NoMAS Portfolio | RC3 | The Bartlett 2018-19

RESEARCH CLUSTER 3 | LIVING ARCHITECTURE | TYSON HOSMER, DAVID REEVES, OCTAVIAN GHEORGHIU MACHINE LEARNING TUTOR : PANOS TIGAS | THEORY TUTOR: JORDI VIVALDI PIERA | TECHNICAL ASSISTANT : ZIMING HE

NoMAS : Athina Athiana, Evangelia Triantafylla, Ming Liu

2018-2019

UCL, The Bartlett School of Architecture

CONTENTS 00

02

INTRODUCTION

00.00 NoMAS CONCEPT ..08

01

ALGORITHMIC RESEARCH

01.00 ENCODED ASSEMBLIES ...20 01.01 RESEARCH WORKFLOW ...22 01.02 INTELLIGENT ENCODED ASSEMBLIES ...38 01.03 RESEARCH OUTCOMES ...46 01.04 CONSTRAINT SOVLER ALGORITHM

...49

COMBINATORIAL DESIGN RESEARCH

02.00 COMPONENT DEVELOPMENT

...88

02.01

COMBINATORICS RESEARCH

02.02

DATA EVALUATION

..104

02.03

ALGORITHMIC CONTROL IMPLEMENTATION

..116

02.04 INTELLIGENT MECHANISM

03

..97

..124

FABRICATION RESEARCH

04.00 MATERIALITY AND ASSEMBLY ...132

01.05 WAVE FUNCTION COLLAPSE ...56

04.01 DIGITAL DESIGN AND FABRICATION

01.06 CROSS SCALE DESIGN ...66

04.02 FABRICATION SYSTEM ...144

01.07

04.03 FIBER COMPOSITES ...148

EVALUATION

..71

01.08 ARCHITECTURAL SPECULATION ...80

...140

04.04 RESEARCH ON NATURAL FIBRE COMPOSITES

...156

04.05 LARGE SCALE FABRICATION

...172

...192

04.06 B-Pro NoMAS PAVILLION

04

ARCHITECTURAL SCENARIO

04.00 INTRODUCING NoMAS PLATFORM ...208 04.01 ARCHITECTURAL IMPLEMENTATION

...214

00 Introduction Define the Context

NoMAS

A Digital platfrom for Nomadic Housing

Athina Athiana, Ming Liu, Evangelia Triantafylla

NoMAS is a digital platform that generates housing communities for digital Nomads. NoMAS is framing a global co-owning concept, developing adaptive housing systems, specific to the needs of every Nomad, tackling the problem of traveling and owning.

The thesis proposes a new model of ownership based on buying the amount of space that every individual nomad can afford. The space that an individual owns doesn’t have a physical footprint, but a digital one, given the opportunity to the nomad travel all around the world, where the platform provides available places, and still own the same amount of space. In order to do so, we generate prefabricated components bolted together, shipped, and robotically assembled on site, generating reconfigurable spaces.

developing a global Nomad community. The digital Nomad is given the opportunity to buy certain amount of space that is physically built up by prefabricated components, assembled on site. There is the flexibility for a nomad to live in multiple places throughout the year while the ownership model enables houses to change size and shape in different locations. With the arrival of every Nomad, the community is gradually growing, while construction in one place can occur in parallel to living or disassembly in another location.

NoMAS is a platform that is focusing on the new ways of social living and investigating the rapid evolution of digital design and fabrication to the challenges faced by construction industry and extreme mobilities.

Digital Nomads are a growing group of people that are constantly travelling around the world staying in each place, from few days to years. They are considered to be location independent people, specifically tied to telecommunication technologies in order to earn their living.

NoMAS project is tackling the need of housing of the Digital Nomads, by providing low cost ownership, personalized and reconfigured spaces while

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ICD/ITKE Research Pavilion 2016-17

AlphaGo 2017, by Alphabet Inc.’s Google DeepMind

Kreysler & Associates, Fabrication Process 2019

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9

Diagram of NoMAS Process

ROOM LAYOUT ROOM TYPE LEVEL OF PRIVACY POSITION

SITE LOCATION

PREFERENCES SELECTION

platform

STORED IN FACTORY

DISASSEMBLY

ROBOTICALLY ASSEMBLED ON SITE

ASSEMBLY OF THE UNIT

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COMPANY

SITE

AGENT

A OPTIMIZATION

SHIPPING

B AGENT’S CHOICE

FABRICATION OF THE ELEMENTS

RECONFIGURATION

FINAL STRUCTURE RC3 | LIVING ARCHITECTURE | 2018-2019

11

Global community

USA

GERMANY LONDON AMSTERDAM

SAN FRANCISCO

NEW YORK TOKYO

THAILAND BANGKONG

MEXICO

PHILIPPINES

BRAZIL

12

KUALA LUM-

BALI

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Digital Nomads

1. Constantly travelling around the world over years.

2. Making profit through telecommunicational technology.

Digital Nomad

3. Lack of stable local relationship.

4. Lack of long-term housing ownership.

Since the project is targeting on digital nomads, we went through a series of research on their lifestyle and requirements on living.

Due to the reason that they are constantly travelling, it becomes less possible for them to obtain a housing ownership, tied to a single location. Another issue is that they are not only away from family and friends, but it is also hard to establish new local relationships with people since they are always coming and leaving.

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Based on these findings, we continued to research on possible living solutions for these people. We compared and contrasted the pros and cons between owning a house and renting a house, sharedownership, and co-living systems.

13

Comparing table of bying/renting a house

Buy a house

Rent a house

Owning the House.

The sense of stability and security.

The Ability to make rearrangement.

Flexibility and mobility for living at different places.

The sense of stability and security.

Bank mortgage or debt.

Availability of money.

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Criteria of popular destinations for digital nomads

Nomad Score Internet Temperature(now) Air Quality(now)

3.48/5 25Mbps 0℃ 32µg/m³

Cost Fun Humidity(now)

0%

Safety

Quality of life

People Density

Walkability

Peace

Traffic safety

Hospitals

Happiness

Nightlife

Free Wifi in city

Places to work

A/C or Heating

Friendly to

English Speaking

Freedom of Speech

Racial Tolerance

Female Friendly

LGBT Friendly

Startup Score

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$ 3,146 /mo

4k ppl/km² 16x16m

15

Housing Ownership

Globalization

Socialization

Customization

Private Bedroom

Private Bathroom

1. Community : 4-10 people.

2. Purpose of Co-living : For socialization.

3. Wanting to share : Internet, Garden, Workspace, Utilities.

4. Not wanting to share : Bathroom.

5. Concern : Degree of privacy.

Research on Co-Living in 2030

16

Essential Features for Digital Nomad Housing and Community

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Nomad A

Nomad B

Area: 75m2

Area: 42m2

Location: close to garden

Location: upper level

Space Layout: C

Space Layout: A

Amenities: bed, bathroom,

work space

Amenities: bed, bathroom

structure placement

structure placement

Nomad B

Nomad C Area: 102m2

Area: 64m2

Location: exterior view

Location: close to working area

Space Layout: A

Space Layout: B

Amenities: bed, bathroom,

Amenities: bed, bathroom,

kitchen

structure placement

Considering the research outcomes, NoMAS is proposing the design of a global company that is creating co-living and co-owning communities for digital nomads.

They can interact with the company through a user interface, a digital platform where they can navigate

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kitchen

structure placement

to the NoMAS location and after selecting destination, give their personal preferences as an input to the system.

NoMAS is giving the opportunity to the user to define the desirable level of privacy, location and space preferences.

17

01 Algorithmic Research Generate the structure

Encoded Assemblies

Investigating Cellular Automata in Conway’s Game Of Life

During the first workshop we investigated Cellular Automata and specifically Conway’s game of life.

Game of life is an infinite two dimensional, orthogonal grid of squares, representing cells in two possible states – alive or dead. Every cell interacts with its neighboring cells and according to some specific rules retains or changes its state.

Game of life was the starting point, in order to evaluate three dimensional structures, or stacks. This was achieved by setting some specific grid boundaries (dimensions) and extracting every new state of the grid upwards, so that each new state represents another layer of the stack.

During the analysis process, we examined cellular automata behaviors according to some sets of parameters. These were : - The seed of the stack, meaning an image at the dimensions of the grid in which we tested from simple examples as dots and lines to more complex ones, combinations of them.

- The different interacting neighbourhoods - Different sets of rules – for alive and dead cells-, and according to them - Quantifable features as local or global density, age, and relation between the seed image and the stack, which will be explained in detail.

3D GAME OF LIFE

STACK

CELL LAYER

ALIVE || DEAD CELLS

SET OF RULES

NEIGHBORHOOD

Investigating Cellular Automata in 3d version of Conway’s Game Of Life

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Extended Catalogues of Cellular Automata

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21

Research Workflow Cellular Automata

Through this first analysis six seed images were selected and tested tested according to a specific threshold, that represents the interpretation/ sensibility of the image pixels.

The examined neighborhood is the moore one, in which every cell interacts with 9 surrounding neighbors. Every case was examined with the eight set of rules, which were selected as they produced a variety of results related to global and local density.

The set of analysis was based on threshold testing examples. The threshold values that were tested, from 0.1 to 0.9 indicated the relation of the threshold to the amount of alive cells, thay would be: the closer to 1, the more alive cells. Although that was a general condtion, the 0.8 value produced some unexpected outcomes resultin to interesting behaviors. So, any further analysis was tested based on a trehshold value of 0.8.

The first feature we analyzed was the pattern of the first layer, as the relation between the seed image and the stack. Secondly, we were particularly interested at the density values, local – related to each layer, and global – related to the whole stack.

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BEFORE PROCESSING

Initial Input Data Input before model starts, Non-changable during running process.

DURING PROCESSING

AFTER PROCESSING

Model Boundary

Analysis Criterias

Learning Outcomes

Defines the shape limitation of the model could be generated.

Criterias be analysed during process, and changable durin process that affects the shape of model.

The relationship and result of each analysis criteria, and use the outcome to generate Top-Down models.

Neighbour #

Rules Collumn # Seed Image

Tendency of Rules: 1st Layer Pattern

Row # Threshold #

Layer Density Layer #

If..., Less...

If..., More...

If..., Collum-Like...

If..., Flat-Surface... ...

Global Density

Age Connectivity

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SEED IMAGE MODEL BOUNDARY: 30 x 30 pxel

NEIGHBORHOOD MOORE R1

SET OF RULES

VON NEUMAN

AGE

EXTENDED MOORE NEIGHBORHOOD

EXTENDED VON NEUMAN NEIGHBORHOOD

GLOBAL DENSITY

LOCAL DENSITY MOORE NEIGHBORHOOD

VON NEUMANN NEIGHBORHOOD

FIRST LAYER PATTERN

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SEED IMAGE

THRESHOLD

NEIGHBORHOOD

SET OF RULES

DENSITY

GLOBAL DENSITY

FIRST LAYER

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ALIVE CELLS THRESHOLD 0.8

MOORE R1 r=3

SET OF RULES

[1,2,3,4] [2,3,3,3] [2,3,3,4] [3,3,3,8]

LOCAL DENSITY

[4,5,3,4] [4,6,3,5] [5,7,3,5] [6,8,2,6]

25

Threshold Testing Catalogue

SEED IMAGE

RULE

0.2

0.3

0.4

0.5

0.6

6883

8854

8352

7869

7869

7869

11499

9822

19

180

823

823

823

823

105

2608

6934

11270

2634

18888

606

4804

9775

17265

23215

11412

11240

13253

2256

12317

12716

8478

11940

13205

4641

16

0

26

1707

5538

0.7

0.8

0.9

MAX

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First Layer Pattern Testing Catalogue

RULE [1,2,3,4]

[2,3,3,3]

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[2,3,3,4]

[3,3,3,8]

[4,5,3,4]

[4,6,3,5]

[5,7,3,5]

[6,8,2,6]

27

Age Testing Catalogue

SEED IMAGE

RULE [1,2,3,4]

0

28

[2,3,3,3]

[2,3,3,4]

[3,3,3,8]

[4,5,3,4]

[4,6,3,5]

[5,7,3,5]

[6,8,2,6]

MAX

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Analysis of Age outcomes

[4,5,3,4] SINGLE LAYER

SHORT STRUCTURES DEAD CELLS

COLUMN- LIKE

[4,5,3,4] TALL STRUCTURES CELL AGGREGATIONS [1,2,3,4] TALL STRUCTURES DISTINCT STACKS

[2,3,3,4] [3,3,3,8] [5,7,3,5] MASSIVE STRUCTURE TALL & MASSIVE STRUCTURES CELL AGGREGATIONS

[4,6,3,5] TALL & MASSIVE STRUCTURES GROWING UP CELLS [6,8,6,2] TALL & MASSIVE STRUCTURES YOUNG & VERY OLD CELLS

Another feature we analyzed was the age of the cells, representing the vertical stability, as a high age value means that the cell has been alive during many layers.

The Age catalogue illustrates different values again with a gradient increasing from white to pink and then black.

The rersults were similarly grouped in order to relate geometries with rules and similar age values.

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29

Local Density Catalogue

SEED IMAGE

RULE [1,2,3,4]

0

30

[2,3,3,3]

[2,3,3,4]

[3,3,3,8]

[4,5,3,4]

[4,6,3,5]

[5,7,3,5]

[6,8,2,6]

MAX

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Local Density Graphs

RULE [1,2,3,4]

[2,3,3,3]

0.155

0.062

[2,3,3,4]

0.229

[3,3,3,8]

[4,6,3,5]

[4,5,3,4]

0.313

[5,7,3,5]

[6,8,2,6]

0.049

0.0534

0.314

0.587

0.120

0.055

0.253

0.257

0.022

0.532

0.312

0.609

0.051

0.058

0.215

0.388

0.043

0.516

0.268

0.586

0.17

0.003

0.036

0.003

0.056

0.288

0.211

0.718

0.250

0.108

0.303

0.414

0.017

0.612

0.314

0.532

0.1

0.088

0.28

0.45

0.12

0.63

0.41

0.51

0.24

0.06

0.29

0.37

0.038

0.63

0.37

0.57

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Analysis of Density outcomes

[4,5,3,4] SINGLE LAYER

COLUMN- LIKE

MID ALIVE TENDENCY

[1,2,3,4] [2,3,3,3 ] STEADY BOTTOM & CELL AMOUNT

[5,7,3,5 ] MASSIVE STRUCTURE EVENLY RANGE

DENSE TOP CELL AMOUNT ++

[6,8,2,6] STEADY BOTTOM ++ CELL AMOUNT ++

During the analysis process, we were particularly interested in the density values, both local – related to each layer, and global – related to the whole stack density. The first catalogue illustrates the different local densities with a gradient, increasing form white to green, while the graphs depict the density value tendencies in each case . The average density value of each rule was also calculated.

In adittion to the produced testing catalogues the outcomes were grouped according to similar geometries and density values, so we are able to define the relation between each rule to local and global density.

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Rule Combination Diagram - Top down Control

SET 1

LOW DENSITY [1,2,3,4] [2,3,3,3] [4,5,3,4]

LOCAL DENSITY CONTROL SET 2

MODERATE DENSITY [2,3,3,4] [3,3,3,8] [5,7,3,5]

LAYER CONTROL

SET 3

HIGH DENSITY [4,6,3,5] [6,8,2,6]

After the detailed analysis of the input parameters and the different behaviours of the design model, the metrics were used as a feedback mechanism in the triggering of local rules in order to develop a controlled growth model.

In adittion to the previous analysis, extended catalogues of different combinations were produced. Out of these catalogues, specific examples, considererd to have interesting behaviour, were selected for further analysis.

Specifically, we grouped our selected rules in 3 sets, according to the density values they produced, and established different density and layer thresholds, in order to produce a variety of geometries, with the similar desirable features.

The illustrated examples idicate the effect of the changing rules in the produced geometry, after the top-down input of the density threshold in certain layers of the stack.

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33

60 [6,8,2,6]

60 [6,8,2,6]

60 [6,8,2,6]

50[2,3,3,4]

50[2,3,3,4]

50[2,3,3,4]

40[2,3,3,4]

1[2,3,3,3]

1[2,3,3,3]

1[2,3,3,3]

5[1,2,3,4]

40[6,8,2,6]

3[2,3,3,3]

20[1,2,3,4]

0 [6,8,2,6]

0 [5,7,3,5]

0 [4,6,3,5]

0 [3,3,3,8]

0 [2,3,3,3]

0 [4,5,3,4]

0 [2,3,3,3]

50[5,7,3,5]

50[2,3,3,4]

50[4,6,3,5]

60[4,6,3,5]

60[2,3,3,4]

60[2,3,3,4]

30 [2,3,3,4]

20[2,3,3,4]

10[1,2,3,4]

20[1,2,3,4]

40[6,8,2,6]

40[6,8,2,6]

40[6,8,2,6]

1[5,7,3,5]

0 [2,3,3,3]

0 [5,7,3,5]

0 [2,3,3,4]

0 [2,3,3,3]

0 [2,3,3,3]

0 [1,2,3,4]

0 [6,8,2,6]

30 [6,8,2,6]

50[6,8,2,6]

50[6,8,2,6]

50[6,8,2,6]

50[6,8,2,6]

50 [4,5,3,5]

50 [6,8,2,6]

5[5,7,3,5]

20[2,3,3,3]

30[2,3,3,3]

10[4,6,3,5]

1[2,3,3,4]

1[2,3,3,3]

0 [6,8,2,6]

0 [1,2,3,4]

0 [4,6,3,5]

0 [2,3,3,4]

0 [4,5,3,4]

0 [6,8,2,6]

0 [6,8,2,6]

50 [6,8,2,6]

60 [6,8,2,6]

50 [6,8,2,6]

50 [6,8,2,6]

50 [6,8,2,6]

50 [4,6,3,5]

50 [6,8,2,6]

20[2,3,3,3]

20[2,3,3,3]

20[2,3,3,3]

20[2,3,3,3]

20[2,3,3,4]

20[3,3,3,8]

0 [1,2,3,4]

0 [1,2,3,4]

0 [2,3,3,3]

0 [2,3,3,4]

0 [4,6,3,5]

0 [2,3,3,3]

0 [2,3,3,3]

50 [6,8,2,6]

50 [6,8,2,6]

50 [6,8,2,6]

50 [6,8,2,6]

30 [6,8,2,6]

50 [6,8,2,6]

50 [6,8,2,6]

20[4,6,3,5]

20[4,6,3,5]

20[4,6,3,5]

20[2,3,3,3]

10[4,5,3,4]

20[4,5,3,4]

0 [2,3,3,3]

0 [1,2,3,4]

0 [4,5,3,4]

0 [4,6,3,5]

0 [6,8,2,6]

0 [2,3,3,4]

0 [4,5,3,4]

30 [6,8,2,6]

50 [6,8,2,6]

50 [6,8,2,6]

50 [6,8,2,6]

50 [6,8,2,6]

50 [6,8,2,6]

50 [6,8,2,6]

10[4,5,3,4]

30[4,5,3,4]

10[2,3,3,3]

10[2,3,3,3]

10[2,3,3,3]

0 [6,8,2,6]

0 [2,3,3,4]

0 [4,6,3,5]

0 [4,5,3,4]

0 [2,3,3,4]

1[5,7,3,5]

0[4,5,3,4]

0[1,2,3,4]

Seed Images CA Rule Combination Catalogue

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45 [6,8,2,6]

40 [6,8,2,6]

45 [6,8,2,6]

35 [4,6,3,5]

35 [5,7,3,5]

35 [4,6,3,5]

15 [1,2,3,4]

15 [1,2,3,4]

5 [1,2,3,4]

0 [3,3,3,8]

0 [3,3,3,8]

0 [4,5,3,4]

50 [6,8,2,6]

35 [2,3,3,4]

55 [6,8,6,2]

50 [6,8,6,2]

40 [1,2,3,4]

30 [1,2,3,4]

15 [2,3,3,4]

15 [4,6,3,5]

30 [2,3,3,3]

10 [2,3,3,3]

0 [2,3,3,3]

0 [2,3,3,3]

0 [1,2,3,4]

0 [2,3,3,4]

50 [6,8,2,6]

50 [6,8,2,6]

20 [4,5,3,4]

20 [2,3,3,4]

3 [2,3,3,4]

3 [4,6,3,5]

0 [2,3,3,3]

0 [2,3,3,3]

50 [6,8,2,6]

50 [6,8,2,6]

50 [6,8,2,6]

45 [6,8,2,6]

20 [4,5,3,4]

20 [5,7,3,5]

20 [3,3,3,8]

20 [1,2,3,4]

10 [1,2,3,4]

10 [2,3,3,3]

5 [2,3,3,3]

5 [3,3,3,8]

0 [2,3,3,3]

0 [3,3,3,8]

0 [4,5,3,4]

60 [6,8,2,6]

55 [6,8,2,6]

20 [4,5,3,4]

40 [6,8,6,2]

45 [6,8,6,2]

62 [6,8,6,2]

30 [4,5,3,4] 5 [2,3,3,3]

30 [4,5,3,4]

5 [5,7,3,5]

0 [4,5,3,4]

0 [2,3,3,3]

0 [2,3,3,3]

0 [1,2,3,4]

60 [6,8,2,6]

60 [6,8,2,6]

50 [6,8,2,6]

60 [6,8,2,6]

45 [6,8,6,2]

25 [2,3,3,3]

35 [1,2,3,4]

20 [2,3,3,3]

25 [2,3,3,3]

20 [2,3,3,3]

5 [2,3,3,4]

5 [4,6,3,5]

13 [2,3,3,4]

13 [3,3,3,8]

13 [4,6,3,5]

10 [2,3,3,4]

5 [4,5,3,4]

0 [1,2,3,4]

0 [1,2,3,4]

0 [2,3,3,3]

0 [2,3,3,3]

0 [4,5,3,4]

0 [5,7,3,5]

0 [1,2,3,4]

35 [2,3,3,4]

45 [6,8,2,6]

45 [6,8,6,2]

25 [5,7,3,5]

25 [5,7,3,5]

5 [4,6,3,5]

5 [4,6,3,5]

0 [2,3,3,3]

0 [2,3,3,3]

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60 [6,8,6,2] 20 [2,3,3,3] 10 [2,3,3,4] 0 [5,7,3,5]

35 [2,3,3,4]

55 [6,8,6,2]

60 [6,8,6,2]

25 [4,5,3,4]

35 [5,7,3,5]

20 [1,2,3,4]

15 [6,8,2,6]

20 [6,8,6,2]

10 [2,3,3,4]

0 [3,3,3,8]

0 [3,3,3,8]

0 [5,7,3,5]

40 [5,7,3,5]

0 [2,3,3,4]

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CA - Rule Combination

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CA - Rule Combination

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Intelligent Encoded Assemblies An Introduction to Genetic Algorithm

During the second workshop, we worked on training a genetic algorithm in order to automate the process of producing stacks with different features, according to quantifiable data.

In the genetic algorithm a population of candidate solutions to a problem is evolved towards better solutions. It takes as input a set of genes and evolves during generations of individuals. After each generation is built, an objective value called fitness is evaluated.

The most fit individuals are selected from the current population in order to use their values, recombined and randomnly mutated for the next generation. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population.

For our research, we implied the genetic algorithm to our three dimensional structures in order to produce desirable forms with specific features.The desirable output was a table – like form, which we translated into quantifable data, according to our criteria.

In order to achieve this we evaluated the two dimensional patterns that can be produced by all the possible alive and dead cells combinations in the moore R1 neighbourhood. We chose to relate our fitness function with the patterns of the less alive cells, so that we can achieve a low global density value, as well as with some specific patterns of more alive cells, of which we were expecting to attribute more stability to the structures.

As genes, we used three set of rules we have figured out from our previous set of analysis, with low, moderate and high average density values, which are locally controlled through the fourth gene, a density threshold, related to the previous layer density in each case.

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SURFACE - LIKE

MODERATE- LOW BODY DENSITY

DENSE BOTTOM

Defining the table-like form

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CELL

LAYER

STACK

GENERATION

POPULATION

2

C

1

MORE FITNESS

LESS FITNESS

A

4

3

B

GENES

Analysis of the genetic algorithm

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Cell 01 Cells 02

Cells 03

Cells 04

Cells 05

Cells 06

Cells 07

Cells 08

Cells 09

Pattern evaluation in Moore R1 neighborhood

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4 ALIVE NEIGHBORS LOW DENSITY

PATTERN ASSEMBLY STABILITY

MEAN STACK DENSITY LOW DENSITY

Fitness criteria

GENES SET OF RULES & LOCAL DENSITY

FITNESS FUNCTION PATTERNS & DENSITY

IMPROVE MODEL PERCENTAGE OF PATTERNS LOW IN DENSITY

2 ALIVE CELLS

LOW DENSITY [1,2,3,4] [2,3,3,3] [4,5,3,4]

3 ALIVE CELLS 4 ALIVE CELLS PERCENTAGE OF PATTERNS //LOW DENSITY

HIGH DENSITY [4,6,3,5] [6,8,2,6]

DENSITY

MODERATE DENSITY [2,3,3,4] [3,3,3,8] [5,7,3,5]

CROSS PATTERN //STABILITY

PATTERNS FOUND

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First testing | 5 generations of 10 individuals

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FITNESS

GENES GENE 1 | GENE 2 | GENE 3 | GENE 4 |

LOW DENSITY RULES MODERATE DENSITY RULES HIGH DENSITY RULES DENSITY THRESHOLD: [0.05 - 0.25]

SEED IMAGE

DEAD CELLS DENSITY

2-3-4 ALIVE CELLS

PATTERN 20%

30%

50%

FITNESS : 0.286 DEAD CELLS DENSITY : 0.878 PATTERNS : 15 2/3/4 ALIVE CELL PATTERNS : 2072

FITNESS : 0.286

FITNESS : 0.289

DEAD CELLS DENSITY : 0.878 PATTERNS : 15 2/3/4 ALIVE CELL PATTERNS : 2072

DEAD CELLS DENSITY : 0.898 PATTERNS : 2 2/3/4 ALIVE CELL PATTERNS : 1794

FITNESS : 0.287 DEAD CELLS DENSITY : 0.892 PATTERNS : 5 2/3/4 ALIVE CELL PATTERNS : 1821

FITNESS : 0.286 DEAD CELLS DENSITY : 0.890 PATTERNS : 13 2/3/4 ALIVE CELL PATTERNS : 1834

First Testing | most fit individuals

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Last testing | 5 generations of 10 individuals

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FITNESS

GENES GENE 1 | INCREASING DENSITY SET OF RULES GENE 2 | SET OF ALL RULES GENE 3 | FLUCTUATING DENSITY SET OF RULES GENE 4 | DENSITY THRESHOLD: [0.05 - 0.25]

SEED IMAGE

BOTTOM LAYERS DENSITY

2-3-4 ALIVE CELLS

PATTERN 25 %

25%

50%

FITNESS : 0.54 PATTERNS : 6 2/3/4 ALIVE CELL PATTERNS : 1995

FITNESS : 0.53 PATTERNS : 9 2/3/4 ALIVE CELL PATTERNS : 2182

FITNESS : 0.53 PATTERNS : 9 2/3/4 ALIVE CELL PATTERNS : 1019 FITNESS : 0.54 FITNESS : 0.54

PATTERNS : 6 2/3/4 ALIVE CELL PATTERNS : 7229

PATTERNS : 6 2/3/4 ALIVE CELL PATTERNS : 1209

Last Testing | most fit individuals

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Research outcomes

Genetic Algorithm as a Feedback mechanism

The research process can be summarized in constantly changing variables, regarding the genes and the fitness function, in order to trigger the desirable outcome, but also achieve a junction between a high fitness value and the table-like form.

So, starting from a set of genes controlling mainly the global density, we moved on to increase the local, instead of the global control, as the form we were seeking is consisted of different local density parts.

At the first testings we switched the specific connectivity pattern and the percentages of the different variables to the function several times, in order to find out to what extend each of the factors affects the function. What we noticed, is that although the algorithm produced some table-like forms, they were not the most fit ones.

46

Moving on to testing 5, we removed entirely the density control from the fitness function, leaving only the density threshold as the fourth gene. In this case we were getting table-like forms as the most fit stacks, but the actual fitness value was remarkably low even for them.

Next step was to re-introduce density control in the fitness function, but in a new way : as bottom layer density. In this case we were getting the desirable forms and high fitness values.

So, the last step was to reassert that the specific compination of genes and fitness produces efficient outcomes by applying it to two other seed images.

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TEST 1

TEST 2

ADD RANDOMNESS ADD LOCAL CONTROL

SWITCH PATTERN SWITCH FITNESS VARIABLES

TEST 5

TEST 4

TEST 3

SWITCH PATTERN

REMOVE GLOBAL CONTROL

TEST 6

ADD LOCAL CONTROL

GENES :

GENES :

GENES :

GENES :

GENES :

GENES :

AVERAGE DENSITY VALUE

DENSITY VALUE TENDENCY

DENSITY VALUE TENDENCY

DENSITY VALUE TENDENCY

DENSITY VALUE TENDENCY

DENSITY VALUE TENDENCY

RANDOM RULE SELECTION

RANDOM RULE SELECTION

RANDOM RULE SELECTION

RANDOM RULE SELECTION

RANDOM RULE SELECTION

FITNESS :

FITNESS :

FITNESS :

FITNESS :

FITNESS :

FITNESS :

GLOBAL DENSITY

GLOBAL DENSITY

GLOBAL DENSITY

GLOBAL DENSITY

LOW DENSITY PATTERNS

LOW DENSITY PATTERNS

LOW DENSITY PATTERNS

LOW DENSITY PATTERNS

LOW DENSITY PATTERNS

LOW DENSITY PATTERNS

CONNECTIVE PATTERN

CONNECTIVE PATTERN

CONNECTIVE PATTERN

CONNECTIVE PATTERN

CONNECTIVE PATTERN

CONNECTIVE PATTERN BOTTOM LAYERS DENSITY

Research process

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Constraint Solver Algorithms An introduction to Wave Function Collapse

Tiles creating the model Adjacency constraints

A main computational tool used during the research process is a constraint solving algorithm based on the quantum mechanics phenomenon - Wave Function Collapse (Karth & Smith, 2017) - applied in order to encode and optimise different kinds of connections and local rules.The algorithm builts iteratively a model, based on the initial input, a group of tiles. The initial tile is a random selection of the algorithm and it further goes on, building on the adjacent tile with the smallest domai of possible selections. The size of the selection domain is determined of the constraints of the tiles themselves. The constraints of the model, as well as the graph itself, are hiding genetic code of the arhitecture within.

Wave Function Collapse is currently used in the gaming industry for creating new levels for players at games.

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

During the research we experimented by implementing different kinds of constraints and propagation rules, so that we can achieve a more effective control on the algorithm and eventually produce optimal achitecture solutions.

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Tiles Creating the Model

50

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Tiles Creating the Model

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Tiles Creating the Model

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Tiles Creating the Model

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Tiles Creating the Model

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Tiles Creating the Model

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Wave Function Collapse Research on 3D combinatorial design

Tile Design Developement

Moving on to the 3D graph, the spatial model is propagated through a cube, having six connecting sides, which should satisfy the adjacency constraints.

The design research started from creating a solid and unique geometry, emerging from the initial cubeplaceholder subdivided into smaller cubes. Four of these smaller cubes are utilized to form the initial basic component. We tested this design logic in different components and different scales regarding 56

the relation with the placeholder. In this way, we are allowing higher flexibility on potential connections and the combinatorics system. Each group of tiles is formed with the basic tile repeated in different rotations - variations.

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[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]

[16]

Rotations - Iterations

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Wave Function Collapse Cooperation with graph theory

graph vertices

cube - container

Working in parallel with the wave function collapse on the graph theory facilitated the analysis and development process. We connected each one of our tiles on the generated structures with a graph vertex and defined the six degrees as the six adjacency references.

Defining six connection labels for every 3d tile (one on each face of the cube-placeholder), allowed the algorithm for more flexibility and emergent conditions.

58

At the same time we were able to analyze and extract data for further development, both for every separate connection/direction of a tile, as well as for every tile, or the structure as a whole.

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D1 D5

D4 D3

D6

D2

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vertices

adjacency

[0]

[D0] [D1] [D2] [D3] [D4] [D5]

[1]

[D0] [D1] [D2] [D3] [D4] [D5]

[2]

[D0] [D1] [D2] [D3] [D4] [D5]

[3]

[D0] [D1] [D2] [D3] [D4] [D5]

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Tiles Creating the Model Set 01

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Tiles Creating the Model Set 02

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Tiles Creating the Model Set 03

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Tiles Creating the Model Set 04

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Tiles Creating the Model Set 05

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Tiles Creating the Model Set 06

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Cross Scale Design

Research on 3D combinatorial design

Multi-scale design

In pararell with the combinatorics system exploration, we are working on testing similar designed tiles at different scales. Taken the initial tileset, each one of the eight tiles is converted into a container, that holds smaller cube subdivisions. The new tiles are resulting by aggregating scaled-down versions of the eight tiles, starting from a different one each time.

In this way we are concluding with a catalogue of 64 new tiles. Each one of the “children” tiles retains the same adjacency constraints with its “parent”, so that 66

the parent can be replaced by any of its children. By combining multiple scales in the same structure we are giving the Wave Function Collapse algorithm the opportunity to result in more interesting outcomes with different local behaviors and various spatial qualities.

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Catalogue of subdivided tiles

Tile replacement logic RC3 | LIVING ARCHITECTURE | 2018-2019

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Generated structures subdivision tiles

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Generated structures combination of scales

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3D printed aggregations

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Evaluation

Algorithmic analysis

The wave function collapse algorithm enables the computational generation of a set of possible solutions to every given problem - defined as the set of tiles and the given constraints. In order to evaluate the generated solutions in each case, as well as to explore the most appropiate tile set that meets our criteria, we analysed the performance data of representative structures of each tile set. We performed a computational analysis through graphs, evaluating the connections and dis-connections among the tiles, as well as through extracting numerical data, such us the density, an estimated area per level (assuming that each cube-placeholder equals to 9x9x9 m) and the performance of each given tile to the structure.

Next step is to utilize an intelligent desicion making system, implementing machine learning agents in order to automatically increase or decrease the objective values , resulting from the algorithmic analysis. In this way, we aim to incorporate more criteria for the desicion-making, such as user preferences and specific site conditions.

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analysis parameters | defining the goals

high connectivity

moderate density

flexible connections

stability / continuous space

balance of buit / unbuilt space

various space qualities

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Data analysis Set 01

2025 m2 2025 m2 2025 m2 2025 m2 2025 m2

structure

area

Model density= 0.50

Model connectivity = 0.92

generated geometries

[16]

72

[13]

[21]

connection graph

[12]

[19]

[17]

[14]

[13]

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Data analysis Set 02

900 m2 900 m2 900 m2 900 m2 900 m2

structure

area

Model density= 0.17

Model connectivity = 0.44

generated geometries

[9]

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[11]

[15]

connection graph

[23]

[15]

[17]

[14]

[10]

[11]

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Data analysis Set 03

900 m2 900 m2 900 m2 900 m2 900 m2

structure

area

Model density= 0.15

Model connectivity = 0.21

generated geometries

[9]

74

[17]

[12]

connection graph

[19]

[13]

[13]

[18]

[24]

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Data analysis Set 04

900 m2 900 m2 900 m2 900 m2 900 m2

structure

area

Model density= 0.21

Model connectivity = 0.38

generated geometries

[8]

[7]

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connection graph

[7]

[14]

[6]

[17]

[13]

[12]

[10]

[6]

[2]

[4]

[2]

[3]

[8]

[6]

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Data analysis Set 05

747 m2 954 m2 999 m2 981 m2 1161 m2

structure

area

Model density= 0.16

Model connectivity = 0.22

connection graph

generated geometries

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[12]

[7]

[3]

[6]

[5]

[9]

[6]

[10]

[5]

[4]

[6]

[5]

[8]

[3]

[0]

[1]

[1]

[9]

[2]

[10]

[4]

[4]

[5]

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Data analysis Set 06

882 m2 1143 m2 936 m2 1107 m2 666 m2

structure

area

Model density= 0.14

Model connectivity = 0.31

generated geometries

connection graph

[8]

[1]

[5]

[2]

[5]

[5]

[5]

[6]

[6]

[7]

[4]

[5]

[6]

[4]

[2]

[6]

[4]

[5]

[6]

[2]

[3]

[1]

[3]

[3]

[3]

[8]

[2]

[4]

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[4]

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Architectural speculation Introducing the Human scale

Part of the research consists the architectural speculation on the project, which includes the study on the contemporary and future co-living needs, study on the living space standard metrics and the adjustment of our design in order to meet these needs.

We evaluated the adjacency constraint systems that we are testing, in order to ensure that the algorithmic process and the design work in synergy, towards achieving the necessery spatial needs. Spaces generated include multiple scaled interior and exterior spaces, as well as spaces appropriate for horizontal and vertical circulation. We are experimetning on testing the spatial and social relations that would result of new nomads coming in existing structures, implementing some basic dimensions to our tileplaceholder.

Tile propagation | Testing possible relation

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3m

3m

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Small Appartment

Working Space

Common Area 2 people Appartment Individual Studio

Individual Studio

Architectural Speculation

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[Case 01] Grid: 1.5 x1.5 m

[Case 02] Grid: 1.0 x1.0 m

[Case 03] Grid: 0.5 x 0.5m

[Case 04] Grid: 1.5 x 1.5m

[Case 05] Grid: 1.0 x 1.0m

[Case 06] Grid: 0.5 x 0.5m

[Case 07] Grid: 1.5 x 1.5m

[Case 08] Grid: 1.0 x 1.0m

[Case 09] Grid: 0.5 x 0.5m

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02 Combinatorial Design Research Component & Algorithmic Development

Component Development Introducing the design of the components

In parallel with the algorithm research, the thesis is focusing on the search of the architectural form of the components. The Tile Design is used as a placeholder for the combinatorial part and the form is developed following the container and the combinatorial system. The design of the form is focusing on fluent curvy forms, in order to produce heterogenous aggregations and create atmospheric spatial qualities.

Looking thoroughly in generating design processes along with design references, such as Thallus project of ZHA CoDe, the Knit Candela we tried to define the computational form searching. The main principles of the modular design are focusing on the elegance and the heterogeneity of the final outcomes. More specifically, the principles of component design are driven by simplicity aiming to complex aggregated structures.

Throughout the research process we produced different geometrical forms exploring the potentials of the algorithm in the developing of the actual architectural space. The design of the components is based on the way the interface with each other.

For every designed component their permutations are introduced in order to explore the combinatorial research. The form search is a key element in the modular housing system, as NoMAS tends to offer a variety of spatial qualities based on the preferences of the user-nomad. For that reason, the combinatorial design consists of an extended catalogue of smallscale components and a catalogue of large-scale components. The aggregated space is aiming to high quality spatial configurations and architectural qualities.

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ZH CoDe Thallus, Milan Design Week 2017

ZH CoDe Knit Candela

Xintiandi 3D Printing Pop-Up Studio by Mamou-Mani Architects

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Solid Unit

Subdivision

Further subdivisions for smoothing

Unit

Form Developement

Smooth Component

Unit

Form Developement

Smooth Component

Example 01

Example 02

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Component Design

component 01

container

interface

Components that interface with Component 01

01

02

03

04

05

06

C

D

E

F

Permutations of the Component 01

A

90

B

G

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Exploring the Combinatorial Design

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

17

18

19

20

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Extended Catalogue of small-scale Components

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Extended Catalogue of large-scale components

Combination of small-scale & large-scale components

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Single Nomad Architectural Speculation

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Private & Public Space

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Combinatorics Research

Exploring the potentialities of the WFC

Testings on different tilesets combinations

Trying to explore the potentials of the combinatorics in relation to the WFC algorithm, we produced a number of testings on different tile designs and combinations of them. In this way, we were able to extract information about which tiles could be combined with others and in what aggregations they are resulting and develop our tileset. In most of the cases, the generated structures would be comprised of about three tilesets out of the nine that were given in total, unless they were completely detached RC3 | LIVING ARCHITECTURE | 2018-2019

in one of the three axis. In this case the structures would involve more tilesets, since they would grow as separate parts. In the previous catalogues, different tilesets are designated with different color .

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WFC aggregations small scale tileset

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WFC aggregations small & large scale tileset

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WFC aggregations exploring the spatial qualities

Case 1: continuous aggregation with inner gaps

Case 3 : separated smaller aggregations

Case 2: partially divided aggragation

Case 4 : repeated continuous surface

By testing different sets of tiles at single layer structures to the WFC algorithm we could extract outputs about the generated spatial qualities and evaluate the efficiency of the tiles. The results were various: from large continuous aggregations with inner gaps (case 1), to partially divided (case 2) or completely divided spaces (case 3). There were also some etreme cases, like repeating continuous surfaces (case 4) or aggragetions that would not create an efficient living space (case 5).

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Case 5 : not efficient space aggregation 103

Data Evaluation

Algorithmic analysis and control mehanisms

The wave function collapse algorithm enables the computational generation of a set of possible solutions to every given problem. However, apart from satisfying the adjacency constraints, it is a content agnostic algorithm.

NoMAS platform is developing an intelligent system that negotiates between the needs of three major forces : the nomads’ preferences, the location constraints, and the company incentives, and produce evaluated optimal solutions. In order to achieve this, we defined a number of parameters and data that would be analyzed at the algorithmically generated structures and which would consist the inputs and objectives of the system. The analyzed data are: built area and green area spaces, built and unbuilt volume, the structural stability of the structures, as well as the type of the generated space, which would serve personalization purposes. The type of space is defined according to the different tile designs and the various spaces that are generated out of different configurations among them.

Next step was to develop mechanisms that would add intelligence to the wave collapse algorithm and its decision making, towards producing structures that will reach desirable levels at the previous defined parameters.

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user preferences [type of space]

site constraints [built volume, built area, green spaces]

company benefit [structural cost]

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D

D

D

D

A1

B

B

C

A0

D

type A space type B space

A1

A

B

A

C

A1

B

Besides building a mechanism that would achieve the generation of efficient living communities, by controling data such as built and outdoor area levels, NoMAS aims to further allow for user-personalised spaces.

In order to achieve this, we defined two types of space, associated with the design of our components.

We classified all the simple-lined connections on any of the 3 axis as type A, while where the connections were shaped by undulating line would be classified as type B. In this way, we can extract an analysis graph out of any generated structure, that will indicate the levels of the different types of spaces and give the possibility to further control the creation of specific spaces with control mechanisms.

A

[A, B ,C ,D,..]

[A0, A1,..]

Space graphs according to the defined connections

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USER PREFERENCES

position WAVE FUNCTION COLLAPSE

level of privacy amount of space

aggregation type of space aggregation

NoMAS platform SITE CONSTRAINTS

build regulations

Tiles Selection

aggregation

Nodes Selection

aggregation aggregation aggregation

site geometry existing users

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GLOBAL EVALUATION CRITERIA

built VS unbuilt optimized aggregation

outdoor/green areas

optimized aggregation

structural stability

optimized aggregation ML agents

COMPANY BENEFIT

optimized aggregation optimized aggregation optimized aggregation

structural cost profit

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Tileset initial catalogue of simple tiles

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Data analysis

side view

perspective

built area

built space

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built volume

green space

0

structural stability

MAX

MIN

MAX

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Tileset complete catalogue of small scale tiles

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Data analysis

side view

built area

perspective

built space

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built volume

green space

0

structural stability

MAX

MIN

MAX

type of space

type A

type B

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Tileset small scale tiles & large scale tiles

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Data analysis

side view

built area

perspective

built space

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built volume

green space

0

MAX

structural stability

MIN

MAX

type of space

type A

type B

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Algorithmic Control Implementation Controlling the tile selection side view

Case 1. vertical growth

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perspective

side view

perspective

Case 2. horizontal growth

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Algorithmic Control controlling the position selection

top view

perspective

Case 3. perimeter growth

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side view

perspective

Case 4. corner growth

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Algoritmic control Maximize built area

side view

perspective

built area

built space

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green space

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Algoritmic control Minimize built area

side view

perspective

built area

built space

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green space

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Algoritmic control Maximize space type A side view

perspective

type of space

type A

118

type B

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Algoritmic control Maximize space type B side view

perspective

type of space

type A

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type B

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Algoritmic control Multi-level control implementation

Random place selection

perspective

perspective

space type A

Perimeter growth

Corner growth

space type B

A significant part of the research process has been the attempt to implement control over the WFC algorithm, in order to trigger specific behaviors. This would later guide the direction of the training process.

The algorithmic control was implemented on the selection of the tiles and followed two directions : either prioritize specific graph vertices (positions), controlling the structure growth, or trying to achieve specific values at the analysis parameteres with the appropriate tile selection, such as built area, or types of spaces.

Finally, we tested combinations of both control levels.

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side view

built area

built space

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built volume

green space

0

stability

MAX

MIN

type of space

MAX

type A

type B

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Intelligent mechanism Machine Learning Agents

In parallel with exploring the potentialities of the wave function collapse algorithm, we started building a selfimprovement learning framework, utilising machine learning agents. We worked on building a hybrid of WFC and machine learning algorithm, that would use an AlphaGo search-like method, allowing for predictive problem solving by back tracking through a search tree of generated solutions.

The AlphaGo algorithm used a deep learning method in order to train a computer programm to play and win the board game Go, which entails an infinite number of possible moves and outcomes. The main idea was to grow a tree of possible moves and results, where an AI agent is capable of predicting the outcome of its actions, driving its behavior towards more desirable results. (AlphaGo, Google Deepmind) Similarly, NoMAS is building a mechanism in which the agent acquires the role of the selector among the possible tiles that satisfy the adjacency constraints of the WFC for each graph vertex. The traditional WFC would do this selection randomnly.

In order to do that, the agent-selector has first to be trained in simply satisfying the adjacency constraints, instead of the WFC. Using a fixed size list of 0, representing the whole tileSet and assigning 1 to the possible adjacent tiles for each vertex was a technique that we used to train the algorithm at the first stage.

The agent was receiving a positive reward for every correct selection and negative for every wrong one .

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Following that, the agent should prioritze the selection of specific components according to the scores they have as a level of satisfying certain conditions, as stability, or built area. After every selection the placed component gains an additional score so that as the episodes are increasing, the score of every component for every parameter is stabilized to a value that indicates the relative efficiency.

Additionaly, the agent-selector is rewarded after the generation of every structure according to the global measurments of the criteria, such as total area or overall stability. In order to attribute a reward that could reflect the relative efficiency of the defined parameters, we are using the Kullbak-Leibler divergence function, for calculating the directed divergence according to defined levels-goals.

According to the function the divergence (D) between two probability distributions P and Q is defined as DKL(P||Q)= ÎŁ P(x)log(Q(x)/P(x)). (Solomon Kullback and Richard Leibler, 1951) KL divergence is representing the information gain or mutual information. This is the amount of information that is derived from one variable by observing the behavior of another variable, a concept linked with the entropy of a random variable.

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WFC Step

TileSet

possible tiles according to adjacency

[0,0,0,0,0,0,0,1,1,1,0,0]

case 1

case 3 case 2

Agent Action

convert tileSet to a vector of 0 and 1 allows the agent to iterate through possible tiles

iteration through previous runs, in order to predict the outcome in each case

Update Tile Score

Tile Selection

Local Reward current tile score : 0.4 WFC Step

Global Agent Reward

Analysis Criteria

Kullback-Leibler divergence

stability built area built volume type of space

optimized aggregation

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reward the agent according to the extent in which the structure satisfies the defined measures

define two criteria as discrete probability distributions P&Q

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Machine Learning Data Analysis

Environment/Cumulaive Reward

0.000

40k

80k

Environment/Episode Length

120k

Losses/Policy Loss

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Environment/Lesson

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Losses/Value Loss

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In order to evaluate and evolve the training process we are observing the behavior of the agent at different runs through the analysis graphs of Tensorboard, a platform provided by Google to observe the training path.

Using this platform, we are mainly interested at the cumulative reward graph behavior, which should be overall increasing, allowing for small ups and downs.

Additionaly, the episode length should have an increasing tedency.

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Environment/Cumulaive Reward

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Losses/Policy Loss

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Losses/Value Loss

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Unsuccessful run Environment/Cumulaive Reward

0.000

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Environment/Episode Length

120k

Losses/Policy Loss

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Environment/Lesson

60k

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Losses/Value Loss

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Successful run

We conducted several runs, during the first stage of the training, trying to teach the agent to connect the adjacent tiles from the tileset, using the conversion to 0 and 1 technique. The observation environment was set as the whole tileset, while the reward was only given at the tile selection stage at this phase.

The agent was positively rewarded for every correct tile place, while it was penalized and reset for every inefficient tile selection.

While the cumulative reward was increasing at the first runs, the episode length was decreasing and the agent would constantly reset the model without efficiently learning (Unsuccessful run).

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By adjusting the relation between the positive and the negative reward, and rate in which the agent would collect information and readjust its technique, we achieved an increasing tedency for both the cumulative reward and the episode length graphs and the agent efficiently learnt to combine the correct tiles for generating structures (Successful run).

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Environment/Cumulaive Reward

0.000

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Losses/Policy Loss

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Overall TensorBoard capture after multiple runs-first stage

After the agent was trained to correctly connect the tiles according to the adjacency rules, next step would be to gradually add observations and relevant rewards in order to produce optimized structures. The optimization criteria would be the defined analysis parameters : built area, outdoor area, stability and desired type of space. Those would be paired for every run, so that we could calculate their relative efficiency and additionaly reward or penalize the agent after the generation of a structure, using the KL divergence function. Using this technique the agent should be able to learn to generate structures that reach defined values, combining two of the given parameters.

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Machine Learning Mechanism Projection

Optimized structure - stability & built area balance

Optimized structure - space type A & built area balance

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03

Fabrication Research Material and Assembly studies

Materiality and Assembly Introduction to Fabrication

In parallel with the algorithmic research we produced several studies on materials and assembly systems.

The process aimed into investigating physical materials, testing assemblies, joints and deep further into prefabrication.

In that context, we did several tests on different materials, joint, and forms and produces a physical prototype of a Table-looking form developed from CA algorithm and evaluated using GA. In order to build the physical prototype as easy as possible, we chose to construct it out of acrylic panels. More specifically, acrylic panels are laser cut, in two different elements: strip and connector.

hole. That joint represents a unit, which is a pattern we are testing in the script. Count how many live cells In that unit, add how many strips to that joint. Repeat the step and connect the other pattern based on position. When finished one layer, go on to the upper layer.

The model came in two colour, black and grey.

Black colour represents 2-3-4- alive cell pattern in the fitness function. Grey colour, on the other side, represents the other situations. First divided the layer cells into 3x3 cell units, count the number in the unit, and assemble in position.

Strip represent the cells, and connector join the strips together, also tights the whole structure together.

Take one strip and one connector, go through the

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FRAMES

CAST MATERIAL

ASSEMBLIES

Prototypes

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Prototype for Assembling system

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Fabrication of the CA Structure Translation

STRIP 160 x 10 x 3

1 UNIT = 3 x 3 CELLS

280 x 10 x 3

CONNECTOR

INTERLOCKING - LINKING STRIPS 3 x 3 x 140 || 280 || 400 || 580 mm COLOURS

BLACK: 2-3-4 ALIVE CELLS | FITNESS GRAY: OTHERS SITUATIONS

BASIC ASSEMBLE METHOD 2 STRIPS + CONNECTOR

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ASSEMBLED 2 STRIPS MEET EACH OTHER

ONE UNIT STRIP

CONNECT OTHER UNITS CONNECTOR LINKS UNITS TO UNITS

CONNECT UPPER LAYER REPEAT STEP 3-5

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TRANSFORM EACH UNIT TO POSITION

CATEGORIZED CELLS INTO UNITS

3X3 UNITS LAYERS

3 3

Assemblies Diagram

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PLACE IN POSITION

STRIP + CONNECTOR

UNIT 3X3 CELLS HIGH FITNESS

LOW FITNESS

Fitness Diagram

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Plysical Moldel Prototype

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Digital Design and Fabrication Research on fiber composites

In terms of innovative housing design, our team is thinking of bring in new material system into the project, which are fiber composites material and monocoque system.

Fiber composites is one type of FRP (Fiber Reinforced Plastic). A composite material made of polymer matrix reinforced with fibers, majorly used for construction of airplane. As a new type of material, which is uncommon for the architectural industry, partly because of it’s high-cost. However, the special characteristics of fiber composites, such as lightweight, prefabrication, flexibility, are able to reduce the over construction cost that save budget.

especially featured for is abilities to adapt irregular, complicated wavy forms that helps it strength than flat, plain form, plus the ability to be unlimited in size, helps to complete the unexpected organic forms that might be generated by the algorithm and Machine learning in the future. Another key feature of fiber composites is that it is very light weight, which helps for easy production, transportation, construction for robotic arm on site and the cost of structural supporting material. It’s characteristic of prefabrication makes the overall construction period faster.

The process first went through a serious of prototype studies on different characteristics of fiber composites materials, including different types of fiber composites material, multiple layers, exterior coatings, and joints studies for monocoque system. One of the features of the project is that the architecture is reconfigurable, that the components are assembled and disassembled according to requirements by robotic arms.

For the project, we are interested in new material of the industry. Recent architectural projects, such as the Expansion of SFMOMA and Serpentine Pavilion 2014, shows the potential of Fiber Composites. It’s

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GLASS COMPOSITES

NEW HOUSING MATERIAL

NEW LIVING EXPERIENCE INNOVATION

FLEXIBILITY

CUSTOMIZING SHAPES

LIGHT-WEIGHT

LESS STRUCTURAL ELEMENTS

PREFABRICATION

LESS TOTAL CONSTRUCTION TIME

DURABILITY

CORROSION RESISTANCE

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COST SAVING

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SFMOMA Expansion / Snøhetta

House of Dior / hristian de Portzamparc 2015

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Serpentine Pavilion / Smiljan Radic

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Bolting Detail / Kreysler & Associates

Bolting Detail / Kreysler & Associates

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Sand Coating / Kreysler & Associates

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Fabrication System

Fiber Composities & Monocoque structures

In order to maximize the use of space and customizing for each nomad, we decided to incorporate with the monocoque system, that will function as walls and supporting structure itself.

3/16”

Specifically, for Fiber Composites, we found that combining foam between fiberglass could make the most effective monocoque for the project. Both the internal and external faces are made of fiber composites, and foam is expending the thickness and strength while maintaining lightweight requirement.

1/16” 1/4” 1/16”

The initial concept of the monocoque system for this project. The centre core is made by foam that supports the fiber composites material that wraps around it, as well as thickens up the width of the component that just layering up fiber composites to achieve desire thickness.

1/32” 1/8” 1/32”

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Save 33% material Lighter weight 8 times stronger

Save 66% material Lighter weight Same strength Same thickness

1/32”

The four sides are implemented with key Flanges that are used for connecting the components in place.

The prefabricated component is featured with different keys to be recognized by machine learning.

Inward keys match with outward keys, and also made in different geometries that tells the direction and orientation of the components to be assembled on site.

SOLID FRP

3/8” 1/32”

Save 66% material Lighter weight 8 times stronger Twice thickness

Sandwich Catalogue

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Monocoque System & Key Flanges

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Different Fiber Composites Testing Catalogue

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Fibre Composites Material studies

We tested different aspects of fibre composites material, from different weights (range from 100g ,250g, 300g, to 600g), different types (different weight of chopped strand mat, different thickness of woven pattern, type of tissue), different layer of fabric sheets (from 1 layer to 3 layers), different combinations of fabrics (mixing, sandwich) in order to learn about the basic knowledge of the features of the material and to decide what feature to adapt for further testings.

Applying gelcoat as an exterior finishing is a common step. In order to give the components more variety to choose for the clients, and to test different atmosphere of the architecture, we combined the gelcoat with different type of sand types.

The aim of combining with sand is to achieve a rough surface texture. However, because of the feature of the gelcoat, all product eventually gives a smooth surface when coming out of the mould.

Therefore, we applied an extra layer of sand on the surface later to achieve the rough surface texture.

Produced Catalogue

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Produced Catalogue

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Prototype (Gelcoating surface)

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Prototype (Gelcoating surface)

Prototype (surface witout tissue)

Prototype (surface with tissue)

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One of the features of the project concept is that the overall structure is changeable, reconfigurable as digital nomads are coming and leaving during travelling. Therefore, in order to adapt this feature, we decided to use bolting as a jointing technique between the components, which makes it easy to assembled and disassembled during the construction process.

Combining the bolting system with the monocoque system, it gives the structure possibilities to be free with columns with just wall components.

The final mould is shaped similar to one of our digital combinations. The mould comes in two parts. First, we made a wood mould by columns of ribs by laser cutting of the preferred curvature, and applied a smooth surface on the ribs.

Second, we used the vacuum machine with plastic sheet to get the mould for fibre composites.

Mould fabrication

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Prototype detail

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Bolting Prototype - Key Flanges

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Fabrication Process

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Research on natural fibre composites Coconut fibre & Sisal fibre

The NoMAS platform is a global business that serves people to travel and stay all around the world. In order to achieve this, the structural system should have the ability to be constructed all around the world in different locations and environments. The previous study on GRP and glass fibre is aimed for urban environments in the city, while we are also interested in other more natural environments, such a beach side or the forest, that are as well popular destinations for digital nomads, like Bali.

The research tried to explore the different aspects of the material. The surface is explored through patterning and transparency in order to be customized for different users.

We started to look at natural fibre composite materials for a beach side scenario. The beauty of the material is appreciated from its distinctive qualities of colour, texture and of belonging to the earth. It is also with the feature of environmentally friendly and ecological advantages for lost cost housing construction. Additionally, they are providing interesting visual and ventilation effects.

We tested different types of natural fibres, including coconut husk fibre, jute fibre, and sisal fibre. After analyzing and comparing the different results and effect, we decided to study the coconut fibre as the main components, and cooperate with other materials.

Coconut fibre, also known as Coir, is a byproduct of coconut. It is possessing strong resistance to bending and compressing due to its high percentage of lignin content.

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Precedents

Examining natural fibres, we continued our research on structure with the monocque system. We know from previous studies that fibreglass has the ability to form itself strongly. However, the natural fibre material behaves different and is not as strong as fibreglass. We researched on more precedents on how monocoque is created.

According to the study, we decided to update our structure into a wood-rib structure with fibre composites surfaces.

Taking out as much as material of the wood-rib in order to create a light-weight structure, as well as allowing for the tubing and electricities to be functioning.

Waterfront Retail / Balmond Studio

Cricket Media Centre / Future Systems London

Porsche Pavilion / HENN

Board Museum Oculus / Kreysler & Associates

The structure will still be using the same jointing method, which is bolting. This allows for the concept of assembling and disassembling when a user joins or leaves the community. We also created holes on the material surface in order to cooperate with robotic arms for assembling.

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Structural Diagram

Turbing & Electricity

Holes for Bolting

Rib Structure

Surface & Insulation

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Different Natural Fiber Comparison

Length

Coconut Fiber

Strength Shaping

Length

Sisal Fiber

Strength Shaping

Length

Jute Fiber

Strength Shaping

Natural Fibre Composite Materials

We tested several different types of natural fibre composite materials. Studies involved comparing and contracting the length, strength, and the abilities of shaping. Among the three materials, coconut husk fibre provides the best overall qualities and potential for further developments. We selected that as the primary material for the prototypes, but cooperated as well with other naturals materials for different spatial atmospheres.

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Initial Testing

Random Layout

Bundle Layout

Random Natural Messy Pattern

Certain Designed Pattern Path

Natural Ramdom Shadow

Clear Shaped Shadow

We conducted a first testing set on different making methods: randomly spread out, or set into bundles that have a denser covering.

Random spread out method tends to give a natural patterning and transparency. Bundle method is intended to created dense, no-gap, opaque effect. The second method requires for more technique on shaping and placing the bundles for the final result. Â

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Initial Full Structure Testing

Rib Structure

Glued Fabric

Fibre layer

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Result

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This is an initial study on the whole composition of the component structure. There are three elements in the prototypes : the rib, the fabric layer, and the fibre surface.

Fibre Beyond Edge

Fibre Cannot Fill the Corner

The rib structure is creating the shape of the components. It is also reducing the weight of the whole component comparing to using a solid inner structure, and supports the surface material. Next step is to cover the ribs with a piece of fabric in order to create a platform or surface to lay the fibre on. The final step includes the application of the fibre. In this way, the three-layer elements are bound together and create a prefabricated component.

The first issues following this technique include the exceeding fibre ends at the edge, while some corners and borders are not fully covered. Moreover, the study revealed that the bundle fibre pattern is creating a higher strength than a random pattern.

Dense Bundle Pattern Stronger More Fibre Needed

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Random Pattern Weaker Less Fibre Needed

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One Unit Tile

One Component

Structure

Testing 1

Ribs

Glued Fabric Layer 164

Assembled Structure

Applied Fibre and wraped with Cling Film for Shaping RC3 | LIVING ARCHITECTURE | 2018-2019

Testing 2

Displacement

Shell Line

Utilization

Van Mises Stress

For the first experiment, we transformed a section of our initial designed tile components.

The first testing on the shape is constructed with the previous three-layer method. We shaped the bundles and laid them densely and naturally following the curve of the structure. The main issue encountered was the shaping of a perfect edge and the technique of having a smooth finish surface result.

During the second testing, we cooperated with the karamba3D plug-in in Grasshopper for a form analysis, and used the shell line analysis to form the fibre pattern.

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Testing 3

The previous study shows the experiments on creating an opaque surface with intentionally designed fibre path pattern. We are interested in discovering different aspects and features of the material. During the first set of studies we tried out two different methods: random layout and bundle layout.

Therefore, we continued to explore the effect of laying the fibre randomly, like building a net.

For this study, we tried the following two different ways. First was the standard threelayer method. However, the result was not satisfying.Due to the supporting fabric layer, the effect of transparency was reduced. The visual impact of the fabric layer was even exaggerated.

Method A: 1. Glued Fabric Layer

Method A: 2. Fibre Application

Method B: 1. Fibre layered separately on a sheet of cling film, with glue

Method B: 2.Half-dry fibre sheet flipped onto the rib structure.

Moreover, differently from the bundle shaping, random shaping gave the fibre the ability to overlap on each other and created a continuing net or surface itself.

In this way, we could exclude the fabric layer to achieve better transparency quality.

In order to create the component without the fabric, we placed a sheet of cling film on the floor, placed the fibre, and applied glue. When the glue was half dried, we flipped the sheet onto the rib structure, which used its weight and gravity to be bounded to the structure.

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Created surface quality

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Reseach on alternative fibre composites

Jute Fibre

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Sisal fibre

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Assemble Testing

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Large Scale Fabrication Research on large scale prototyping

During the previous sets of studies we explored the different possibilities of the natural fibre composite materials. By having a review on that, we continued our research to a lager scale in order to investigate more about the spatial qualities they provide.

Furthermore , we intended on testing the efficiency and details of our structural system.

Utilizing the set of designed tiles, we used three types of components to make a 1.5m x 1.5m wall representation, which includes a window.

Reviewing previous studies, we chose some aspects to develop further: different transparency in gradual transition, the shadow quality, and the mix of two different materials.

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Fabrication Method 1

Component surface with cling film

Fibre and glue application

For the fabrication of the first component, our intention was to create a transparent surface, combining two fibre composites materials. In order to achieve this we used the technique of the cling film, instead that of fabric layer, for applying the fibre.

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Waiting for the glue to dry in order to form the shape

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Fibre and ribs separation for removing the cling

Fibre placement to the component

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Difficulties

1. Transition between two materials We tried to cooperate with two different materials in the prototypes that could represent different atmospheres or surface qualities.

During the testing, we applied one material and then the second material.

The testing revealed that it is hard to control a smooth transition area within the two materials. The result shows that the second material (coco fibre) is clearly on top of the first one (sisal fibre).

2. Fibre pattern The coconut husk fibre is different from previous ones, with better and stronger, straighter quality. Therefore, although the fibre is laid out randomnly, intented to create a net-like pattern, the results shows bold, straight, bundle pattern.

3. Unflattened surface.

In previous studies, we covered the fibre layer with a sheet of cling film in order to form the shape. In this study, we took out that layer, and the result showed that the fibres are not pressed down as a smooth surface.

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4. Difficulties with Cling Film The cling film is weak, and it is creating an imperfect surface for the fibre.

Due to this, when the fibre layer dried in the form, it was hard to match perfectly back to the structure.

5. Difficulties with Cling Film The imperfection between the cling film and the rib structure creates miss matching when the fibre surface is dry. Since the fibre layer is already stiff at this point, there is no other chance to reform it.

6. Exceed Edge Fibre In previous studies, we cut off the extra fibre, but this method does not give out a nice finish edge, which creates problems in the later assembly

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Fiber Shaping & Transparency

1. Original fibre shape.

2. Fibre separated and curled into a ball

3. Fibre placed into a bag to set the reshaping

4. Final reshaped fibre

According to the outcomes of the previous testings, in the next one, we separated, softened and curled the fibres in advance so they would not be tight, but strong, straight bundles, and we would be able to create the pattern we desire.

were finished we worked on next sections.For different transparency effects, we laid multiple layers of fibre at the required places , in order to control the use of fibre.

In order to handle the fibre, we used small amounts, separated them, and formed them into a net, in order to lay that section in place. After separate sections 178

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1-Layer

2-Layer

Desired pattern effect.

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3-Layer

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Transition Between Materials

Two materials mixed in advance.

Fibre applied in three sections.

Outcome

In order to create a smooth transition between the two materials, we mixed those in advance. Thus, when applying the fibre, instead of two materials, we are seemly applying three material in three sections : Material A, Mix, Material B.

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Desired Transition Effect.

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Edge Refinement

Boundary wrap with tape.

Boundary wrap with cling film.

Scissor is not able to cut the edge perfectly, especially at the corner, so we tried with knife.

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1. By edge wrapping.

With the edge wrapping method, although there is no more needed to cut out exceed fibre, the method creates bundles of fibre at the edges, which conflict with the idea of creating seamless edge effect. Moreover, some parts are folded and reduced too much to cover the rib structure.

2. By knife.

With a knife, we could keep the seamless boundary and a sharp edge.

A permanent problem throughout the studies was the fibre exceeding the edges of the component. During previous testings we tried to cut the pieces off by scissors, but the result was not satisfying. In order to test solving the edge problem during the making process, instead of the final component, we used tape and cling film to create a boundary for the edge. However, the result RC3 | LIVING ARCHITECTURE | 2018-2019

was still not satisfying: the edges were not exceeding, but some parts were folded and reduced too much to cover the rib structure.

Finally, we found that the shave of the exceeded edge with a knife created the best outcome for the surface edge finishing.

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Fabrication Method 2

After the last tested making method, we resulted that it includs many fundamental problems so we continued the fabrication process with the previous making process.

So, for the next component we placed a sheet of cling film on the structure and marked the outline of the surface. Next steps were flipping, placing, and taping the sheet on the floor, or a plain surface.

The glue was first applied on the cling sheet and after the fibre layer was applied, we placed an additional glue layer. In order to create a smooth surface, we applied another layer of cling film after the glue and fibre. When it was half-dried (1-2 hours), we flipped the fibre layer onto the rib and let it use its weight, gravity, and left of glue to connect to the rib. We peeled off the cling film after around 1-2 hours of waiting, and let it completely dry in air.

1. Lay a sheet of cling film and mark out the edge.

Using this method, the result between the rib structure and fibre is mostly satisfied.

2. Flip the sheet and tape down.

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3. Apply a layer of glue.

5. Apply glue and lay a sheet of cling film on top.

4. Apply fibre as needed.

6. When half-dry, flip the fibre sheet back onto the rib.

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Seamless Edges

After assembling the components, the edges are seamless and well merged as a whole. The next step would be adding more construction details, such as openings for bolting and unbolting the shadow gap between the components, and the visual effect of the interfaces.

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Lighting and Shadow Quality

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Physical Model

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Physical Model

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B-Pro NoMAS Pavillion Building at 1:1 scale

B-PRO Design Proposal 1 3m

3m

Perspective 1

Top

2.2 m

Perspective 2

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3.2 m

Side

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B-PRO Design Proposal 2 2.5 m

2m

Perspective 1

Top

2.4 m

Perspective 2

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Side

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Structure Transformation

Overall

Rib Structure

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Reconfiguration

Reconfiguration of components

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Structural Performance

Rib Structure

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Shell Optimization

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Utilization

Displacement

Stress

Van Mises Stress

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Structure Coconut Fibre Composite Skin

Bolt Assess Hole Through Skin Only

11.5mm Diameter Hole for bolting 60mm apart

6mm Plywood Connection Plate

1200mm x 9mm Plywood Rib

M10 Steel Bolt, Nut, Washer

Element Details

Inner

Outer Bolt Assess Hole protector (Wood)

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Fabrication Process

1. CNC Cutting the ribs, and assemble

1. Mix PVA glue and water (3:1)

2. Place cling film on structure, mark the edge

4. Place cling film on flat surface, tape down, apply fibre

5. Apply glue

6. Add a layer of cling film on top to ensure connection

7. When half-dry, place fibre layer back to the structure

8. When complete dry, cut off the extra fibre

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B-Pro Placement

Orientation at B-PRO site

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Final Model

High Density of Fibre

Medium Density of Fibre

Low Density of Fibre

Sisal Fibre

Coconut Fibre

Sisal Fibre Fibre Affect Design

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BPro Show 2019 Prefabricated coconut-fibre composite monocoque prototype, in which components are bolted together in 1:1 scale.

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04 Architectural Scenario NoMAS Concept

Introducing NoMAS platform Interface Design

The concept of NoMAS platform is being framed as a start-up company and it is designed as an interface.

Digital ownership

The interface creates an intermediate level of association between the digital nomad and the company. The user is able to select the location where the platform operates and provides available space, select their preferences regarding the layout and the type of the room, the level of privacy, the position of their personal space within the existing structure and define the way individuals want to interact with the other nomads. The digital nomad can therefore buy the amount of space he can afford and create his own living space.

The evaluation mechanism takes into account three forces: the user preferences, the site constraints and the company incentives. The field of possible solutions arises from the intersection of the three factor needs. A learning mechanism is triggered in order to generate the optimal configuration and calculate the actual location of the new spaces within the existing structure. As a result, the platform provides the user with the possible configurations, and the user can reset the proposals or save his choice. When the nomad saves his choice, the platform generates one more configuration that satisfies the company in a greater extent, offering some alternatives to the selected preferences of the nomad. The agent - nomad can still choose between those two choices.

global business

Personalisation

Optimization

Agent

Agent’s Choice

Site Constraints

Company benefits

Company’s Suggestion

NoMAS Structure Diagram of the Design of the Interface

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Starting screen of NoMAS Interface

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Location Selection

Preferences Selection

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Existing structure in the specific location

First Choice / Reset the choice

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Second Choice / Reset the choice

Third Choice / Save the choice

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Alternative offer from the platform

Final structure

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Architectural Implementation Researching on adaptability of NoMAS

The architectural scenario was built based on different locations. Our system has the ability to adapt to the different site conditions and regulations as for example the amount of green area, the built space, the maximum height. The constraints of every location define the composites material of the monocoque structure in each case. During our research we also investigated the potentiality of the scalability of the structures as the system enables the addition and /or removal of components.

More specifically the scenario was built based on two different locations, a seaside scenario and a urban location scenario. During that process we tried to explore the adaptability of the system in the different needs of the locations, and how the system negotiates between the given parameters. One of the main principles of NoMAS is the continuous lifecycle of the structure. So, the arrival of every nomad affects the balance of the already built areas and negotiates between the private, circulation and public spaces of the overall structure.

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Building the community Urban Site Scenario

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Urban Site Scenario A community build in an urban location. The community is hosting approximately 15-20 nomads and has the ability to gradually grow with the arrival of every nomad. The composite material that is used in this scenario is fiberglass.

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Building the community Seaside Scenario

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Seaside Scenario A community build in a seaside location. The community is hosting approximately 10-15 nomads and has the ability to gradually grow with the arrival of every nomad. The composite material that is used in the following scenario is coconut fiber, as it is a local and sustainable material, ideal for tropical climates.

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/νομάς/