archiGo

archiGo

Research Cluster 3, 2017-2018 M.Arch Architectural Design

UCL, The Bartlett School of Architecture

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JELENA PELJEVIC | YEKTA TEHRANI | SHAHRZAD FEREIDOUNI | NOURA ALKHAJA

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RESEARCH CLUSTER 3 | LIVING ARCHITECTURE TYSON HOSMER | DAVE REEVES | OCTAVIAN GHEORGHIU

MACHINE LEARNING TUTOR | PANAGIOTIS TIGAS THEORY TUTOR | JORDI VIVALDI PIERA ADDITIONAL THANKS TO:

ROBOTICS CONSULTANT | ARTHUR PRIOR

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Contents Chapter 00 : Studio Brief

Chapter 04 : Fabrication

0.0 Introduction | Studio Brief

4.1 Tile Particles 4.2 Joinery Studies 4.3 Fabrication Research 4.4 Prototype Research 4.5 Tiles + QR Scanning 4.6 Deployment 4.7 Discrete Connectivity Studies

Chapter 01 : Project Overview 1.1 Thesis Statement 1.2 Holistic Diagram 1.3 Concept Reference: Inspirations 1.4 Research on Connectivity Rules 1.5 Neighborhood Rules 1.6 Case Studies

Chapter 02 : Computational Research 2.1 Wave Function Collapse 2.2 Graph Research

Chapter 03 : Tile Research 3.1 Tiling System 3.2 Tile Breeds 3.3 Spatial Tiling Research

Chapter 05 : Mars Research 5.1 Mars Platform Research 5.2 Case Studies 5.3 Criteria for Evaluation 5.4 Life Support System 5.5 Sun Analysis 5.6 Machine Learning Criteria

Chapter 06 : archiGo 6.1 Machine Learning 6.2 Density 6.3 Stability 6.4 Optimized Evaluation 6.5 APP

Chapter 07 : Architectural Speculation 6.1 Spatial Development

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INTRODUCTION

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RC3

Introduction

Chapter 00

Studio Brief : Living Architecture

Studio Research Framework: Living Architecture // Tyson Hosmer | Octavian Gheorghiu | David Reeves “The role of the architect here, I think, is not so much to design a building or city as to catalyze them; to act that they may evolve. That is the secret of the great architect.” Gordon Pask “Traditionally we are taught to use top-down methodologies to design buildings. As the master of our design we look at the physical, social, and historical context of a project in its site as a set of inputs and we envision form and space to fit. Yet almost all meaningful social, biological, and economic systems are organized and “designed” from the bottom-up.

Architects turn to diagrams and other abstract machines as both analytical and generative devices.

Like a set of architect’s x-ray goggles, they are asked to “reveal” the underpinnings of the design. Utilising their power of abstraction, diagrams are called upon to liberate the architect by stripping away superfluous information to “reveal” ordered relationships between contextual layers. The conversion or hinge between analysis and generation is where the true instrumentality of the diagram consistently breaks down. What is the diagram’s relationship to form and material? Is it form? Can it be a physical construct? The mathematician John Von Neumann said that “Life is a process, which can be abstracted away from any particular medium”. He believed that the basis of life is in information and asked the question “What kind of logical organization is sufficient for an automation to reproduce itself?”

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CHAPTER ONE | PROJECT OVERVIEW

0.1

THESIS STATEMENT 12

Living Architecture // Tyson Hosmer, Octavian Gheorghiu, David Reeves “ archiGo is an experimental architectural proposal, which takes the AlphaGo program (Google’a AI program which plays the board game Go) as inspiration of an intelligent model for decision making. The proposal consists of partial spatial configurations planned as “Tiles”, which continuously assemble to generate larger spatial configurations, with two main objectives being: Fulfil multiple performance goals within both Part to part (Local) relations, and part to whole (Global) relations. The ‘local’ conditions are met through a constraint-solving algorithm, while an AI agent is trained by continuously analysing and evaluating larger assemblages and learning to make improved local tile placement decisions resulting in configurations that better negotiate multiple global goals. This experimental proposal aims to challenge the rules of conventional architecture where the architect constructs a platform in which processed data is inputted as required criteria. It is based on these established criteria, which the artificial brain is trained for, to create modular, unforeseen architecture. Taking Mars as the eventual location for this proposal, selected measures are assigned to the model as minimum criteria to which this agent is being trained for: including Minimum area needed, proximity to energy collection zones, solar exposure, stability and connectivity.”

RC3

Chapter 01

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HOLISTIC DIAGRAM

Project Overview CRITERIA AREA SUN CONNECTIVITY

MARS

STABILITY RESOURCES DENSITY DEPTH

TILE BREEDS TILE BREEDS TILE BREEDS

archiGo ARCHI GO

LOCAL CONSTRAINT SOLVER

TILE BREEDS TILE BREEDS TILE BREEDS TILE BREEDS

PARTS OF SPACES

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JOINTS

LOCAL MEASURES

RC3

Chapter 01

General project overview: Taking Google’s AI program Alphago, which plays the board game Go, as an intelligent model of decision making, through firstly: implementation of partial spatial units within a constraint solving algorithm to generate larger habitable spaces, while inputting various global required criteria, to be achieved/optimised via a trained artificial brain.

LOCAL MEASURES

AGGREGATION

OPTIMIZED AGGREGATION

AGGREGATION

OPTIMIZED AGGREGATION

AGGREGATION

OPTIMIZED AGGREGATION

AGGREGATION

ML

OPTIMIZED AGGREGATION

AGGREGATION

OPTIMIZED AGGREGATION

AGGREGATION

OPTIMIZED AGGREGATION

AGGREGATION

OPTIMIZED AGGREGATION

DEPLOYMENT

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Concept References

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RC3

Chapter 01

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Concept Refrence: AlphaGo Inspiration

Project Overview

AlphaGo is the first computer program to defeat a professional human Go player, the first program to defeat a Go world champion, and arguably the strongest Go player in history. #Alphago Zero: In October 2017, The AlphaGo Zero paper was published in the journal ‘Nature’. Unlike the earlier versions of AlphaGo which trained on thousands of human amateur and professional games to learn how to play the game. AlphaGo Zero bypasses this process and learns to play the game of Go without human data, simply by playing games against itself.

Experts describe the program as “a significant step towards pure reinforcement learning in complex domains”. “We made this progress by streamlining the architecture behind Zero; we unite the policy and value networks into a single neural network and incorporate a simpler tree search that relies on this single neural network to evaluate positions and sample moves, without performing rollouts of the games. This can be thought of as using a single top-level professional to advise the system on its next move, rather than taking a crowdsourced answer from hundreds of amateur players. The simplicity of AlphaGo Zero’s architecture also dramatically speeds up the system while also lowering the amount of compute power it needs.”

AlphaGo

(Source: deepmind.com/research/alphago.)

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“We believe this approach may be generalisable to a wide set of structured problems that share similar properties to a game like Go, such as planning tasks or problems where a series of actions have to be taken in the correct sequence. Examples could include protein folding, reducing energy consumption or searching for revolutionary new materials.”

(Source: deepmind.com/research/alphago.)

Teams Outlook on the model: We strongly believe that since such approach could become generalisable to a wide set of structured problems that share comparable attributes to a game like Go, such as arrangement of tasks or problems where arrays of actions/decisions must be taken in the precise order.

Some examples listed by Deepmind researchers include: “protein folding, reducing energy consumption or searching for revolutionary new materials.” For us, such intelligent model of decision making could revolutionize the way in which processed data might one day form not only the steps of formation, but also the final architectonical product.

RC3

Chapter 01

Image Source: deepmind.com

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MACHINE LEARNING

Concept Refrence: AlphaGo Inspiration

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Project Overview

The use of intelligent computational algorithms implies the existence of a system that handles the production of built spaces. It also presumes that such system is capable of learning and producing continuously modified structures. It would be valid then to expect an outcome of highly responsive and adaptive structures when thinking of an architecture produced by AI. A debate that has been raised over 50 years ago by Nicholas Negroponte who presented to the world his idea about an architecture machine that can engage in partnership with the human designer to produce architecture.

The proposed ArchiGo system Uses a combination of algorithms to generate reconfigurable aggregations of spatial tiles, the reconfigurability aspect is a response to changes in the input parameters derived from either the context of where the structure is being articulated in, or from the constraints and needs defined by the architect.

This strategy brings both the human designer and the artificial intelligence to a symbiotic partnership to produce adaptable responsive spaces.

The symbiotic approach (between the user, the designer and the algorithm) re-examines the boundaries of the architect’s role , and suggest a possibility that the only way for the architect- in the future- to influence the design is by interacting with the system, the architect would then become a user, or rather an informed user.

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Chapter 01

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MONTE CARLO METHOD

Concept Refrence: Monte Carlo Method Inspiration

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Project Overview

Most machine learning algorithms rely on optimizing search tree algorithms , but one of the most revolutionary ones is the Monte-Carlo .

It was used to train an artificial intelligence to teach itself how to play win the board game Go which entails an infinite number of possible moves and outcomes .� Monte Carlo tree search (MCTS)11,12 uses Monte Carlo rollouts to estimate the value of each state in a search tree. As more simulations are executed, the search tree grows larger and the relevant values become more accurate.

The policy used to select actions during search is also improved over time, by selecting children with higher values. Asymptotically, this policy converges to optimal play, and the evaluations converge to the optimal value function�.

The idea is to grow a tree of possible moves (actions) and results (states) , where an AI agent is capable of predicting the outcome of it’s actions , driving its behavior towards a more desirable results and refining the results of the training outcome. archiGo has optimized similar strategy to teach a machine learning agent how to produce reconfigurable structures.

At each run of the training , an aggregation is produced , evaluated and stored within a search tree , the agent will iterate though this tree during the tile propagation and examines tile clustering similarities to avoid the repetition of clusters that leads to less appropriate aggregations.

Source: Mastering the game of Go with deep neural networks and tree search , David Silver

RC3

Chapter 01

Image Source: datacommunitydc.org

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CONSTRAINT SOLVING ALGORITHM

Concept Refrence: Constraint Solving Algorithm

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Project Overview

“Maxim Gumin’s WaveFunctionCollapse (WFC) algorithm is an example-driven image generation algorithm emerging from the craft practice of procedural content generation.

In WFC, new images are generated in the style of given examples by ensuring every local window of the output occurs somewhere in the input. Operationally, WFC implements a nonbacktracking, greedy search method.”

Source: Karth, Isaac, and Adam Smith. “WaveFunctionCollapse Is Constraint Solving in the Wild.” University of California Santa Cruz, Department of Computational Media.

The WFC being a crucial approach within archiGo’s system, allows for aggregation/ propagation of the spatial tiles, to generate larger habitable units. The tiles are continuously assembled following the constraints set, and moreover, when the algorithm is not executed until the end, the solution is invalid, and the algorithm requires a rerun; continuously producing a holistic model building with the parts it receives as input.

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Chapter 01

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Concept Refrence: Tiling System

Project Overview

Partial architectural building units are introduced as “tiles” to be implemented within the constraint solving algorithm to result in procedural content generation, applied to be interpreted architectonically.

TILING SYSTEM

An array of tiling research has been introduced and tested. Breeds start from the initial scale of: general building block units to finally; partial spatial units that assemble to generate spaces. While each “Part of space” or “Spatial Tile” is constructed to habitable architectural scale, it is not habitable on its own. Once connected to neighboring tiles, larger habitable spaces are generated.

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Overview of development of each breed is covered in following sections.

RC3

Chapter 01

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Research On Connectivity Rules

CONNECTIVITY LOGIC

Through the course of the year, various experiments were conducted to study methods of assembly and aggregation through logics of connectivity. These remote “connections/relations” eventually allow for magnitude of possibilities exchanged between the fragments of any system.

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Initial experimentations included: -Assembly of parts through packing platonic solids, -assigning constraint rules to physical parts while experimenting with materiality, aggregating larger constructs following the assigned steps/rules.

Further computational experiments: -Voxel constructs through CA, experimentation with implemented neighborhood/density, voxel generation age, and etc.. and study of each outcome, -Implementation of meshes within voxels to experiment with constraints along with previous rules set.

If these “connections/relations” are what (Tangibly and intangibly) link the all fragments together, they could potentially become the key means for controlling, evaluating, and altering all connected fragments in order to generate the desired whole, within any given system.

Project Overview

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Chapter 01

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Research On Connectivity Rules

GEOMETRY EXPLORATION [ 01]

Project Overview

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Starting with the simple triangular geometry, we split the unit in 6 (3*2) parts in order to define the places of connection.

By exploring rules of connections without materialization, we tried to establish different modes of connections within singular units.

Material explorations included the study of corrugated polypropylene to create a slotting mechanism in which the material is slit on one side of either units to allow notching of the units together. The previous combinatorics translated into this joint connection created a language and the behavioral search was set.

Later, by introducing reinforcing metal brackets, each unit gained 3d quality on its own, allowing for growth that is stronger before.

Following simple rules created by the geometry and material combined we explored various types of connectivities within the same pattern when enveloped.

RC3 C

Chapter 01

D

B

E

A

F

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Research On Connectivity Rules

Project Overview

Research Environmental Factors

Seed Image Study

Neighborhood Studies Introduction to new neighborhood connections.

Exploding local density rules.

By locally evaluating small changes throughout the rules and growth of GOL we were able to gain control over three main aspects of the code.

Exploding local age rules.

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Looking into localized rule boundaries of Neighbourhood Access, minimum and maximum Age and Density growth many variations of elaborate growing systems were created to represent intricate assemblies of GOL tables.

RC3

Chapter 01

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Research On Connectivity Rules

GEOMETRY EXPLORATION [ 02]

Project Overview

Unit Coding

Using mainly model prototypes and material studies we were able to find interesting and unpredictable results to transform an unlikely stable material such as a clear plastic tube in combination with wooden dowels to create coded units that are able to grown into various sizes of sheets that can connect to one and other to build a stable rigid growing system.

After researching various geometric forms and materials, plastic tubes caught our interest due to their geometric qualities and flexible qualities. By cutting the tubes in the center we were able to transform their shape into interesting geometries that can carry out possible growth and systematic qualities. Although we had reached a point were the tubes created interesting flexible transformable units there was no question that they were not able to structurally build and accurately connect to one and other due to their flexibility. That is why we introduced dowels as a rigid component to add structure.

A

A B

A B

B

B

BA AB

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AB

B A

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B A

B A

B A

B A

A

A

A

B

B

B

B A

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A

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B B

B A

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B

B A B

B A

A

RC3

Chapter 01

The logic of each unit is based according to their directionality and connectivity. Using the Type A and Type B units shown on the right we are able to create various groups of units that are able to connect together and expand their language. Through various tests and prototypes we were able to narrow down which assemblies are more successful to use in our system according to their weight durability and growing ability.

Moreover by looking into geometry and material research and joints we were able to create specific connection points using malefemale connections and sliding joints that can be fixed at a 90 degree angle to create accurate connections.

Moreover, by combining our tubes and dowels we were able to create simple but efficient units that can structurally build and grow using their connection points.

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Research On Connectivity Rules

Project Overview

Research Environmental Factors STEP 1

1 3 2

Connect Joint //Looking Up

STEP 2

STEP 5

Loop Joint Around Dowel //Create Triangle

Face Joint Looking //Top Triangle Joint

STEP 3

STEP 6

Add Final (3rd) Dowel To Joint

Connect & Clip //Final Dowel

STEP 4

STEP 7

Connect Final Joint to //Vertical Dowel

DONE!

Seed Image Study

Neighborhood Studies Introduction to new neighborhood connections.

Exploding local density rules.

Exploding local age rules.

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RC3

Chapter 01

By combining our tubes and dowels we were able to create simple but efficient units that can structurally build and grow using their flexible connection points.

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Research On Connectivity Rules

GEOMETRY EXPLORATION [ 03]

Project Overview

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By focusing on the fluid movement of an abstract objects through a space that is mapped with voxel boxes .The investigated objects evolved into a 3 distinguished types of multi-surface objects which compose the moving parts of the system. The basic system is composed of 2 main unites ( A and B ) , but both have mirrored versions ( A1 and B1 ) , the third unite is C which is used for establishing connection between the small area faces of unites.

By looking into geometry and material research and joints we were able to create specific connection points using male-female connections and sliding joints that can be fixed at a 90 degree angle to create accurate connections.

Moreover, by combining our tubes and dowels we were able to create simple but efficient units that can structurally build and grow using their connection points.

RC3

Chapter 01

The three basic rules for this system are : 1 Respecting the bounding box space : the movement is allowed only within a regular 3d grid of cubes that maps the virtual space where each unite is confined within a cube and the connection is possible through the faces aligned at the faces of the cube.

2 Unite “C” is used a joint : and only allowed to connect with small area faces of the unites type “A” and “B”.

3 Fce to face connection : the connection between

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Research On Connectivity Rules

Project Overview Research Environmental Factors

Type A

Spiral Behavior

Face to face connection.

Loop Behavior

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In order to create a bottom-up system using a specific coding language to arrange geometrically packed units in space, our initial goal was to find a way to blur the boundaries between lines and connection points in space.

The Final combination represents a possible answer for the question of how an abstract geometry can move within a geometrical grid which is used to map a virtual space following a certain set of rules, and growing system of elements.

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Chapter 01

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Research On Connectivity Rules

Interlocking Connection

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Project Overview

Face to Face Connection

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Chapter 01

Joint Connection

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Neighborhood Rules Research

NEIGHBORHOOD RULES [ 01]

Project Overview

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Initial experimentations included: -Assembly of parts through packing platonic solids, -assigning constraint rules to physical parts while experimenting with materiality, aggregating larger constructs following the assigned steps/rules.

Further computational experiments: -Voxel constructs through CA, experimentation with implemented neighborhood/density, voxel generation age, and etc.. and study of each outcome, -Implementation of meshes within voxels to experiment with constraints along with previous rules set.

RC3

Chapter 01

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Neighborhood Rules Research

Project Overview

Research Environmental Factors

Seed Image Study

Neighborhood Studies Introduction to new neighborhood connections.

Exploring local density rules.

By locally evaluating small changes throughout the rules and growth of GOL we were able to gain control over three main aspects of the code.

Looking into localized rule boundaries of Neighbourhood Access, minimum and maximum Age and Density growth many variations of elaborate growing systems were created to represent intricate assemblies of GOL tables.

Exploring local age rules.

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RC3

Chapter 01

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Neighborhood Rules Research

NEIGHBORHOOD RULES [ 02]

Project Overview

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Looking at methods of extending the research on Conway’s game of life we were able to manipulate and gain control over the growth and patterns of each running test. Our research on these behaviours was done through adding code extensions that created elaborate behaviours which we’ve highlighted through our research to understand the endless possibilities of CA.

RC3

Chapter 01

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Neighborhood Rules Research

Project Overview

Research Environmental Factors

Seed Image Study

Neighborhood Studies Introduction to new neighborhood connections.

Exploding local density rules.

By locally evaluating small changes throughout the rules and growth of GOL we were able to gain control over three main aspects of the code.

Looking into localized rule boundaries of neighbourhood Access, minimum and maximum Age and Density growth many variations of elaborate growing systems were created to represent intricate assemblies of GOL tables.

Exploding local age rules.

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RC3

Chapter 01

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Concept Refrence: Case Studies

Project Overview

Kisho Kurokawa Nakagin Capsule Tower 1972

Kengo Kuma GC Prostho Museum Research Center 2012

Image Source: archdaily.com

Image Source: notey.com

Image Source: archdaily.com

Japan’s Metabolist structures; one of the earliest forms of experimentation with adaptability and growth.

Cidori is an assembly of wood sticks with joints having unique shape, which can be extended merely by twisting the sticks, without any nails or metal fittings.

A pavilion that extends beyond its gridded boundaries using a traditional Japanese joinery system.

CASE STUDIES: JOINTS 52

Sou Fujimoto Serpentine Pavilion 2013

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Osaka Expo 1970

Chapter 01

Osaka Expo 1970 Image Source: archpaper.com

Connectivity using discrete yet flexible units that define architectural typologies using a continuous frame work.

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Concept Refrence: Case Studies

Project Overview

Arata Isozaki City in the air 1962

The Fiction of the Oblique The Architecture of Claude Parent & Paul Virilio 1963 - 1969

Image Source: theartstack.com

Image Source: the function of the oblique book

This Urban project elaborates on resolving density issues using a core system to reformulate a new modern social structure.

Investigating a new kind of architectural and urban order that rejects traditional axes of the horizontal and the vertical to create an architecture of disequilibrium in an attempt to shift to a new dynamic architecture.

CASE STUDIES: GEOMETRY 54

RC3

Chapter 01

Paul Rudolph Lower Manhattan Expressway 1970

Peter Cook, Archigram The Plug-In City 1960

Piet Blom Cube House Rotterdam 1984

Image Source: curiator.com

Image Source: archdaily.com

Image Source: archdaily.com

Rudolph’s Y-shaped corridor suggesting a new way of Geometrical expression to keep infrastructure intact and suggest a new approach to city building.

Combining architecture, technology, and society to introduce a new approach to urbanism through its new geometrical expression of spatial organization.

Slanted and angled roofs give an interesting expression to new Interior and Spatial design.

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Concept Refrence: Case Studies

Project Overview

Buckminster Fuller Fly’s Eye dome 1980

Manuel Dominguez Nomadic City 2013

Image Source: archdaily.com

Image Source: dezeen.com

New future of architecture and living using lightweight mathematical structural framework.

Traveling city that maintains its connection using horizontal and vertical system.

CASE STUDIES: EXTREME ENVIRONMENT 56

RC3

Chapter 01

Ekuan and Tange Masterplan for the Pilgrim accomodation for Mina 1975

Peter Cook Living Pod 1966

Eero LundĂŠn and Juulia Kauste Nordic Pavilion 2018

Image Source: Project Japan Metabolism Talks Book

Image Source: hiddenarchitecture.com

Image Source: dezeen.com

A planning project aiming to resolve density issues and connectivity within an extreme environment.

Living system that operates over living pods connecting to adapt to extreme environments.

Extending and expanding units that define new ways of organizing and extending architectural spaces.

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CHAPTER TWO | COMPUTATIONAL RESEARCH

0.2

Wave Function Collapse

Computational Research

Tiles creating the model

Wave Function Collapse

The research was driven by exploring different connections, local rules, and how they can be encoded in algorithm. In the project itself, to assure these criteria will be considered, the architectural system is propagated through a constraint solving algorithm based on the quantum mechanics phenomenon – Wave Function Collapse (Karth & Smith, 2017). The algorithm is iteratively build based on the input provided - the tile set. It is encoding the neighborhood constraints into a model for propagation. The algorithm itself guarantees us a strong part to part relations because it is greedy solving the constraints without backtracking. The propagation is starting from a random point in the graph assigned by the algorithm, building on further on the tile with the smallest domain – with domain shrinking and expanding based on the constraints of the local neighborhood of the tile itself. The constraints of the model are hiding genetic code of the architecture within. Manipulating its order is what

CONTRADICTION FOUND !

creates the mutant. However, the criterion of growing in the direction of the smallest domain is sometimes too arbitrary, meaning that at certain points again there will be a random tile picked from the subset of vertices with this status.

Smallest domain Contradiction Found

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Chapter 02

Resetting the Model

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Wave Function Collapse

Smallest Domain

Computational Research

Tiles creating the Model

Contradiction Found

If the constraints were met

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Chapter 02

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Wave Function Collapse

Computational Research

equal domain possibilities

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Chapter 02

propagation scenario

Contradiction Found on the last position

propagation scenario

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Wave Function Collapse

Having explored the propagation rules, it is notable that having bias choices can determine if the propagation will collapse or not. However having the exact same constraints can also give some qualities to the system.

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Computational Research

RC3

Chapter 02

Not having bias constraints can cause some global quality of the model to fall back.

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GRAPH STUDY

Graph Research

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Furthermore, to build the spatial model, in order to get most of it, the model is propagated through truncated octahedron, taking advantage that it allow us to grow in all Cartesian directions, assigning a unit - tile on each vertex.

Since the architectural model is built on a digraph, it is easy to bring top-down analysis on how many closures, normals, depths and isolated vertices the model has. Through separating different connection labels, some generations of tiles, and the models they build, are evaluated as too dense, which lowers the value of other criteria (as sun exposure evaluated through particle collision).

Based on this information, we were feeding back the next breed of tiles accordingly, but what if we used this information to train a machine learning agency to make this decisions for us? The vast combinatorics how to use these constraints within the architectural model could in fact benefit from intelligent decision making.

By merging the two systems into one, we can rely that the structure will maintain its enclosures, as well as continuity in order to maintain safety, and comfort levels within the clustering of the spatial zones.

Computational Research

RC3

Chapter 02

+Z

X, Y, Z X-1, Y-1, Z

X, Y, Z-1 X-1, Y-1, Z-1 -Z

Building the Truncated Octahedron graph, making all the 14 combinations of Cartesian coordinate system abstract.

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Graph Research

First, we explored the behaviors we can get from the constraints, building a set of tiles that is using 2 constraints per tile, each pair having one primal and one secondary

archiGO ALGORITHM

neighbor. This opened a wide range of

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vertical behaviors, almost like staircases. Also, they are very likely to form loops.

Computational Research

RC3

Chapter 02

Adding tiles connecting only the two primal neighbors allowed for the extension of these behaviors.

However, this behavior is only growing linearly, losing connectivity qualities within the system.

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Graph Research

Introducing tiles that are constrained to have 3 neighbors rather than 2 is allowing for better connectivity.

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Computational Research

RC3

Chapter 02

Introducing tiles that are constrained to have 3 neighbors rather than 2 is allowing for better connectivity.

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CHAPTER THREE | TILE RESEARCH

0.3

Tiling System

Tiling Research

In this approach, each tile is a part of space that is not necessarily utilisable on its own, but once connected together, they generate larger/habitable units that assemble based on the program needed. Here, the tiles become “spatial joints” that link the whole system, while also performing as spatial units themselves.

Initial experimentations of tiles begin as building blocks/units that assemble to generate larger aggregations, through a course of development, the final tile breeds perform as parts of spaces, which once connected, create habitable spaces, generated for variety of tasks.

While each “Part of space” or “Spatial Tile” is constructed to habitable architectural scale, it is not habitable on its own. Once connected to neighboring tiles, larger habitable spaces are generated. These spatial joints are the core spatial fragments of a system that is Continuously shape shifting with certain constraints and degrees of freedom to generate Varied spatial arrangements, linked together with cause and effect of consecutive generative actions.

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Chapter 03

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Concept Refrence: Constraint Solving Algorithm

Tiling Research

+

=

Machine Learning Breed 02 Tiles

The virtual truncated octahedron is used as a vessel to transfer geometrical entities thorough space to produce spatial continuity .

A space filling polyhedron which is the truncated octahedron is used to contain group of surfaces that would together form a spatial tile , and when connected to other tiles articulate continuous space.

.Number of loose tiles : 6 .Total floor area: 432 m2 .Total number of tiles : 18

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.Number of loose tiles : 2 .Total floor area: 744 m2 .Total number of tiles : 18

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Chapter 03

.Number of loose tiles : 7 .Total floor area: 1029 m2 .Total number of tiles : 25

.Number of loose tiles : 4 .Total floor area : 980 m2 .Total number of tiles : 22 The WFC algorithm is used to generate aggregations with different tile sets , the resulting aggregation is not optimized against criteria such as area or stability.

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Concept Refrence: Constraint Solving Algorithm

Machine Learning Breed 02 Tiles

Tile Mutation

.Number of loose tiles : 4 .Total floor area: 876 m2 .Total number of tiles : 28

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Tiling Research

.Number of loose tiles : 3 .Total floor area: 988 m2 .Total number of tiles : 23

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Chapter 03

Tile Mutation

.Number of loose tiles : 2 .Total floor area: 874 m2 .Total number of tiles : 21

Tile Mutation

.Number of loose tiles : 2 .Total floor area: 763 m2 .Total number of tiles : 19

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Machine Learning Breed 02 Tiles

Concept Refrence: Constraint Solving Algorithm

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Tiling Research

.Number of loose tiles : 1 .Total floor area: 966 m2 .Total number of tiles : 18

.Number of loose tiles : 2 .Total floor area: 852 m2 .Total number of tiles : 22

.Number of loose tiles : 1 .Total floor area: 977 m2 .Total number of tiles : 24

.Number of loose tiles : 3 .Total floor area: 683 m2 .Total number of tiles : 17

RC3

Chapter 03

.Number of loose tiles : 1 .Total floor area: 520 m2 .Total number of tiles : 13

.Number of loose tiles : 1 .Total floor area: 596 m2 .Total number of tiles : 23

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Tile Breeds

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Tiling Research

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Chapter 03

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SPATIAL TILING RESEARCH

Spatial Tiling Research

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Tiling Research

Initial experimentations of tiles begin as building blocks/units that assemble to generate larger aggregations, through a course of development, the final tile breeds perform as parts of spaces, which once connected, create habitable spaces, generated for variety of tasks.

While each “Part of space” or “Spatial Tile” is constructed to habitable architectural scale, it is not habitable on its own. Once connected to neighboring tiles, larger habitable spaces are generated. These spatial joints are the core spatial fragments of a system that is continuously shape shifting with certain constraints and degrees of freedom to generate varied spatial arrangements, linked together with cause and effect of consecutive generative actions.

Spaces generated include larger interior spaces for intended for communal/collaboration spaces, smaller interior spaces intended for personal spaces, circulation spaces, exterior terraces/walkways, and more.

The constraints and tiles design work in synergy to achieve the necessary spatial needs; for example, all tiles include accesses to neighboring tiles, all tiles allow for access to circulation spaces wither on their own, or through access to consecutive tiles, openings for adequate sun exposure, and more.

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Tile 0: 25 Tile 1: 1 Tile 2: 15 Tile 4: 16 Tile 7: 10 Tile10: 13 Tile12: 10 Tile13: 6 Total Floor Area: 3110 M2 Sun Exposure Est: 78%

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ASSEMBLY 04 ASSEMBLY 02

Tile 0: 20 Tile 2: 1

Tile 1: 4 Tile 3: 7

Total Floor Area: 232 M2 Sun Exposure Est: 68%

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Tile 0: 23 Tile 1: 12 Tile 2: 1 Tile 3: 13 Tile 8: 1 Total Floor Area: 460 M2 Sun Exposure Est: 64%

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ASSEMBLY 04

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Tile 0: 31 Tile 1: 9 Tile 2: 2 Tile 5: 8 Tile 13: 8 Tile10: 9 Total Floor Area: 2100 M2 Sun Exposure Est: 76%

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0.4

Tile Particles Particle

Fabrication

Within fabrication, each tile set is broken down to smaller discrete components which then have local joinery connections. Selected elements therefore join to feeds into the whole system to create a larger connectivity system.

FABRICATION

Based on previously explained tile breeds, each tiles is made up of vertical and horizontal pieces that are removed and combined at any given moment according to necessary requirements derived from the algorithm model.

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Each component is made up of hybrid female to male joinery connections that are derived from the connectivity logic which it based on the algorithm.

In the process of assembly, these smart pieces are initially flat packed and sent to site. After further investigation and local site evaluation further decisions are made for extra components based on additional necessity need of the elements.

In addition to single units the robot is given recognizable QR codes for each tile set to further optimize the assembly.

And Finally, the hybrid combination of these panels ensure overall connectivity within the system.

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TILES + PARTICLES

Fabrication

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Illustrating the combination of various tile components within each tile that are reassembled to feed into the initial tile design.

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Joinery Studies

Fabrication

JOINERY STUDIES

Initially smaller block shaped pieces were studied to follow intricate discrete positive and negative joint systems were one pieces is design to feed into the next in a clear order.

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At each step of the study the joints of the elements were improved to feed into a easier connectivity system.

In addition to their connectivity logic the geometrical shape of the pieces allow for an easier reconfigurability and adjustment system which also allows the blocks to easily slide and ride on each to reform and define various directionality options.

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JOINERY CONNECTIONS

Joinery Studies

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Fabrication

PHASE 1

PHASE 2

PROFILE GROOVE

ANALOGUE LAYERS

Each individual material study beginning from phase 1 to phase 5 evaluates the strength, accuracy, design and connectivity of the fabricated blocks.

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PHASE 4

Chapter 04

PHASE 5

Phase 2 & 3 are made up of layers of cut wood pieces that combine to make the two units that can join together to hold 1-4 metal rods in between.

Phase 4 & 5 are more intricate layers of CNC parts that combine t make two versions of the latest prototype design of the units that can hold the same amount of rods and also join face to face using tong and groove joinery. Due to the precision and high accuracy level of milling the blocks are much more stronger once joined together.

CNC SET NO. 1

CNC SET NO. 2

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After evaluating each prototype at each stage we were able to increase the strength and ability of each unit at each consecutive stage to promote the qualities of the particular type of wood.

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REVISED JOINERY CONNECTIONS

Joinery Studies

128

At this stage the joints and connections on each spatial element were revised to fit into new joinery connections which combine to complete the spatial design.

Fabrication

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Joinery Studies

Connection Studies:

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Joinery Studies

Window Studies:

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Fabrication Research

Fabrication

End Effecor & Fabrication Research

Designing Gripper

Testing its Picking and Placement Measurements

Counter Crafting Robotic Construction of Lunar and Martian Infrastructure.

3D Printing in Space

On Site QR Scan

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Robotic Fabrication As an research on using the robot arm to run a simulation to pick and place our blocks we were able to grasp an understanding on the important criteria that will operate the assembly steps to comlete the selected model.

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Prototype Research

Fabrication

Connectivity and assembly studies highlighting possible vertical connections and possible directional growth.

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Prototype Research

Fabrication

Prototyping architectual panels with joint details according to designed model.

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Pick and Place studies with Robot arm to achieve resolved assembly organization.

Chapter 04

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Tiles + QR Scanning

Fabrication

Vertical & Horizontal Smart Component

FLAT PACKING

Vertical Smart Component

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For deployment the panels are intended to be flat packed and sent to site to optimize building strategies for the initial arrival to the chosen extreme site.

QR CODES

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Each tile combination and single component is assigned with provided QR codes to enhance building steps and to allow immediate recognition to the given task for assembly.

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Fabrication

QR CODE SELECTION

Tiles + QR Scanning

s On

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Robot Scanning Tiles & Pieces During Assembly

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Supporting Rods Added for Support Based on Structural Annalysis

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Robot Adding More Pieces Based on Tile Recognition

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PROCESS

Deployment

144

A study on fabricating components and prototyping for studies on how to improve the fabrication process of components. Each element can join to complete the designed model derived from the tile breeds. And necessary cladding is added to ensure protection from local environmental conditions.

Fabrication

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Combination of Spatial Configuration

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Discrete Connectivity Studies

Image describing two tiles that have combined to enlarge interior spaces.

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Discrete Connectivity Studies

Fabrication

Studies on various assemblies of the components and pieces to explore further spatial development.

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CHAPTER FIVE | MARS RESEARCH

0.5

MARS: THE PLATFORM FOR INITIAL RESEARCH

Mars Research

While architecture, when entwined with engineering, has always been about certainty, considering environments like that of Mars as the setting of our architectural proposal, evokes a deep sense of uncertainty. This, generates an enormous need for adaptation and reconfiguration, achieved through not only fusion of magnitude of multidisciplinary approaches, but also controlling, manipulating and stretching the relations between parts to generate more globally optimized wholes.

While this experimental proposal could be applicable to an array of environments, Mars is taken as an example of an extreme environment in which certain minimum criteria need to be met for the system to performative. Here, several criteria are set as minimum requirements for the system to work towards achieving.

These include: Overall stability, Connectivity of parts, adequate Sun exposure to all spatial clusters, proximity to energy collection zones where necessary, and minimum floor area needed for each program/task.

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Mars Research Curiosity Rover Tweets

MARS ENVIRONMENT

One of the primary motives for exploring Mars was to look for life possibility in the outer space, the threshold where science fiction meets reality was proved finally by solid evidence.

Despite its dry extreme environment, photos and samples retrieved from Mars surface proved after extensive analysis the existence of water on Mars at a distant point in the past. Scientists have studied patterns of rock formations and concluded that in many areas on the surface of Mars, how the sand grains in some locations were piled and solidified in layers, and the fact that they contained minerals, could be explained only by water existence. They also examined rocks which had cavities that usually found on earth rocks when minerals dissolve into water leaving vacuum.

Finding Iron in Hills July 2017

Ice Water Crystals in Mars Clouds August 2017

Many exploration missions was then able to discover thin layers of ice sheets berried inches beneath the surface, NASA in 2016 declared that eight different locations could be labeled as possible water reservoir.

Close observation to geographical features such as mountains and meadows also confirmed the water flow theory, such updates and photos were attained thanks to robots sent to Mars like Curiosity and Opportunity. They played a major role in advancing the pace of exploring mars surface by collecting samples, analyzing them, and sending images back to earth for examination, not to mention live tweets to engage the public with the exciting new findings.

Radiation Analysis Level X2 After Storm September 2017

Active Rover With Improved Algorithm June 2017

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Mars Research

Mars Research Environmental Factors

OLYMPUS MONS

AMAZONIS PLANITIA

UTOPIA BASIN

ARABIA TERRA THARSIS MONTES

HELLAS BASIN

ARGYRE BASIN VALLES MARINERIS

ELYSIUM MONS

NOACHIS TERRA

Map of Mars Environment Terrain and Poles

Universe Today, 16 Dec. 2016, www.universetoday.com/14859/ gravity-on-mars/.

Choosing all of mars as a possible location for research and finding resources.

Mars Gravity: 3.711 m/s² // the effects of long-term exposure to gravity that is just over one-third the Earth normal will impact future habitation plans.

NASA, www.grc.nasa.gov/www/k-12/airplane/atmosmrm.html.

Mars Atmosphere is the layer of gases surrounding Mars. //The atmospheric pressure on the Martian surface averages 600 pascals.

Breaking Science News | Sci-News.com, www.sci-news.com/space/mars-twisted-magnetotail-05344.html.

Mars No Magnetic field. // Only scattered “fossil” Magnetic field in various areas.

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Mars environment is of the rough and unstable kind, sand storms could envelope large huge areas of the surface, high levels of radiation due to its very thin atmosphere, 100 times thinner than the earth’s, which is largely composed of carbon dioxide.

The average temperature is -60 Celsius with the exception of a comfort zone at the equator of 20 Celsius.

Many speculation were placed as to how Mars lost its atmosphere, one of the most compelling hypothesis that its light gravity and its lack of magnetic field made it vulnerable to solar wind and particles which eroded and thinned the atmosphere over the course of millions of years.

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Chapter 05

Mars Radiation Proof Material Study

Due to high risk of radiation exposure on Mars, a solution for habitation must provided to prevent harm to humans on mars.

Throughout the entire trip, astronauts must be protected from two sources of radiation. The first comes from the sun, which regularly releases a steady stream of solar particles, as well as occasional larger bursts in the wake of giant explosions, such as solar flares and coronal mass ejections, on the sun.

Polyethylene Plastic (Plexi Glass) THE MOLDING BLOG, www. themoldingblog.com/2016/03/10/ plastics-may-replace-aluminum-in-firstflight-to-mars/.

// Very flexible material // Low tensile strength

Hydrogenated BNNTs (Plastic/Tape or Yarn) S. Lashmore, David. “Revolutionary Boron Nitride Nanotube Fiber and Yarn�CEOCFO Magazine, 22 May 2017

// Each thread of the Mesh: 3 to 50 nanometers. // Heat resistant. // Very strong and flexible.

These energetic particles are almost all protons, and, though the sun releases an unfathomably large number of them, the proton energy is low enough that they can almost all be physically shielded by the structure of the spacecraft.

Possible Risk of Radiation exposure on Mars: I. Acute radiation syndrome (ARS) can be caused by intense solar particle events Aluminum

II. both acute (i.e. short-term risk of radiation sickness) and late (e.g. cancer) effects are possible.

THE MOLDING BLOG, www. themoldingblog.com/2016/03/10/ plastics-may-replace-aluminum-in-firstflight-to-mars/.

// Very weak. // Common for previous use but not so much now.

III. Late radiation morbidity is associated with the chronic exposure to galactic cosmic radiation (GCR)

ICE and Water NASA, 13 Dec. 2016, www.nasa.gov/ feature/langley/a-new-home-on-marsnasa-langley-s-icy-concept-for-living-onthe-red-planet.

IV. CNS and cardiovascular diseases cancer effects are possible.

// Clear Coat. // Fragile.

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Mars Research

SOIL European Space Agency, www. esa.int/Our_Activities/Space_Engineering_Technology/Testing_Mars_ and_Moon_soil_for_sheltering_astronauts_from_radiation.

// Easy to use. // Quick fix. // Not very resilient.

Polysilazane Coating Hu, Longfei. “Surface and Coatings Technology.” Journal - Elsevier, ScienceDirect, www.journals.elsevier.com/ surface-and-coatings-technology/.

// Int-corrosion & Tarnish. // Gas Barrier Coating //Clear Coat. //Strong. // Lasts in high Temp.

Artificial Magnetosphere ScienceAlert, www.sciencealert.com/ nasa-wants-to-launch-a-giant-magnetic-shield-to-make-mars-habitable.

// Uses high level of resources therefore difficult to create.

Liquid Hydrogen Center, Goddard Space Flight. “Home.”

Tech Briefs, 1 Feb. 2017, www. techbriefs.com/component/content/ article/tb/techbriefs/manufacturing-prototyping/26330.

// Very good material but poor structural quality.

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Mars Radiation Proof Material Study

In recent research done by NASA, they state that “The space radiation environment will be a critical consideration for everything in the astronauts’ daily lives, both on the journeys between Earth and Mars and on the surface,” said Ruthan Lewis, an architect and engineer with the human spaceflight program at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. “You’re constantly being bombarded by some amount of radiation.”

Radiation, at its most basic, is simply waves or sub-atomic particles that transports energy to another entity - whether it is an astronaut or spacecraft component. The main concern in space is particle radiation.

Energetic particles can be dangerous to humans because they pass right through the skin, depositing energy and damaging cells or DNA along the way. This damage can mean an increased risk for cancer later in life or, at its worst, acute radiation sickness during the mission if the dose of energetic particles is large enough.

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ORION’S PRESSURES VESSEL

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Mars Habitat Unit When designing a space habitat, the internal planning of the unit will have to tested against a number of parameters such as privacy and physiological comfort. Because of the limitations imposed by the harsh nature of the outer space and the fact the resources will be at scarce and everything will have to be considered at minimum without compromising basic human living conditions.

Modularity is a basic feature in both envisioned and realized examples of the outer space habitat unites, NASA has produced many studies and prototypes that satisfies different conditions and needs for the purpose of exploring extraterrestrial planets. We could observe that most of them has the same basic body structure but the possibility to be equipped to house various functions such as Laboratory, Storage, Habitat and ect.

Mars Research

Mars Habitat Human Living Space

Habitat Demonstration Unit Source: nasa.gov

Sections of living units Source: phys.org

The shape of the unit itself should serve either a vertical growth scenario ( such as a cylinder), a rarely used approach, or a horizontal expansion such as the barrel shape, the most common one.

Usually those units are composed of solid parts at the ends where a hatch is fixed to ensure a stabilized pressure is maintained inside the unit, and inflatable parts to reduce the weight and the size and consequently the transfer cost via rockets or separate rocket mounted launcher that gets activated when the spaceship approaches the surface of the planet.

Units could be grouped to form a connected colony that shares basic resources such as Oxygen which are provided by a special type of another prefabricated unite called LSS( Life Support System) that has a number of per-installed system to recycle water, oxygen and to maintain a balanced levels of gases in the internal atmosphere as well as temperature.

Life Support Scheme Source: nasa.gov

Modular Habitation System Source: liquifer.com

Modular Pressure Vessel System Source: revolvy.com

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Source: The Martian, Movie Still

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Case Studies: Space Architecture

Mars Research

Fosters and Partners 3D printed Mars Habitat 2015

Fosters and Partners Lunar Habitat 2012

Image Source: dezeen.com

Image Source: fosterandpartners.com

Envisioning a habitat constructed from regolith, the loose soil and rocks found on the surface of Mars to 3D print over the arriving pods for protection.

“The practice has designed a lunar base to house four people, which can offer protection from meteorites, gamma radiation and high temperature fluctuations.�

(Fosters and Partners)

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Clouds Architecture Office Mars Ice House 2015

Space Factory MARSHA 2018

SEArch+/Apis Cor 3D Print Habitat 2018

Image Source: nasa.gov

Image Source: archdaily.com

Image Source: designboom.com

adopting to a vertically-orientated cylinder, that results in a series of spatial and efficiency studies to redefine mars habitation.

A habitat that carefully considers its position and shape to maximize light whilst protecting against radiation exposure. (designboom.com)

“Mars Ice House was designed to bring light and a connection to the outdoors into the vocabulary of Martian architecture.� (marsicehouse.com)

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AIR LOCKS

Sophisticated items like airlocks, and pressure doors would have to be imported from earth until Mars developes a mature manufacturing capability. By coordinating with the design of the interplanetary capsules most of the necessary items can be salvaged from the landing craft, with the exclusion of the initial landing base, which should be preserved as part of history.

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“The Joint Airlock has two main components: a crew airlock from which astronauts and cosmonauts exit the ISS and an equipment airlock designed for storing EVA gear and for so-called overnight “ campouts” wherein Nitrogen is purged from astronaut’s bodies overnight as pressure is dropped in preparation for spacewalks the following day.

This alleviates the bends as the astronauts are repressurized after their EVA.” Source: spaceref.com

Mars Research

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Refrence: https://marsbase.org/section11

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ENVIRONMENTAL CONTROL

Life Support System

ENVIRONMENTAL MONITORING

ATMOSPHERE MANAGEMENT

FLUID INFRASTRUCTURE SYSTEM

WATER MANAGEMENT

Refrence: NASA, LIFE SUPPORT OVERVIEW

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Chapter 05

. MONITORS AND CONTROLS PARTIAL PRESSURES . MAINTAINS TOTAL PRESSURE . MAINTAINS TEMPERATURE AND HUMIDITY

. PRODUCE OXYGEN . REMOVES CARBON DIOXIDE . FILTERS FOR PARTICLES AND MICROBES . REMOVES VOLATILE ORGANIC GASES . DISTRIBUTES CABIN AIR (VENTILATION)

. INTEGRATING INTO AN ARCHITECTURAL LANGUAGE

. RECYCLES WASTEWATER . STORES AND DISTRIBUTES POTABLE WATER . USES RECYCLED WATER TO PRODUCE OXYGEN

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CRITERIA FOR EVALUATION: LSS

166

Mars Research

ALGAE GROWTH

PROTON EXCHANGE

SOLID WASTE PROCESSING

THERMAL CONTROL

AIR REVITALIZATION

LIFE SUPPORT SYSTEM

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Chapter 05

“Life Support Systems (LSS) activities develop the capabilities to sustain humans who are living and working in space - away from Earth’s protective atmosphere and resources like water, air, and food. This includes monitoring atmospheric pressure, oxygen levels, waste management, and water supply, as well as fire detection and suppression.

Existing life support systems on the ISS provide oxygen, absorb carbon dioxide, and manage vaporous emissions from the astronauts themselves.

Addressing any gaps will make life support systems more reliable and effective, which will lead to integrated testing on Earth and ISS in preparation for future human spaceflight missions deeper into the solar system.”

Important architectural criteria are: “I. Define life support system architectures for different space mission classes.

II. Assess life support system technologies.

III. Perform life support systems integration.

IV. Define and monitor life support system testing.

V. Develop an integrated life support system model that will be used to understand life support system dynamics and the potential impacts of new technology infusion.

VI. Study reliability, maintenance, and crew time requirements of the state-of-the-art subsystems to understand which subsystems require technology development to meet exploration requirements.”

URINE AND WATER PROCESSING

Source: nasa.gov

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CRITERIA FOR EVALUATION: Sun Analysis

Mars Research

SUMMER SOLSTICE

EQUINOX

25°

WINTER SOLSTICE

25°

N

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LOCAL SUN CRITERIA

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Chapter 05

The sun exposure is used as another evaluation criteria for optimizing the resulting aggregations. The tile placement is influenced by the direction and the desired amount of sun expos ure.

For interior spaces, shielded windows have been proposed to provide natural lighting for interior spaces, the size and placement of windows on the tile surfaces is the result of several sun simulation tests to prevent over exposure and risk of high levels of harmful radiation, while at the same time allow for similar to earth interior environments.

Other spaces such as the crops growing (green spaces) are on the contrary in a need of higher levels of natural lighting , thus their tiles are subject to different values when being evaluated for sun exposure.

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CONNECTIVITY

CRITERIA FOR EVALUATION: Machine Learning Criteria

170

Mars Research

One criterion introduced is connectivity of both: the tiles to neighbouring tiles (this also results in more stability, which itself is another criteria) but also spatial connectivity through the tiles. As the course of training of machine learning agent develops, the results become more optimised in this factor.

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Chapter 05

MODEL 4

MODEL 1

MODEL 2

MODEL 3

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Mars Research

FLOOR AREA

Another introduced requirement/ criterion is floor area. Through assembly of these spatial tiles, required floor area is achieved.

The users/architect input the minimum required area for each program, and as the tiles aggregate, the area is calculated, while the ML Agent is continuously adapting and learning to better evolve the aggregations to achieve this requirement.

MODEL 1

MODEL 2

MODEL 3

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LAB 200 m2 MEDICAL 38 m2 MEETING 30 m2

SOCIAL

LFS 200 m2 STORAGE 130 m2 EVA 75 m2

WORK

KITCHEN 50 m2 WASHROOM 45 m2 GYM 40 m2

DOUBLE PERSONAL SPACE 97.5 m2

SINGLE PERSONAL SPACE 65 m2

MAINTENANCE

GREEN HOUSE

GREEN HOUSES

Chapter 05

DINNING 90 m2 ENTERTAINMENT 75 m2

2800 m2

CIRCULATION: 15%

TOTAL POPULATION: 30 PEOPLE

PRIVATE

SOCIAL

WORK

MAINTENANCE

GREEN HOUSE

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CHAPTER SIX | archiGO

0.6

ArchiGo Algorithmic Concept

Machine Learning

176

archiGO

Machine Learning takes the use of computational algorithms a step further by optimizing the ability of statistical and search algorithms to develop an intelligence, an ability to reason about solutions for problems that are ill-defined and often challenging to humans due to their complexity. It’s also about building a general model that can process large data quantities, classify them in a comprehensive manner, and infer knowledge or solutions via machine learning.

This presents an opportunity for architects to experiment with this technology when confronted with unusual building scenario such as Mars, where the structure should be considered for a highly fluctuating and unstable environment. The ArchiGo model has been developed to take inputs from both the environment and the user, where it processes a set of predefined discreet spatial parts into a coherent structure that optimizes both the constraints derived from the mars environment and the user requirements defined by the architect. The ArchiGo model uses an AlphaGo search like method that allows for predictive problem solving by back tracking through a search tree. The search tree is made up of matched states and outcomes and continues to grow and branch as the solver progress through learning iterations.

The overall model was a hybrid of WFC algorithm , typical machine learning algorithms , and the adaption of Mont Carlo tree search used in AlphaGo . The WFC algorithm is able to solve a given space and fill it with tiles based solely on constraints defined by the tile geometry , the resulting aggregations is a combination of tiles that lacks architectural qualities , the tile selector within the WFC algorithm infers a range of possible tiles to be placed at each vertex based on the neighboring tiles constraints and randomly selects a tile . When Machine learning was used , a virtual value is added to refine the tile selections process called the “tile weight “ , it indicates the probability of the tile to be selected when having a higher value for the weight , the agent training then is designed to stimulate the selection of tiles that produces aggregations with more desired architectural qualities by favoring them during the selection process via assigning higher weight values .

At each run of the learning episode an aggregation is produced by the WFC solver , where tiles violating parameters such as displacement and density are labeled as weak tiles and their weights are consequently reduced . This type of evaluation is limited in producing optimized aggregations because it does not fully allow the ML algorithm to associate desired values with the types of proper tiles that can produce such aggregations , the evaluation happens based on global assessment of the whole aggregation regardless of the local tile clustering behavior that influences how the solver propagate .

With the implementation of the tree search , the agent could track back through previous aggregations ( comprehensive data classification ) and makes predictive decisions by avoiding the repetition of tile placements that leads to undesired aggregations. The training is designed to award the agent based on the overall aggregation quality produced by the agent , where it takes over the selector role in the WFC algorithm . The aggregation is evaluated against three main aspects at each run : the stability , the density , and the required area . The accumulative value of these criteria will decide the aggregation quality of which the agent is rewarded based on.

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Chapter 06

ML Layers: Complexity

36 Layers: 65 elements per Layer

Number of Criteria

36 Layers: 50 elements per Layer 28 Layers: 25 elements per Layer

Grid Size

2x2x2

2x3x3

2x3x3

3x3x3

3x4x3

3x4x4

The training environment is devised to encourage associations between goals and states to be formed. At the beginning of the training episode the agent observes initial values such as the required area , the density constraints , the stability constraints , .. etc.The agent will start processing the tile placement but with tracking back through the tree of previously stored aggregations , the agent will iterate through each one and compare between the tile neighborhood being processed for tile selection and any similar neighborhood situations in previous aggregations , if a match is found , the agent moves to check whether the central tile is available in the domain of possible tiles and reduces its weight if it was labeled as a weak tile in that aggregation and vise versa if not . This minimizes the opportunity of bad tile clustering that leads to bad aggregation to be produced again.

The training is by itself conducted with the use of artificial neural networks where a number of hyperparameters (parameters that cannot be directly linked to the training scenario) influence the training quality . When the number of those artificial neural networks and elements designated in each of those layers is increased ,the training quality and results is significantly enhanced.

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Machine Learning

archiGO

W=3

W=5

W=5

[

[

Starting with the simple triangular geometry, we split the unit in 6 (3*2) parts in order to define the places of connection.

ML

Domain of Possible Tiles

By exploring rules of connections without materialization, we tried to establish different modes of connections withing singular units.

By exploring rules of connections without materialization, we tried to establish different modes of connections withing singular units.

Iterate Through Previous Aggregations

Allowing the archiGo agent to find matching neighborhood states in previous aggregations

// W = Tile Weight

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Compare the current tiles availble in the domain with central tile in each of those domains

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Chapter 06

Starting with the simple triangular geometry, we split the unit in 6 (3*2) parts in order to define the places of connection.

By exploring rules of connections without materialization, we tried to establish different modes of connections withing singular units.

By exploring rules of connections without materialization, we tried to establish different modes of connections withing singular units.

With WFC algorithm tiles do not have wights and the tile selection for each vertex is random.

Unoptomized aggregation In typical machine learning agent there is no tree to iterate through and the tile weights are changed based on evaluation of global criteria rather than local conditions.

Globaly optimozed aggregation W=4

W=2

W=4

If the central tile is a weak tile then reduce it’s weight

Produced an optimized aggregation W=4

If the central tile is a stable tile then increase it’s weight

W=2

W=6

An aggregation optimized to both local and global parameters

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OPTIMIZING EVALUATIONS

Machine Learning

archiGO

The graphs above are captured from the Tensor board , a platform provided by Google to observe the training path , the accumulative reward graph reflects the reward value achieved by the agent at each training episode .It referents how it was able to increase the reward ( thus the quality of produced aggregations ) over the training course , and it reflect how the agent is exploring the terrain of values .

The stability evaluation is done by calculating the force and torque values applied to each tile and averaging the total for the aggregation, the agent is encouraged to produce aggregations that keeps those values below the break force value and the break torque value defined by the user for structural stability considerations. A visual reference was designed to reflect those values, where a color gradient that ranges from blue (as minimum) to red (as maximum) would reflect the forces applied to the aggregation.

The density evaluation is also considered during tile placement, where the agent is encouraged to favor tiles that takes a limited number of neighbors .Furthermore, it also encouraged to ignore minimum density value bounds for the tiles of the lower portion of the aggregation.

The area evaluation is another value to be sought by the agent where the agent is encouraged to select tiles that produce aggregations within the approximate limits of a defined area value.

The area evaluation is another value to be sought by the agent where the agent is encouraged to select tiles that produce aggregations within the approximate limits of a defined area value.

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Density

archiGO

The training is designed to encourage the agent to respect tile density limitations without compromising the stability , by allowing tiles located closer to ground to violate maximum density value .

Min Allowed Density: 2 Max Allowed Density: 7 Aggregtion Quality 54%

DENSITY

Min Allowed Density: 2 Max Allowed Density: 5 Aggregation Quality 34%

Number of Neighbors: 2

Min Density: 0

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>>

Number of Neighbors: Tile Density

Number of Neighbors: 4

RC3

Min Allowed Density: 3 Max Allowed Density: 9 Aggregation Quality 68%

Number of Neighbors: 7

Chapter 06

Min Allowed Density: 5 Max Allowed Density: 10 Aggregation Quality 72%

Number of Neighbors: 14

Max Density: 14 >>

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Stability

archiGO

STABILITY

Yellow tiles indicate weak tiles that are under the influnce of stress and torque values that approaches the break point limits.

With the training progress the gent learns how to produce more stable structure by choosing tile cluters that leads to more stable aggregations.

Average Force & Torque: 67.5 Aggregation quality: 22%

Min Force & Torque: 0

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>>

Average Force & Torque: 48.3 Aggregation quality: 42%

RC3

Average Force & Torque: 33.5 Aggregation quality: 76%

Chapter 06

Average Force & Torque: 28.4 Aggregation quality: 85%

Max Force & Torque: 100 >>

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Optimized Evaluation

archiGO

Multi-Criteria :

STABILITY | Aggregation Quality 55%

DENSITY | Aggregation Quality 63%

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Machine Learning Breed 02 Tiles MULTI-CRITERIA:

MACHINE LEARNING BREED [02]

Concept Refrence: Constraint Solving Algorithm

8

RC3

Chapter 06

Design: parameters : Aggregation data : Design parameters Aggregation data : 2 2 - required Area : 2200 m - aggregation -quality aggregation - required Area : 2200 m : 86 %quality : 86 % 2 - min : 3 of 14 - achived Area - achived : 1945 m2 - min tile density : 3tile of density 14 : 1945 mArea - average - max : 8 of 14 - average density - max tile density : 8tile of density 14 : 5 density : 5 - average value - max :torque : 85 N- m - max torque value 85 N value m average torque value : torque 23.4 N m : 23.4 N m force value - max: 100 forceNvalue : 100 N - average force- average - max force value value : 48.79 N : 48.79 N

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AREA AND DENSITY:

MACHINE LEARNING BREED [01]

Optimized Evaluation archiGO

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STABILITY AND AREA:

Tile Set of Breed[01] :

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MACHINE LEARNING BREED [01]

Optimized Evaluation

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archiGO

AREA AND STABILITY | Aggregation Quality 86 %

AREA AND DENSITY| Aggregation Quality 65%

STABILITY AND DENSITY :

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AREA:

MACHINE LEARNING BREED [01]

Optimized Evaluation archiGO

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DENSITY AND STABILITY | Aggregation Quality 82 %

AREA | Aggregation Quality 65 %

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DENSITY:

MACHINE LEARNING BREED [01]

Optimized Evaluation archiGO

Chapter 06

STABILITY:

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STABILITY | Aggregation Quality 23%

DENSITY | Aggregation Quality 36%

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archiGO

MULTI-CRITERIA:

MACHINE LEARNING BREED [01]

Optimized Evaluation

RC3

Design parameters : - required Area : 5300 m2 - min tile density : 3 of 14 - max tile density : 6 of 14 - max torque value : 75 ‎N⋅m - max force value : 95 N

Chapter 06

Aggregation data : - aggregation quality : 78% - achived Area : 6150 m2 - average density : 5 - average torque value : 13.3 ‎N⋅m - average force value : 60.7 N

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Concept Concept Refrence: Refrence: Constraint ConstraintSolving SolvingAlgorithm Algorithm ML + Optimized Evaluation

22

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AREA AND DENSITY:

Breed 02 Tiles

MACHINE LEARNING BREED [02] Machine Learning

Area Areaand andDensity Density: :

archiGO

RC3

STABILITY AND AREA:

Stability Stability and Area :and Area :

Design parameters : - required Area : 2200 m2 - min tile density : 3 of 14 - max tile density : 8 of 14 - max torque value : 85 N m - max force value : 100 N

Chapter 06

Tile Set of Breed[02] :

Aggregation data : - aggregation quality : 86 % - achived Area : 1945 m2 - average density : 5 - average torque value : 23.4 N m - average force value : 48.79 N

3

3 9

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Machine Learning Breed 02 Tiles

MACHINE LEARNING BREED [02]

ML + Optimized Evaluation

AREA AND STABILITY | Aggregation Quality 73%

2

AREA AND DENSITY| Aggregation Quality 73%

ncept Refrence: Constraint Solving Algorithm Concept Refrence: Constraint Solving Algorithm

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Chapter 06

Ar Are

STABILITY AND DENSITY:

Machine Learning Learning Machine Breed 02 02Tiles Tiles Breed

Stability and Density : Stability and Density :

4

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Optimized Evaluation

archiGO

AREA:

MACHINE LEARNING BREED [02]

Area Area: :

55

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Machine Learning Breed 02 Tiles

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Chapter 06

DENSITY AND STABILITY | Aggregation Quality 68%

4

This aggregation is produced by using the ArchiGo algorithm to optimize only Are , it can be observed how the aggregation stability performance is not well optimized , and the density as well, unlike the Area criterion used to optimize it.

AREA | Aggregation Quality 79%

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Concept Concept Refrence: Refrence: Constraint Constraint Solving Solving Algorithm Algorithm Optimized Evaluation

6

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6

DENSITY:

Machine Learning Breed 02 Tiles Breed 02 Tiles

MACHINE LEARNING BREED [02] Machine Learning

Density Density : :

archiGO

RC3

Chapter 06

STABILITY:

Stability Stability : :

7 7

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Optimized Evaluation

archiGO

Multi-Criteria :

STABILITY | Aggregation Quality 55%

DENSITY | Aggregation Quality 63%

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Machine Learning Breed 02 Tiles MULTI-CRITERIA:

MACHINE LEARNING BREED [02]

Concept Refrence: Constraint Solving Algorithm

8

RC3

Chapter 06

Design: parameters : Aggregation data : Design parameters Aggregation data : 2 2 - required Area : 2200 m - aggregation -quality aggregation - required Area : 2200 m : 86 %quality : 86 % 2 - min : 3 of 14 - achived Area - achived : 1945 m2 - min tile density : 3tile of density 14 : 1945 mArea - average - max : 8 of 14 - average density - max tile density : 8tile of density 14 : 5 density : 5 - average value - max :torque : 85 N- m - max torque value 85 N value m average torque value : torque 23.4 N m : 23.4 N m force value - max: 100 forceNvalue : 100 N - average force- average - max force value value : 48.79 N : 48.79 N

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AREA AND DENSITY:

MACHINE LEARNING BREED [03]

Optimized Evaluation archiGO

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Chapter 06

STABILITY AND AREA:

Tile Set of Breed[03] :

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MACHINE LEARNING BREED [03]

Optimized Evaluation

archiGO

AREA AND STABILITY | Aggregation Quality 61 %

AREA AND DENSITY| Aggregation Quality 68%

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STABILITY AND DENSITY:

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AREA:

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Optimized Evaluation archiGO

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Chapter 06

DENSITY AND STABILITY | Aggregation Quality 79%

AREA | Aggregation Quality 66%

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DENSITY:

MACHINE LEARNING BREED [03]

Optimized Evaluation archiGO

Chapter 06

STABILITY:

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archiGO

STABILITY | Aggregation Quality 86%

MULTI-CRITERIA:

MACHINE LEARNING BREED [01]

Optimized Evaluation

DENSITY | Aggregation Quality 79%

RC3

Design parameters : - required Area : 4500 m2 - min tile density : 3 of 14 - max tile density : 8 of 14 - max torque value : 100 ‎N⋅m - max force value : 100 N

Chapter 06

Aggregation data : - aggregation quality : 75% - achived Area : 5600 m2 - average density : 6 - average torque value : 26.8 ‎N⋅m - average force value : 44.8 N

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ADAPTING TO FIELD CONDITIONS

Optimized Evaluation

218

The distance field is used to mask some vertices of the graph , forcing the used solver to propagate through the rest of the available vertices .

It is sued to simulate terrain and obstacles that might exist on the location of where the structure is based , allowing for a more rigorous adaptive aggregations to be generated .

The agent is trained to produce optimized structures for different grid sizes in this case and for irregular graphs , allowing for a more propagating robust system to be used.

archiGO

RC3

Chapter 06

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APP

archiGO

Graph size / propagation archiGO RC3

archiGo is trained to manipulate certain graph sizes

  

GRAPH SIZE : 80 VERTICES

X Y

NOT ASSIGNED YET : 80 VERTICES

Z

Mimicking the topology using sdf can help the user build the desired model

 [  ]



  -  04  

[2]

There are currently 4 tile breeds that the user can choose from a drop-down menu User’s space to fill in how much area is needed

Buttons for Resetting the model, Rotating view, Screen Capture or saving the built 3D model

The user can read the different factors evaluated, and consider the proposal generated

                          

   

Counters per tile chosen by the agent

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

A I

RC3

Chapter 06

Graph visualisation of different analysis; structure, density, graph components

Real-Time Camera output of the aggregation

F A

C C

T A

C |

D A

D A

221

APP

222

archiGO

RC3

Chapter 06

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CHAPTER SEVEN | ARCHITECTURAL SPECULATION

0.7

Spatial Development

Exterior Spaces; In this case, connection/ access to consecutive tile

226

Architectural Speculation

Circulation / Stairway; also connection/ access means to consecutive tiles

RC3

Chapter 07

Spaces generated include larger interior spaces for intended for communal/collaboration spaces, smaller interior spaces intended for personal spaces, circulation spaces, exterior terraces/walkways, and more. The constraints and tiles design work in synergy to achieve the necessary spatial needs; for example, all tiles include accesses to neighbouring tiles, all tiles allow for access to circulation spaces wither on their own, or through access to consecutive tiles, openings for adequate sun exposure, and more.

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Spatial Development

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Architectural Speculation

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Chapter 07

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Univeristy College London Bartlett School of Architecture 2017 - 2018