agent being calculated and its closest neighbor. This beam allows a gravitational load to be applied to calculate the displacement value. As part of the structural calculation, the material properties can be applied to the algorithm giving the agents an understanding of the material that will be used to calculate the loads. This sets a threshold for which the agents must meet to be within structural tolerance.

In order to target the formation of cantilevers within the simulation, the agents with the highest displacement value are given a correction to reduce the load. This reduction in displacement is done by applying a downward force to the agents which are suspected to be cantilevering. This downward correction causes the agents to move closer to the ground to resolve the structural system. The higher the displacement value the larger the downward correction resulting in a resolved structural system. Once the agents each the ground, they are then assigned as a structural support in the system which stops them from dividing in the growth algorithm.

These calculations are done for each of the agent on every loop through the algorithm. This creates an informed structural system that is constantly correcting its own form based on the feedback received from the structural system. This type of structural programming was what resulted in the shell forms that are used throughout this thesis. When additional agentbased systems are applied to the shell, the structural algorithm maintains its hierarchy and resolves the form regardless of the other influences.

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Multi-Agent Algorithm The interesting convergence of agent-based design is the combination of growth and swarm algorithms running concurrently to one another. For this thesis, the growth algorithm was used to simulate the shell form as well as the programmatic layouts. The shell begins as a set of points and then is grown while applying structural calculations to create a stable structure. For the programmatic elements, the algorithm is used as a way of aggregating the program pieces for the building based off of trigger criteria within the algorithm.

The swarm algorithm is used to mimic the movement of people through the site. This allows for a mapping of movement as well as an understanding of entrance into the building. The multi-agent algorithm is crucial when looking at the merging between the two types. The power of computational design is apparent when giving all of the agents the capacity to inform one another.

The simulations starts by mapping the movement of the swarm agents for one-hundred frames before starting the growth of the shell. This allows time for the swarms to form and begin to navigate the site. Once the growth of the shell starts, the swarm boids influence the formation of the shell. A boid within a given radius of a shell agent will share its heading vector with the shell. This will cause the shell to deform from the movement of the swarm. The swarm boids also communicate with the programmatic growth agents to define placement based on the boidsâ&#x20AC;&#x2122; path. As all of these calculations are taking place, the shell agents are influencing the placement of program based on the structural anchor points for the shell. This network of behavior manipulation is only possible through computational design due to the amount of calculations necessary. The layering of this information affords for an informed design strategy based on the given parameters such as the shell, crowd, and programmatic elements described.

44 Methodology

45

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Case Study: Installation

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Case Study: Installation Design Intent As an initial exploration for the agent-based design process described in earlier sections, this case study uses a scale model of an architectural installation to understand the potential successes and failures for the simulation. This scale model was act as a proof-of-concept for the algorithm design and serve as a reference for later sections. Through this design exercise, it is possible to evaluate the effectiveness of the algorithm as an installation design tool and its ability to produce a structurallyviable form that translates a digital concept into reality in a way that is feasible for constructibility.

Figure 14â&#x20AC;&#x192; lager tunnel

The inception for the installation project was the lager tunnels under downtown Cincinnati. These historic tunnels were used for beer production dating back to the late 1890â&#x20AC;&#x2122;s. The form and scale of these lager tunnels were intriguing as a basis for design. The installation, originally planned to be put in these lager tunnels for exhibition, would be a gesture to the form of the tunnel while translating this intent through agent-based design.

The vaulted expression of the tunnels led to the manipulation of the script to produce shell-like forms that mimic the tunnels characteristics. The growth algorithm has no inherent knowledge of a shell structure and therefore through adaption of the script, as well as the integration of structural analysis, it was possible to produce a shell structure produced through growth while remaining structurally stable.

After running the simulation with the shell structure parameters, the algorithm generates a series of agents in threedimensional space. The placement is irregular and random based on the division of the agents. A decision was made, when considering construction of the installation, to make the agent spacing regular through voxelization. As described earlier, this is the process of snapping the agents to a three-dimensional grid for which the spacing is regular. This regularity allows for a modular-construction technique to be applied. This modular design means that one unit can be replicated numerous times and applied into this modular grid. The voxelization will act as a scaffolding for these modular units. In this installation, each of the agents suspended in the grid will become the centroid for the unit. Because the grid has regular spacing, all of the units will attach in the same locations on the neighboring mirrored unit. This allows for a unified method of attachment rather than unique connections for each unit.

48 Case Study: Installation

The scale model serves as the case study for the algorithm in an installation application and provide validation on the structural integrity of the simulation results. The scale model was exposed to forces, as the full-scale installation would have, which allows for an evaluation of the algorithm and the formal design of the installation based on the shell-like structural simulation.

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Figure 15â&#x20AC;&#x192; lager tunnel

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Unit Design

Figure 16 connection detail

When designing the module, the goal was to find a method that was able to produce the one thousand modules necessary for the installation as well as give enough flexibility to create a complex form that could be replicated. The method selected which allows for this amount of flexibility was a 3D printed master part to get the complexity and then using a cast to replicate the master part. In order to get a structurally-integral form, a urethane plastic was chosen as the casting medium.

This material yields high strength, even for small parts. Urethane also has a quick set time which allows for rapid fabrication of the modules. For calculation purposes, the unit sizing was set to an eight inch cube. By determining the module size, the load analysis could be determined based on the overall height of the installation. When cast in urethane, each unit would weigh roughly one pound which meant that at a ten foot overall installation height, the bottom module would have to carry a load of twenty pounds. Running these values through the analysis software returned an acceptable deflection of onesixteenth of an inch under the maximum load.

After confirming that the module would work within the constraints of the installation, the first mold was produced. The master 3D print was suspended in a box that was filled with silicone rubber to form a mold. Upon removing the mold from the box, it was apparent that the casting process chosen was not viable for the module design. There were voids within the module that trapped casting materials which encased the master part within the mold. To resolve this, a second iteration of the module design was created. In form, it used much of the original catenary curve motif as a basis but instead of creating linear elements that were difficult to work with in casing, the connection points were used as restraints for stretching a “fabric” to generate more planar elements. These planar surfaces allow for more open space in the module while providing the same structural stability as the first iteration of the module. The fabric was stretched between the connection points and then inflated to give the surface volume. These “fabric” planes were further refined through a series of iterations in order to allow them to be cast.

Once the module had a form that was able to be cast, it was run through the deflection analysis to determine if it was structurally viable for the installation. The module weighed in at 1.27lbs in the eight inch cubic form. While this weight in the

52 Case Study: Installation

module itself is not necessarily a problem, but the amount of urethane resin required exceeded the budget for the project which caused the module to be reduced to a six inch cubic form. This reduced the overall weight of the module to just 0.46 lbs. This resized module was analyzed and performed well at only three-sixteenths of an inch deflection. With this structural validation, the unit was deconstructed to allow for a mold to be produced.

Figure 17â&#x20AC;&#x192; stretched-fabric design

The unit design is a reflection of the lager tunnel vaulted form that will compliment the algorithm-generated form. While the full-scale installation was not generated, the process of designing the units and the connections will still be reflected in the scale model. This will allow it to be evaluated under similar criteria as the full-scale model.

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Figure 18â&#x20AC;&#x192; design process chart

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Mold Design Due to the complexity of the unit design, it was necessary to produce through digital fabrication techniques. The choice to make a cast of the unit, as described in the previous section, was imperative in order to quickly replicate the 3D printed master unit. There are several casting methods such as injection molding, CNC machined, as well as a traditional one or two part mold. Each of these methods pose their strengths and weaknesses in casting. An additional consideration for each is the complexity of the unit that is being cast as well as the material properties. For all of the casting methods, the unit was broken down into three pieces to make fabrication as simple as possible.

Figure 19â&#x20AC;&#x192; three-part unit

The two part mold is a traditional casting method that uses some form of material to imprint the master part. A silicone rubber was used as the molding material. In order to use this method, the part is suspended in a containment box which will enclose the rubber once poured in. As part of the two part method, half of the rubber was poured in to encase half of the 3D-printed master part. Once this was poured in, indexing pins were used to line up the other half that still needed to be poured once the first side was cured. After letting the mold sit for six to eight hours, the second half is poured into the containment box to create the two part mold.

Figure 20â&#x20AC;&#x192; breakdown of mold pieces: orange are CNC machines, magenta are 3D printed

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The next method explored was injection molding. This type of molding lends itself to the unit design because of the heavily digital design process. For injection molding, unlike the two part mold, there does not need to be a physical master part. Instead, the digital design file is able to be translated to a CNC machine that performs a subtractive fabrication technique. This means that the negative of the part is carved out of some type of molding material, typically steel or aluminum that can then be used as the mold which eliminates the manual process of casting the negative in a medium such as rubber. This type of molding process is ideal for producing numerous parts in rapid succession with minimal cleanup or breakdown.

Figure 21â&#x20AC;&#x192; 3D printing mold pieces

The final molding method that was explored was a CNC milled mold. Similarly to the injection molding process, the molding material starts as a rigid block of material and through subtractive fabrication processes, is turned into a usable mold. The molds were machined from solid blocks of fortypound polyurethane foam which was rigid enough to allow Figure 22â&#x20AC;&#x192; photograph of CNC milled mold used to test fabrication technique

for reusability and limited wear. Due to the restriction of axial movements to three, the mold had to be designed in a way that all of the surfaces were machinable in a single orientation. The three molds were broken down into thirty-seven parts. Of those parts, thirty-one were milled on the CNC machine. The remaining six were not able to fall within the criteria so they were 3D printed. These thirty-seven pieces were then assembled after they had been fabricated to form a water-tight mold for casting.

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Figure 23â&#x20AC;&#x192; 3D printed module

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Strength Curves

In mathematics, there are three primary curves: parabolic, logistic, and exponential. Each of these curves give a certain characteristic based on the curve type. The strength curve is defined by the upper and lower limits of the agents to be affected by this strength in order to properly formulate the function. In this case, the displacement force was the defining force that would be scaled based on the strength curve. When the algorithm is producing large displacement values, this is evidence of an unsupported or cantilevered condition. In order to resolve this, the algorithm takes the displacement force from the structural analysis and applies the exponential strength curve to the force which will allow the cantilever to be resolved.

This strength curve is changing the unary force that is being applied to the agents. The more displacement and further it is on the strength curve, the more unary force will be applied towards the ground. In the simulation, this application can be seen when the cantilevered agents “fall” or begin moving rapidly

Figure 24 parabolic strength curve

Figure 25 logistic strength curve

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Figure 26â&#x20AC;&#x192; exponential growth curve

towards the ground plane in order to define a support to resolve the structural system.

This process of using strength curves took many iterations to generate a form that was structurally stable and had an understanding of these gravity and cantilever forces. Once this part of the algorithm was working, the installation began to form shell-like forms that could be inhabited and also responded to the lager tunnel form that initially inspired the form. Seen in Figure 27, the smallest change in the way the algorithm processes data translates to a very different form as a result.

This series of iterations is a timeline that is representative of the algorithm development. As the earlier iterations indicate, the growth is not controlled and there is little understanding of structure. Once the introduction of the Karamba3D calculations takes place, there is an observed change in the logic behind the growth algorithm. The form begins to resemble a shell structure that is buildable. The incorporation of the structural analysis was dependent on the proper amount of influence over the growth.

Too much influence from the analysis would produce a pure dome structure while too little control would allow the structure to become weak and loose structural integrity.

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Figure 27â&#x20AC;&#x192; installation iterations from growth algorithm

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Iteration 1

Iteration 2

Static Unary No Structural Analysis

Dynamic Unary Force Structural Analysis Deflection Force

Iteration 3

Iteration 4

Parabolic Strength Curve Anchor Point Stop

Anchor Point Base Displacement Magnitude Limit Z Height

Iteration 5

Iteration 6

No Division at Base Ground Plane Dynamic Unary

Deflection Limit Ignore Large Deflections

Iteration 7

Iteration 8

Exponential Strength Curve Scale Force to Neighbor Limit Unary Strength

Logistic Strength Curve Scale Force to Neighbor Limit Maximum Deflection

Installation Design In the process of designing a module for the installation, there was a parallel development on the algorithm that was generating the form. The agents are dividing and creating growth through a sporadic process that generates a collection of points. As a primary part of this project, the incorporation of the structural analysis into the algorithm would allow the agents to have an understanding of gravity as well as material properties.

This additional information that is input creates a much different form than the uncontrolled growth.

Once the structural analysis was integrated with the growth simulation, the installation began to form shell that could be inhabited and also responded to the lager tunnel form that initially inspired the form. Once the incorporation of the structural analysis had concluded, the site was introduced as a design element. The algorithm could now understand where the installation was to be displayed as well as the boundary conditions of the tunnel. This meant that the algorithm could use the walls and floor of the site as a support to resolve any structural loads. Iterations 6 and 7 in Figure 27 begin to show the simulation using the wall as a support when the modules seem to reach this boundary plane where they will be able to lean against. The ability to influence the growth of the form through the site was the first rudimentary design principles added to the algorithm. The next would be the ability to map out a circulation path within the installation that influenced the form. The ability to input a path gave the opportunity for movement through and underneath the installation. The last design influence was areas for interaction. This gave the ability to define areas for seating or standing that would then influence the growth of the agents.

Figure 28â&#x20AC;&#x192; installation rendering

The final output from the simulation was able to incorporate the elements mentioned. In Figure 29, the rendering shows these elements and the outcome of the algorithm. The formal characteristics of the geometry are a result of the structural feedback on the growth algorithm. The simulation was restricted by the volume of the tunnel which lead to the use of the wall as support. The circulation pathway shown was used to deform the geometry and allow for movement within the installation.

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Figure 29â&#x20AC;&#x192; installation diagram

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Case Study Evaluation The installation was a way of using the simulation at a smaller scale before it was applied to a building form in the subsequent case study. Starting with a conceptual idea for an installation based off of a lager tunnel, the algorithm was designed to have an understanding of these conditions. While the script can inherently produce an infinite variety of forms, there were design considerations implemented to encourage the growth to form shell-like structures that mimic the lager tunnel vaulting. This shell form would be a theme throughout the case study and be a motif for the form as well as the modules that make up the installation.

The algorithm reached a point of completion where it was able to generate shell structures that were stable based on the structural analysis mentioned earlier. An additional programmatic element to the installation was circulation paths that influenced the form. These pathways would act as a repelling force to create a larger void space that allows for the movement of people. This force acted on the agents while generating the form that would have to adapt to this Figure 30â&#x20AC;&#x192; 3D printed scale installation

change and generate a structurally stable form. This addition of a programmatic element was an initial step towards an architectural-based input method that could later be used in a building application. The other additional predetermined design was a zone for seating within the installation. This was set prior

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to running the script and the algorithm was able to grow in a way that allowed for a seat at the correct height within the overall form. The final element of the algorithm was to give it an understanding of the environment in which it was generating.

Therefore, the additional information of the ground plane as well as the lager tunnel walls were input into the script. This allowed for the structural analysis to generate supports where the form met the ground plane as well as use the walls of the tunnel as a support to lean the form against. These parameters were also done with the intention that they could be used in the following building design project.

To understand the successes and limitations of the algorithm, the scale model will provide a certain level of validation for the design. The 3D-printed version allows for an understanding of the design intention and tests the structural integrity of the form.

While the material at this scale is stronger than it would be at a full-scale, it confirms that the algorithm is able to generate a structure that has an understanding of supports, as well as the environmental conditions of ground and the lager tunnel wall which also provides support.

The integration of programmatic requirements was still very elementary in this case study. The program was a static line that served as a representation of movement through the space. Future development of this integration could create a more responsive structure. The addition of interactive elements, such as seating and lighting, could also enhance the installation by providing more parameters for growth. The algorithm was able to generate a form dependent on structural analysis, site awareness, as well as prescribed circulation paths. The completion of this installation gave an understanding to the process of generating a shell form that was influenced by design-specific parameters.

Figure 31â&#x20AC;&#x192; module units stacked

Figure 32â&#x20AC;&#x192; installation rendering, aerial

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Case Study: Transit Hub

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Case Study: Transit Hub Smart Cities

“Growth is inevitable and desirable, but destruction of community character is not. The question is not whether your part of the world is going to change. The question is how.”38

As technology continues to improve, more cities are adopting legislation that will enhance their cities and provide improvements for the occupants as well as a optimizations to the infrastructure the city is built on. These are generally improvements for existing cities that would benefit from this updating to match the current technological improvements. As technology companies continue to develop new products for consumers, they are also looking at the benefits that integration at a city level could have on the sustainability in addition to the occupants that are part of the area. Currently, there have been proposals made in, and around, the United States for these highly integrated and technologically advances cities referred to as ‘smart cities’39.

38.

39.

40.

41.

McMahon, 2015 Allwinkle, 2011 Li, 2015 Manfredi, 2017

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A smart city provides improvements over conventional city planning and implement aspects that look towards the future of the area. For example, with the recent increase in electric car sales and progress on autonomous vehicles, cities are beginning to look at a network of ride-sharing as well as places for parking and charging these vehicles40. For the development of new smart cities, the goals that are being proposed include: zero-emission zones, vehicle-free occupants, autonomous transportation, pedestrian-friendly movement, and collection sensors for metrics such as water and energy usage41. These are a few of the commonly shared goals for these cities that have been proposed within the last 3 years. A few of these objectives, with the exception of autonomous transport and data collection, are implemented at smaller scales today. Cities such as San Francisco, California and Columbus, Ohio are using many of these strategies to provide updates and improvements to the cities42. Zero-emission is something that the LEED program as well as the Living-Building challenge are working towards on individual building levels43. In the case of the smart city, these requirements for zero-emission would be on a city-wide basis.

This wide adoption of these sustainability strategies would

be something not seen before in any city including the two previously mentioned.

As autonomous transportation continues to improve, the need for every occupant or family in the city to own a car is unnecessary. Already planned in the coming future, Tesla has announced the ‘Tesla Network’ which would be a service that allowed the autonomous vehicles to provide services similar to that of Uber or Lyft44. The vehicle owner would use the car to commute to their destination and then instead of a vehicle being parked for the entire work day, the car would autonomously navigate the city and provide other citizens a ride to their destination. This would negate the need for occupants of the city to own a car if they were able to pay a fee to get to their destination with minimal hassle. It would also remove the need for parking garages that occupy valuable square-footage in cities.

Figure 33 Tesla supercharger station

There have been a number of smart cities proposed already.

The two that are most relevant to this project are by Alphabet of Google and Bill Gates of Microsoft. The first project, by Alphabet, is proposed for a waterfront in Toronto, Ontario45. The project looks at reducing traffic congestion, creating high-tech networks of systems, as well as provide affordable housing through new methods of construction. These opportunities provide the area Figure 34 Toronto Sidewalk Labs planning

with forward-thinking planning that would set a new precedent for cities to follow. The project plans to have densely aggregated housing along the waterfront with easy access to workplaces from these units. Alphabet has also proposed the incorporation of an array of sensors into the city that would provide realtime data from a variety of use-cases such as water, sewage, and emissions. This type of monitoring is unprecedented on a city-scale but would allow for the city to function as a piece of hardware that Google can manage. This could potentially reduce

42. Allee, 2016 43. International Living Future Institute 44. Tesla, 2018 45. Sidewalk Labs

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the emissions of a city if the sensors were able to trace a blip in data.

Similarly to the Alphabet project, Bill Gates has proposed a smart city in Arizona that would be more heavily focused on autonomy rather than data analytics in order to reduce the emissions for the city while providing a better overall experience46. The city would be located fifty miles outside of Phoenix, Arizona. The proposal states that the city would “create a forward thinking community with a communication and infrastructure spine that embraces cutting-edge technology, designed around digital networks data centers, new manufacturing techniques, autonomous vehicles and autonomous vehicle hubs.”47

Figure 35 waymo autonomous vehicle

These ambitious goals for the smart cities proposed by both Alphabet and Bill Gates use forward-thinking and technology-driven design to influence the people that live in the city. This thesis project will use a similar site to the two previously announced projects twelve miles south of Boston in Weymouth, Massachusetts. The city implements many of the aforementioned strategies to reduce emissions as well as increase the overall wellness of the occupants. The masterplan

Figure 36 aerial rendering of Union Point, courtesy of Elkus Manfredi

46. Taddune, 2018 47. Belmont Partners 48. Manfredi, 2018

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for the city was done by Elkus Manfredi and Sasaki Associates based in Boston48. The ‘smart city’ is located at the abandoned Naval Air Station South Weymouth which has already undergone preliminary changes such as the arterial roads that will serve the new city. The site’s proximity to Boston is crucial in the development of the new city. The masterplan calls for fourthousand new housing units as well as three-million square

feet of office space49. This amount of housing and office space will create a large influx of people each day making the current South Weymouth station insufficient. Based on the existing framework of the masterplan, this project will analyze the need for a transit hub in the city of the future taking into account the goals dictated by this and other ‘smart cities’ to prepare the architecture for the future.

Figure 37 rendering of Union Point, courtesy of Elkus Manfredi

49. Manfredi, 2018

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Design Intent

Figure 38 lighter-than-air blimp at South Weymouth

Located next to the city of Weymouth, Massachusetts is the abandoned site of the Naval Air Station South Weymouth. The U.S. Navy airfield was in operation from 1942 to 1997 and served as a station for the Navy’s Lighter-Than-Air program50. The site was used as a storage center for these blimps and airships as a submarine patrol for the coast. After being decommissioned in 1997, the land was sold to the Tri-Town Development Corporation to become a master planned development at South Weymouth50. It wasn’t until 2016 that the master plan was given considerable development by Elkus Manfredi and Sasaki Associates and renamed as Union Point which reflects a partnership with the surrounding areas of Rockland, Abington and Weymouth.

Figure 39 naval air station, South Weymouth 1942

Figure 40 existing station for South Weymouth

50. Weymouth Massachusetts, 2016.

51. MBTA South Weymouth

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The existing train station is located along one of the arterial routes for Weymouth serving the surrounding area. Located twelve miles south of the city, the station receives many commuting passengers that travel to Boston for work each day. On average, the current station receives 5,513 passengers each day which is able to be accommodated51. The existing station consists of two parking lots, each holding about fourhundred cars for the passengers using the commuter rail. This station location is problematic for the master planned area as it is significantly outside of the developed area. This would mean that transportation needs to be in place to shuttle riders between the station and the new city. The second problem with the station is that is not sized to accommodate the significant increase in passenger traffic. The estimated daily traffic for Union

Point would be thirty-one thousand passengers. In order to use the existing station, it would need to be significantly resized but this still doesnâ&#x20AC;&#x2122;t address the shuttling problem. To best resolve these problems, this project proposes to move the train station within the limits of Union Point and reroute the passenger rail to accommodate the new city.

As mentioned in the previous section, smart cities have many new requirements and are projecting the changes in transportation to accommodate the new infrastructure. These requirements range from autonomous vehicles to net-positive energy returns. The transit hub will not only serve as a charging station for vehicles but it will meet the needs for a vehiclefree city. With close proximity to the stadium as well as the baseball field, the station will provide a gateway for residents of neighboring cities to attend the sporting events and concert venues. These considerations will be taken into account when developing and programming the space to provide adequate space for current and future development of the area. The transit hub should emulate the future of transportation in a city and allow for the developments called for in the new smart city.

Figure 41â&#x20AC;&#x192; ground plan of preliminary design

For this project, the transit hub will include train platforms, autonomous vehicle parking, battery swap stations, local transportation, retail shops, parking for the surround area, and green space to extend the green belt that runs through the city.

The new station will also be able to accommodate the traffic of the new area and the stadiums with an estimated peak usage of 43,000 per day. The train platforms will serve as a new stop to replace the existing South Weymouth station. These lines

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Figure 42â&#x20AC;&#x192; proximity to boston, twelve miles south

connect to the Massachusetts Bay Transit Authority Commuter Rail which terminate at South Station in downtown Boston.

This direct linkage with Boston will allow for commuters to travel between Union Point and neighboring areas with ease.

Once commuters arrive to the city, there will be no option for driving a car inside Union Point. Because the area is vehicle free, there will be an abundance of autonomous vehicles serving as transportation for residents and visitors. Due to the ever-changing demand for transportation, these vehicles will need a storage area. The transit hub will facilitate these needs in addition to providing battery swapping stations for those vehicles that need recharged. This incorporation of autonomous vehicle needs into the transit hub will provide some futureproofing for the building.

As the passengers arrive at the station, there will be an area for pick-up and drop-off just as there are at most current train stations. The difference for this train station is that they will all be autonomous. For the neighboring area, there will be parking provided to leave their vehicles while exploring the city.

For those passengers that are waiting for the commuter rail, there will be shopping and restaurants available. The shops will also serve some of the stadium occupants during usage due to its proximity. Lastly, the transit hub will use the remaining area in front of the station for public green space. The Union Point masterplan52 includes a green belt that runs throughout the city and terminates at the edge of the transit hub site. By continuing this public space, it will allow for pedestrians to easily access the transit hub for transportation or as a destination for retail and dining.

The transit hub will be the culmination of all the previous simulations in the attempt to use algorithmic design for architecture. The incorporation of programmatic elements as well as structural analysis will be used in order to influence the algorithm with rudimentary architectural design decisions.

This case study will explore the success of the growth-based strategy used in this thesis for architecture.

52.â&#x20AC;&#x192; Mandredi, 2015

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Algorithmic Design When designing the transit station, there were three main components addressed by the algorithm: structure, human circulation, and program. As in the installation case study, the structure of this building was calculated using Karamba3D. This allowed the form of the building to be structurally resolved with support points touching the ground. The shell algorithm is a growth-based agent system with structural analysis incorporated. The form of the building was controlled through the specific inputs that led the building to form a shell. The shell of the building was influenced by the surrounding context and attracted to specified elements. It was also pushed along the train tracks to elongate the form to allow for trains to pass through. These are design elements that the algorithm reacts when generating the shell.

The crowd simulation for this case study use a swarm-based algorithm. This gives the agents the ability to be self-organizing and function as a swarm rather than individual agents. The algorithm starts these agents at specific points within the city, specifically the main intersection corridor, the baseball stadium, the train platform, and the football stadium. These were all points that are high-traffic areas within the surrounding context. The movement of the agents from these specific points will simulate the movement during these high-traffic times that need to be accommodated by the new transit hub. These swarm-agents left trails as they moved throughout the site. These movements were then used to inform the generation of the shell. As the swarm-agents moved, their vector direction was sent to the nearest shell agents. This caused the shell to deform with the direction of movement of the swarm. As stated before, specific properties were programmed such as the shell movement along the train track. When running the simulation, the shell exhibited a formal placement of entrance for the building based on the high-traffic swarm movement in this area. This action was not programmed but the algorithm was able to translate the movement of the swarm into a fundamental design decision allowing for the entrance to the building to form a large open space.

The final step for this case study was to give the algorithm an understanding of the program in the building. This was done at the most fundamental level with a general idea of size as well as adjacencies. There were two types of program agents: train platform and retail. Each of these agents were given

Figure 43â&#x20AC;&#x192; building simulations

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Figure 46â&#x20AC;&#x192; building simulation development

Process 1

specific placement parameters to help in the initial generation of program. These program agents used the growth-algorithm as the shell did minus the structural analysis integration. The program agents were given an understanding of their purpose in the shell. The train agents would need to stay near the platform while the retail agents had a little more freedom under the shell. The train agents would aggregate along the platform while still leaving some space for circulation. The retail agents started their division under the shell and were influenced by their proximity to a support point for the shell structure. This caused them to grow at a rapid rate as they approached the structure. Both of these program agents were influenced by the movement of the crowd agents as well. The trails left behind by the swarm algorithm acted as circulation pathways that would be used by these programmatic agents.

Process 2

Process 3

Figure 44â&#x20AC;&#x192; boids as agents start to grow

Process 4

Process 5

Figure 45â&#x20AC;&#x192; boids influencing agents

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The development of this multi-agent system allows for an informed design process taking into account multiple aspects. In this simulation, the shell structure was informed by the swarm movement but also was an influence on the programmatic agents. This connection between the different agents allows for the design to reflect the multiple inputs simultaneously. Each of the different agent groups communicate with each other to form a homogeneous system that works as one to create the building structure as a result of this analysis.

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Figure 47â&#x20AC;&#x192; script diagram

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Train Station Design As discussed earlier, the transit hub needs to meet the needs for the smart city that is being proposed. With the increase in traffic as well as the autonomous transportation goals of the city, the transit hub needs to respond to the growing needs for the immediate passengers as well as the future needs.

The formal characteristics of the building are taken from the algorithm as a direct translation of points to a surface. This shell was then manipulated to resolve any erroneous elements left over from the simulation and then became the base surface for the building. The irregular surface was rebuild to have regular triangulations which would make it possible to create a space frame. Once the sizing of the triangulation was set, the corner points of these triangles were taken to generate the space frame. The space frame generated is a four-way double layer space frame derived from these triangle corners. The depth of the space frame was set to an acceptable distance for the span in this project. Once this was done, the interior of the building could be programmed in the remaining space.

Figure 48â&#x20AC;&#x192; ground floor plan

82 Case Study: Transit Hub

The first level of the building features a large outdoor pavilion and a continuation of the green-belt that terminates at the edge of the site. The lower part of the site is adjacent to the sports stadium while the upper part of the site serves as the local transit center. There is a light-rail station as well as the entrances for autonomous and passenger vehicles. The main level of the building serves as a retail corridor as well as access to both the train waiting area and autonomous vehicle loading zones. The retail space is organized towards the center of the space to leave large uninterrupted views within the shell and create a terracing between the floors. The lower floor is the main autonomous loading area as well as parking and charging. This space is visible from the main floor in an opening to below where passengers will wait for their rides. This area also includes a small cafĂŠ space to serve these waiting passengers. The upper floor is a mixture of retail and dining with a large waiting area for train passengers.

To control the movement of passengers onto the platform, the entrance to the platform is from above on the second floor near the waiting area.

The floor plates are all pulled away from the space frame to allow for a floating quality. This also allows for views below and above through these openings. The transit hub features large glass opening to view the trains as well as expansive spaces which can be attributed to the use of the space frame.

Passengers are greeted with a large atrium space from both the city as well as the stadium entrances. On the main floors, the openings to the autonomous zones will allow passengers to interact with all three floors at once. One of the most important aspects of the design is the interaction with the space frame. On the lowest level, passengers are able to see where the frame meets the ground supports in the autonomous zone. On the second floor, passengers are able to move through the space frame as they travel over the train track to the continuation of the green-belt on the other side. These interactions were choreographed to give the users an understanding of the scale of the space as well as allow a variety of scales through the usage of these passageways.

83

Case Study Evaluation This case study was the culmination of structural analysis, pedestrian simulation, site analysis and programmatic layout to create the formal representation of the transit hub. The structural analysis was the continuation of incorporating structural analysis into the simulation after the installation case study. The algorithm was still producing a shell form but received new limitations in this case study such as height and context.

This performed as expected in the transit hub. The algorithm was able to generate a resolved structure with ground support points to resolve the loads. When running the algorithm, there was not a predetermined stopping point for the simulation based on structural analysis. Therefore, there are some unresolved cantilever conditions as a result. Had the simulation continued to run, the algorithm would have resolved these issues. These conditions are able to be resolved manually after translating the algorithm into a building.

Figure 49â&#x20AC;&#x192; algorithm analysis

The next task that was simulated was pedestrian circulation.

The goal of this was to synthesize the paths that passengers would take through the space as well as in the neighboring sites. The pedestrian agents were able to inform the shell of the building through structural deformations based on their proximity and directionality. This created a large open entryway where there was heavy traffic. The ability for the agents to inform the structural formation is an important step in the integration of multiple agent types and processes. However,

84 Case Study: Transit Hub

the pedestrian agents are limited to a two-dimensional understanding of space. The circulation is only happening on a ground plane which doesn’t account for the upper and lower levels that were added afterwards.

The incorporation of site into the algorithm allowed for an understanding of the context as well as importance in circulation.

For the structural agents, the attraction to the nearby stadium was a design decision but allows the building to be more specific and introduces a design gesture within the algorithm. The starting points for the pedestrian simulations were based on site analysis to determine the main corridors as well as the highest traffic areas. This would allow the form of the building to respond to passengers as they would approach the building similarly to the pedestrian agents in the algorithm. Future developments could be made to better understand the neighboring programmatic elements as well as simulate the movement of different types of traffic (human vs. vehicular).

Figure 50 retail algorithm

The algorithm had a general understanding of program and was given parameters in order to lay it out. The train platforms needed to be near the tracks so the agents aggregated along them. For the retail, the agents were given parameters that measured the proximity to ground support points in order to trigger their growth. This caused the agents to spread out as the structure grew. The pedestrian agents also manipulated the placement of the program based on their movement trails. These ‘trails’ acted as a circulation diagram for the layout of the program. Similarly to the pedestrian simulation, the programmatic layout was only on a single level and the algorithm did not have specific square-footage goals.

Overall, the combination of these four different analyses into a single algorithm allowed the design of the building to be influenced simultaneously by these parameters and respond accordingly. There were programmed design traits such as the shell form but the overall form with the entrance created by the pedestrian agents as well as the formal quality of the building were unintentional. These are a phenomena created through this algorithmic design process described by Snooks53. These elements are unexplained and not programmed for but create architectural interest and can be used in the overall building form. The algorithm has a fundamental understanding of design through basic inputs and is able to create a form based on a set of information provided.

53. Snooks, 2014

85

Figure 51 section one

Figure 52 section two

86 Case Study: Transit Hub

87

88

Evaluation

89

Evaluation Algorithmic simulations are able to synthesize an array of different biometric processes, such as the swarm and growth algorithms addressed earlier. While these processes alone are able to create forms, the resulting output does not represent any specific process or ideology. In the growth algorithm, which this thesis utilizes primarily, it is able to construct generic growth of a set of agents. The novel application of this biologic growth process is the ability to harness the computational power to process and simulate fundamental design principles.

Figure 53â&#x20AC;&#x192; displacement force on agents

The initial goal was to incorporate structural analysis into the growth algorithm in order to create an informed system.

This was done through the open-source code for Karamba3D54 which allows for direct integration into the growth simulation.

As described in the earlier section, the agents of the growth simulation became nodes between which beams were formed in order to perform structural analysis. This integration returned values in the measurement of displacement. Each of the agents have a displacement value associated which allows the algorithm to influence only the agents that exceed the displacement value for the given material. The agents are each assigned a unary force dependent on the load calculated in the structural analysis. This unary force is in the applicate direction with varying strength depending on the displacement value that is then run through a strength curve. The result of this process causes the shell agents to collapse towards the ground plane until a support is established. This reduces any cantilever conditions that would have been present without the structural analysis.

This thesis looks at the integration of many processes such as programmatic analysis, structural analysis, crowd movement, and site conditions to name a few. All of these processes have been integrated into the script used to generate the formal architectural elements in the later sections. This integration allows the algorithm to simultaneously analyze and understand the influence that these different processes have on the form being generated in the algorithm. Through the process of simultaneously running structural analysis with the growth simulation, the result begins to dictate the formal characteristics of the growth algorithm. Once the structural analysis was integrated, the form of the building could then be influenced through parameters such as the material being used to construct the form as well as the strength of the connections.

The shell-like form for both the installation as well as the transit hub came from this intentional manipulation of the algorithm to

54.â&#x20AC;&#x192; Preisinger, 2014

90 Evaluation

produce these forms.

Figure 54â&#x20AC;&#x192; agents flocking trails over site

By creating an intelligent form with an inherent understanding of structure, other manipulations that influence the form being analyzed will be resolved in real time through the structural analysis portion of the algorithm. A fundamental but important principle of design is the response to the movement of people. This parameter allows the growth agents to understand the patterns and paths of movement through space. At the scale of an installation, this integration allows for a passageway where occupants can interact with specific elements that have been determined. The installation used circulation paths to dictate the form which stretched over the desired area. The circulation path was able to manipulate the growth agents to allow the form to respond to these new conditions which meant that the structural analysis would have to perform calculations in order to make this form manipulation work. At the building scale, this influence was used on a much broader scale. The algorithm was simultaneously running growth simulations for the shell of the building as well as a flock simulation for the crowd agents that were influencing the form.

The algorithm uses approximately one-thousand flocking agents to simulate the movement of crowds of people in the site. The movement of these agents influences the shell-like form that is simultaneously being produced on the site by pushing and pulling these agents relative to the movement of the flock. This allows for highly populated areas to become more open while less traveled areas are more closed off allowing for structural supports that meets the ground. The flocking agents had site influences that lead to the circulation patterns they produced. In

91

the transit hub. There were important surrounding contextual elements such as major thoroughfares as well as the adjacent stadium that influenced the movement of the crowd.

Incorporated on the building scale, the idea of programmatic development was included in the algorithm. This allows the simulation to process and understand potential locations for different programmatic elements. In the case of the transit hub, this meant that the algorithm was able to produce zoning for retail space, train platforms as well as circulation. While all of these programmatic agents were being calculated, they were also being influenced by the flocking agents which allows the programmatic elements to have an understanding of circulation and movement through the space.

Through these different parameters in the algorithm, there are simultaneously thirty-three processes taking place every second in order to perform the calculations necessary to run the simulation. The processes listed above are not a comprehensive list but begin to analyze the computational power that the algorithm affords. Through more coding and development, the algorithm could potentially run countless more processes to create improved integration for design purposes. Currently, only the most fundamental elements of design have been addressed through the algorithm but this integration of multiple elements demonstrates the potential opportunities for future developments to the simulation.

92 Evaluation

93

94

Conclusion

95

Conclusion The integrated multi-agent algorithm described in this thesis seems to have promise as a generative design tool.

The inclusion of structural analysis in the algorithm seems to be promising for both installation generation as well as building forms. The pedestrian simulations showed merit in site analysis while also providing formal manipulations of the structural system. The integration of architectural program elements in the generative system demonstrates the probable use for building design in the future. The case studies in this thesis provide promise of its usefulness in real-world applications. While the algorithm has currently only been used for formal manipulations, the faรงade and other architectural elements could be incorporated as additional agent algorithms.

Additionally, the use of this algorithm could generate structures with programmatic intention as well as refined structural qualities to produce a building derived from this data. In order to make this achievable in the future, further research needs to be done into incorporating design intelligence into the algorithm both programmatically and formally.

96 Conclusion

97

98

Appendix

99

Related Work

Figure 55 2016-2017 pavilion

Figure 56 digital fabrication diagram

Figure 58 2016-2017 pavilion

100 Appendix

Figure 57 computer controlled assembly

ICD ITKE Research Pavilions University of Stuttgart // Stuttgart, Germany // 2016

The Institute for Computational Design and Construction (ICD) and the Institute of Building Structures and Structural Design (ITKE) at the University of Stuttgart complete yearly research pavilions that explore a variety of construction techniques as well as computational design processes. In this pavilion from 2016-2017, the team used an novel use of fibers to construct the installation. Through a process of computational analysis of a form, drones and robots were able to be choreographed in tandem to create the pavilion structure. The analysis of the form was able to determine the density of fibers as well as their placement in order to make the pavilion structurally viable.

The project uses biomimicry derived from the spider webbing as a basis for the fiber construction. The team aimed to use computational design tools in conjunction with digital fabrication techniques to create a lightweight but strong structure.

The spider webbing offered the team the ability to use the organization from these arachnids and apply those techniques to an architectural pavilion. This amalgamation of digital fabrication and computational design elements allowed the structure to be built taking advantage of the biological processes that it mimics.

55.â&#x20AC;&#x192; Fleischmann, 2011

101

Figure 59 cloud pergola installation

Figure 61 algorithm generated cloud

Figure 62 installation detail

102 Appendix

Figure 60 mimicing weather patterns

Cloud Pergola Alisa Andrasek // Venice Biennale // 2018

Alisa Andrasek believes that computational design tools, such as growth algorithms, offer designers the ability to create what she refers to as ‘high resolution architecture.’ This is only available through the accelerated power of computational tools which allow for complex structure generation. These structures would not be constructed without the help of digital fabrication tools such as the ones used by Andrasek and Wonderlab. Andrasek’s research focus in all of her projects is with high resolution micro-structures that are information-rich and provide complex analysis through data-driven design.

The installation gets its formal characteristics from weather pattern data. The algorithm simulates the movement of the agents in the script from the weather data provided which is then sent to an artificial intelligence processor to compute the steps necessary for robotic construction. The structure of the installation is three columns to support cloud hanging overhead. This process of using weather data which is then interpreted and transformed into something that is 3D printable would not be possible without computational design and artificial intelligence processes. The form of the installation is generated through a robotic arm that is extruding plastic to form this pavilion. The installation creates movement through the structure as well as a dynamic viewing interface through the dense 3D printed structure.

This pavilion and design process, in combination with Klemmt’s growth simulation, were influential in the creation of the scale model installation for this project. The ideology behind using computational design as a formal driver in a project and incorporating fabrication techniques in a design are something that influenced the design and enhanced my understanding for working on a building scale.

56. Andrasek, 2018

103

Figure 63 Arnhem Central structural system with skylight

Figure 64 exploded view of shell

Figure 65 diagram of circulation and shell

104 Appendix

Figure 66 boat-like structural form

Arnhem Centraal Station UNStudio // Arnhem, Netherlands // 2015

The Arnhem Centraal Station in the Netherlands is a multiuse space in which only have of the occupants are passengers of the train line. This station is unique in that in addition to the commuter train, the building has an office tower, a cinema, and retail shops. The design for this station was done by UNStudio and was in the planning process for over 20 years. The designers wanted to design this building to be a “transfer machine” for train passengers as well as provide a front door for the city.

The building is constructed using new construction techniques adopted from boat design. The thin, light undulating structure is a replacement for the traditional heavy steel framing and concrete. Instead, the building uses thin steel panels formed to the precise shape to fit in place. This means that the building still has the traditional look of a concrete shell but with much less weight and cost than the traditional methods. The space is free of columns thanks to this innovative structural technique. The lead designer of UNStudio said “Arnhem Centraal is no longer just a train station. It has become a transfer hub.” This precedent was used its for spatial qualities as well as innovative fabrication techniques.

57. UNStudio, 2015

105

Glossary Acceleration: a rate of change for the velocity of an object over a period of time (See Methodology section) Agent: a point in a three-dimensional environment that is assigned specific properties that are unique to this particular point (See Methodology section) Algorithm: a mathematic process through which a problem can be solved within a finite amount of time (See Methodology section) Applicate: refers to the z-axis as it relates to a coordinate in three dimensions (See Methodology section) Attractor: a point of object which alters the vector of an agentâ&#x20AC;&#x2122;s movement to be in the direction of said attractor (See Methodology section)

Banking: refers to the agent in Reynoldsâ&#x20AC;&#x2122; swarm simulation and its ability to translate in a tangential direction to the agent (See Methodology section)

Boids: refers to the agents that exhibit the flocking characteristics in a swarm simulation (see Methodology section) Cantilever: refers to the overhang created by a structural member that is secured at the opposite end (See Evaluation section) Centroid: this is the center of mass for a geometric form that has density such as a cube. This is used to apply units to a voxel grid (See Case Study: Installation section) CNC: computer numeric control, automated control for machining tools that provide precision movement ( See Case Study: Installation section)

Constructor: refers to the method of assigning properties to an agent when it is being constructed (See Methodology section) Displacement: refers to the amount of movement by the beam in a structural calculation. This movement is a result of deflection (See Methodology section) DNA Sequencing: refers to the categorization of the four DNA bases used to determine things such as probability of certain diseases (See Methodology section) Functions: these are elements of the algorithm that perform

106 Appendix

specified calculations that are then accessible to the entire script (See Methodology section)

Indexing: refers to the process of using a point of reference in machining ( See Case Study: Installation section) Injection Mold: a method of fabrication when plastic is heated to the point of disambiguation and injected into a mold before cooling to reveal a molded plastic part ( See Case Study: Installation section)

Net-Positive: the ability to produce more energy than is consumed (See Case Study: Transit Hub section) Node: refers to the point that is attached to the end of a beam.

This is used for the structural analysis where a beam is tethered in place by two nodes (See Methodology section) Normal: refers to the vector pointing in a perpendicular direction that is surface of the given point (See Methodology section) Polyurethane Foam: a polymer composed infused with small amounts of blown agents to produce a cellular foam ( See Case Study: Installation section)

Pseudocode: refers to a method of describing the operations of a piece of code without the coding language syntax (See Methodology section)

Stereolithography: this is a process of 3D printing with layers of powder using a binding agent to create a solid object ( See Methodology section)

Tooling: refers to the process of machining and the different tools necessary to perform this operation ( See Case Study: Installation section)

Unary: refers to a vector that is applied in a specific direction as a vector. This is commonly used as a representation of a gravitational force (See Methodology section) Urethane Plastic: a type of polyurethane plastic that is an elastomer able to retain elasticity and strength while still being tremendously impact resistant. A durable material chosen for the installation case study (See Case Study: Installation section)

107

Vector: this refers to a linear form of directionality that is constructed by a base point and a corresponding point in threedimensional space that defines a line for which movement can take place (See Methodology section) Velocity: refers to the speed of an object in a given direction. This uses a vector as a direction and translates that to motion (See Methodology section)

Voxel: refers to a method of constructing a grid in while point values are rounded to the nearest integer value. This creates an array of discrete elements that are representative of objects in a three-dimensional space (See Methodology section)

108 Appendix

109

Table of Images

Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure

1 2 3 4 5 6 7 8 9 10 11

Figure Figure Figure Figure Figure Figure Figure Figure Figure

12 13 14 15 16 17 18 19 20

Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure

21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

Figure 37 Figure 38 Figure 39 Figure 40 Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure

110 Appendix

41 42 43 44 45 46 47 48 49 50 51 52 53 54 55

ARUP, 2018: generative design 3D print, ARUP Brandon Sieb, 2015: bird swarm Kokkugia, 2008: swarm urbanism, kokkugia.com Neri Oxman, 2015: unified approach to grown structures, MIT Media Lab Zaha Hadid Architects, 2013: Ciab Pavilion, karamba3d.com Richard Hardman, 2018: growth simulation Richard Hardman, 2018: initial setup, growth algorithm Richard Hardman, 2018: agent-based algorithm diagram Richard Hardman, 2019: agent characteristics diagram Richard Hardman, 2019: neighbors located within radius of agent Richard Hardman, 2019: attraction points within radius of agented within radius of agent Richard Hardman, 2019: closest neighbor list diagram Richard Hardman, 2019: agent division diagram Brewing Heritage, 2014: larger tunnel Richard Hardman, 2018: module rendering Richard Hardman, 2018: connection detail Richard Hardman, 2018: stretched-fabric design Richard Hardman, 2018: design process chart Richard Hardman, 2018: three-part module design for fabrication Richard Hardman, 2018: breakdown of mold pieces: orange are CNC machines, magenta are 3D printed Richard Hardman, 2018: 3D printing mold piece Richard Hardman, 2019: photograph of CNC milled mold Richard Hardman, 2019: photograph of 3D printed module Richard Hardman, 2019: parabolic strength curve Richard Hardman, 2019: logistic strength curve Richard Hardman, 2019: exponential growth curve Richard Hardman, 2018: installation iterations from growth algorithm Richard Hardman, 2018: installation rendering Richard Hardman, 2019: installation diagram Richard Hardman, 2019: photograph of installation scale model Richard Hardman, 2019: photograph of 3D printed modules stacked Richard Hardman, 2018: installation rendering, aerial Tesla, 2018: kettleman city supercharger station, telsa.com Sidewalk Labs, 2018: toronto city planning, sidewalktoronto.ca Kristoffer Tripplaar/Alamy, 2017: waymo autonomous vehcile, Arizona Elkus Manfredi, 2017: aerial rendering of Union Point cty, elkus-manfredi. com Elkus Manfredi, 2017: rendering of Union Point stadium, elkus-manfredi. com Weymouth Massachusetts, 1942: lighter-than-air airship, weymouth. ma.us Weymouth Massachusetts, 1942: naval air station, weymouth.ma.us Massachusetts Bay Transit Authority, 2014: existing station for South Weymouth, mbta.com Richard Hardman, 2018: ground plan for preliminary design Richard Hardman, 2018: proximity to boston map Richard Hardman, 2019: building simulations Richard Hardman, 2019: boids and growth agents Richard Hardman, 2019: boids influencing agents Richard Hardman, 2019: building diagram Richard Hardman, 2019: ground floor plan Richard Hardman, 2019: algorithm analysis Richard Hardman, 2019: retail algorithm Richard Hardman, 2019: section one Richard Hardman, 2019: section two Richard Hardman, 2019: displacement forces on agents Richard Hardman, 2019: agents flocking trails over site Roland Halbe, 2017: digital fabrication diagram, uni-stuttgart.de University of Stuttgart, 2017: 2016-2017 pavilion, uni-stuttgart.de

Figure 56 Figure Figure Figure Figure Figure Figure Figure Figure Figure

57 58 59 60 61 62 63 64 65

University of Stuttgart, 2017: computer controlled assembly, uni-stuttgart. de Roland Halbe, 2017: design pavilion, uni-stuttgart.de Alisa Andrasek, 2018: cloud pergola, alisaandrasek.com Alisa Andrasek, 2018: mimicing weather patterns, archinect.com Alisa Andrasek, 2018: algorithm generated cloud, alisaandrasek.com Alisa Andrasek, 2018: installation details, alisaandrasek.com UNStudio, 2015: entry-way structural view with skylight, unstudio.com UNStudio, 2015: exploded view of shell structure, unstudio.com UNStudio, 2015: diagram of circulation and shell, unstudio.com UNStudio, 2015: boat-like structure of shell form, unstudio.com

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