Block Party Phase I Booklet

BLOCK PARTY NAHMAD BHOOSHAN STUDIO 2017-19

Architectural Association School of Architecture Design Research Lab

MArch Thesis 2017-19 Phase I Book Report Submitted Sept 26, 2018.

Taeyoon Kim Atahan Topรงu Bhavatarini Kumaravel

BLOCK PARTY NAHMAD BHOOSHAN STUDIO 2017-19

Architectural Association School of Architecture Design Research Laboratory Tutored by Shajay Bhooshan, Alicia Nahmad Vazquez Submitted by Taeyoon Kim, Atahan Topรงu, Bhavatarini Kumaravel MArch Thesis Phase I Book Report

4

TABLE OF CONTENTS

01

THESIS

1.1 DESIGN RESEARCH AGENDA 1.2 STUDIO BRIEF 1.3 THESIS STATEMENT

9

9 10 12

02

GAME RESEARCH

18

03

BLOCK RESEARCH

105

04

DATA-DRIVEN RESEARCH

146

HOUSE RESEARCH

174

BIBLIOGRAPHY

224

05 06

2.1 2.2 2.3 2.4 2.5

GAME DESIGN PRECEDENTS CHAIN REACTION AND HOW IT EVOLVED COMPUTATIONAL UNITS GAMEPLAY SCENARIOS GAME INTERFACE

3.1 URBAN PRECEDENTS 3.2 BLOCK FRAMEWORKS

4.1 URBAN DATA RESEARCH PRECEDENTS 4.2 MACHINE LEARNING PRECEDENTS

5.1 5.2 5.3 5.4

BRICK RESEARCH PRECEDENTS BRICK GEOMETRY FABRICATION PROCESS ROBOTIC ASSEMBLY

6.1 REFERENCES

18 31 56 82 94

105 138

146 158

174 184 200 210

224 5

01

THESIS 1.1 DESIGN RESEARCH AGENDA

1.1.1 THE DRL

1.1.2 CONSTRUCTED AGENCY 1.2 STUDIO BRIEF 1.3 THESIS STATEMENT

9 9 9 10 12

7

8

1.1 DESIGN RESEARCH AGENDA 1.1.1 THE DRL

The Design Research Laboratory (DRL) is a 16-month post-professional design programme, leading to an MArch (Architecture & Urbanism) degree. For over a decade, the DRL has been organised as an open-source design studio dedicated to a systematic exploration of new design tools, systems and discourses, targeting design innovations in architecture and urbanism. The DRL actively investigates and develops design skills with which to capture, control and shape a continuous flow of information across the distributed electronic networks of today’s rapidly-evolving digital design disciplines. Learning in the studio is project-based and includes the development of comprehensive, year-long design projects, supported by design workshops and seminars, applying new forms of associative logic towards the conception and materialization of comprehensive design proposals. Design work is pursued by collective self-organised design teams within three parallel design studios, addressing an overall design research agenda through shared information- based diagrams, data, models and scripts. The collaborative structure of the DRL design studio enables design teams to address the programme’s design research agenda through a sustained body of design work, which is regularly evaluated by student design teams, tutors and invited critics, and is channelled towards the development of recursive, research-based design methodologies and comprehensive design outcomes.1

1.1.2 CONSTRUCTED AGENCY

Intro | AA DRL | Architecture and Urbanism MArch (DRL) – AA School.

Accessed September 6, 2018. http://drl. aaschool.ac.uk/about/.

1

“Graduate School.” In AA Prospectus 2017-18, by Architectural Association School of Architecture, C12. London: AA Print Studio.

2

A day in the AADRL studio.

FIGURE 1.1.1.1 (left)

Constructing Agency, explores expanded relatioahips of architecture by considering the future of living, work and culture.

The aim of the research is to expand the field of possibilities by exploiting behaviour as a conceptual tool to synthesise the digital and material worlds. Advanced computational development is utilised in the pursuit of architectural systems that are adaptive, generative and behavioural. Using the latest in advanced printing, making and computing tools, the lab is developing work that challenges today’s design orthodoxies. Architectures that are mobile, transformative, kinetic and robotic are all part of the AADRL agenda, which aims to expand the discipline and push the limits of design within the larger cultural and technological realm. Theodore Spyropoulos’ studio explores how behaviour-based design methods can be used to reconsider cultural projects for today. Agent-based Parametric Semiology, Patrik Schumacher’s studio, contributes to the ‘semiological project’ which promises to upgrade architecture’s communicative capacity within the work environment. Shajay Bhooshan’s studio, House.Occupant.Science.Tech.data (HOSTd), explores robotic fabrication while enabling mass-customisation strategies that can compete with contemporary co-living models in highly productive cities. The promise of mass-customisation integrated with new models of housing now allows for the generation of a vibrant community fabric.2 9

1.2 STUDIO BRIEF

NAHMAD BHOOSHAN STUDIO

The three-year research agenda of the studio, starting from January 2017, is motivated by the following observations regarding contemporary design, fabrication technologies and trends in contemporary living.

1.

Digital design and fabrication technologies is maturing with significant progress being made by researchers in the fields of computational architectural design3, computational geometry4, structural design5, robotic manufacture6 etc.

2.

Social, economic and political conditions in large, high-productivity cities such as Tokyo, London, New York etc. have evolved7 such that the market conditions are now suitable8 to engender a demand for mass customised housing.9

The two observations together yield the premise of the research agenda: Developing real-estate solutions for contemporary living in high-productivity cities are a prime avenue for application of the maturing domain of digital design and fabrication. In other words, the promise made by seminal design research and polemic publications on Mass customisation and housing such as Negotiate my boundary10 and the generation of a vibrant community fabric11, can now indeed be realised.

Thomsen et al. 2015 Adriaenssens et al. 2016 5 Anon 2015 6 Reinhardt et al. 2016 7 The guardian 2016; jon earle & irene pereyra n.d.; IKEA 2017 8 Bardakci & Whitelock 2003 9 Chong et al. 2009; Gann 1996 10 Steele 2006 11 Autopoesis of residential community (Schumacher 2002) 3 4

Bricks casted at Boston as part of the Studio trip

FIGURE 1.2.1 (right) 10

11

1.3 THESIS STATEMENT

Inspired by Shajay Bhooshan, Alicia Nahmad Vazquez, and Aldo Van Eyck, we research ways to re-establish the connection between urban planning and housing which has been lost in modern cities for decades, if not centuries.

We propose a co-living system where users negotiate their boundaries through game. The complex user dynamics shape the house, which shape each block, and block by block reshape the entire city. Home becomes a micro-city, and city becomes a huge house.

Home as city, city as home.

A user can enter the game to generate and manipulate his/her own surrounding. The user gets to make his/her own decisions about what to share, who to share with, or not to share at all.

The users will get to make decisions for the composition of their own house as well as negotiate their boundary within the network, both physically and socially. These units accumulate to form an entire block. Hence, the block is no longer a static entity, but one which keeps evolving dynamically, shrinking or growing according to the input of the users. This process will bridge the gap between mass customisation of the block and the user, making each block unique and dynamic.

A top-down prescription to urban housing has resulted in dry, monotonous masterplans which treat all individuals as equal variables for calculation and are callous to the social changes. Hence, most of today’s cities including London fails to reflect the needs or wants of the inhabitants, with the outdated formulas of the past. Without room for adjustments, the city subdivides in an unhealthy manner within a caged framework, resulting in tiny rooms of poor quality. The habitants of the city stay impotent, unable to alter their environment due to regulations and laws forbidding them to do so.

The bottom-up approach to housing will restore and empower the habitants of the city to make their own decisions. On top of this, the house to block relationship is re-defined in a way that units of individuals or families come together to form a community, which will shape the block, and ultimately reshape the city. The social condenser spaces will be re-programmed to reflect the interests & preferences of the inhabitants.

As inhabitants engage with the system, community will evolve out of gameplay. The social dynamics is married with the physical from house-scale to block scale.

In order to make this vision feasible, we are researching automated masonry construction carried out by mobile fabrication units, which are deployed to the site. Taking advantage of the precision of prefabrication to the site, the bricks will be assembled automatically. Inspired by the Block Research Group at ETH and 12

Source: Aldo van Eyck Writings

FIGURE 1.3.1 (right)

‘Home as city, city as home.’

13

Eladio Dieste, we explore thin shell masonry structures that display superior structural strength while consuming less material.

The game is developed in such a way that the users can participate through their SNS network account. This helps the users take advantage of his/her network of friends, and enable the system to suggest a list of compatible flatmates. Multiple players can view the simulation of their community building via augmented reality, making the experience more interactive and participatory. We have specifically chosen AR over VR for a number of reasons. The players will be able to overlay the results of their gameplay onto the reality before the physical construction takes place, and make informed decisions based on environmental factors of the site. AR has the strength to project the visionary outcome onto the reality, and by doing so it blurs the boundary between the digital simulation and the reality. Since our goal is to bring these visions closer to reality and engage people, we consider it to be the more powerful medium compared to VR. Also, by enabling AR on multiple mobile devices, other people can share the content being displayed and interact simultaneously. Instead of being cut off from reality with goggles and headphones, AR devices allow the users to stay in the real world and interact with other human beings while playing. As Pokemon-Go has demonstrated, AR game is more engaging and encourages people to interact with it in conjunction with the reality, which is what we wish for our urbanism project. Gamification of the urban housing can make community building more fun and engaging for all the parties involved.

Machine Learning is utilised in order to preconceive how the floorplans of London can be manipulated using the computational strength of today’s GPU. Inspired by projects such as Invisible Cities

by Gene Kogan with the Open Dot research team, and fake-butgood-enough-for-robots by Stamen Design Studio, we are using the Pix2Pix software (Image to image translation using conditional adversarial network) to process satellite images and floor plan data of London city. This development could happen with the help of our tutor Alicia, Cristobal Valenzuela and Dr. Jun-Yan Zhu. We anticipate that this software will assist the architect and urban planner with an insight into how the structures and blocks can be reconfigured to better suit the inhabitants’ needs. We are also exploring the potential of t-SNE in recording and classifying user profiles during gameplay. With the analysis data of t-SNE, the users will have a better understanding of their community and be able to find compatible flatmates easily. More potential implementations of the data are being actively discussed in our group, and we predict that it can do more than providing feedback to the players and the architect.

All in all, we are pursuing new methods to visualise and explore how urban housing situation in London can be improved. These methods are AR visualisation of gameplay connected with Social Network Service account, and adopting machine learning tool to provide insight in the design process and as a feedback device. We are using these methods at an unprecedented urban scale in order to explore how Urban planning itself can be rethought, while looking for answers to improve the housing crisis of London.

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15

02

GAME RESEARCH 2.1

2.2

2.3

2.4

2.5

GAME DESIGN PRECEDENTS 2.1.1 SIM CITY 2.1.2 THE ‘GO’ GAME 2.1.3 SCRABBLE GAME 2.1.4 THE HEXAGON GAME CHAIN REACTION AND HOW IT EVOLVED 2.2.1 CHAIN REACTION - THE MOBILE GAME 2.2.2 APPLYING THE GAME TO ARCHITECTURE 2.2.3 CODIFYING THE GAME 2.2.4 EMULATING THE REAL WORLD 2.2.5 IMPLICATIONS COMPUTATIONAL UNITS 2.3.1 USER PROFILES 2.3.2 NEUFER SPATIAL STANDARDS 2.3.3 SHARING CHOICES 2.3.4 SHARED NEUFERT 2.3.5 SOCIAL CONDENSER 2.3.6 EXPERIMENTAL GAME 2.3.5 DECIDING ON THE PRIMITIVE UNIT GAME PLAY SCENARIOS 2.4.1 BLOCK BUILDING GAME INTERFACE 2.5.1 SNS CONNECTION 2.5.2 CREATING THE HOUSEHOLD 2.5.3 FINDING USER INTERESTS 2.5.4 CHOOSING THE SPACES 2.5.5 LEVERAGING APPLE’S AR-KIT

18 20 24 25 26 31 31 35 38 48 54 56 56 58 67 72 74 74 80 82 89 94 94 97 97 99 101 17

2.1 GAME DESIGN PRECEDENTS

We are trying to explore gamification as a strategy of urbanism in order to solve the housing crisis of London. The city has experienced three major housing bubbles since the 70s, and has been in shortage of housing for a long time. On 16th of May, 2016, The Mayor of London, Sadiq Khan, has exposed the extent of the capital’s housing crisis mentioning that the overpriced housing was driving the population out of the city. We recognise that London housing crisis cannot be dealt in a brute physical manner, where we simply supply more houses. The demand for property in London is global while the supply is local. Hence, social, cultural, economic aspects need to be considered in unison  in order to resolve the issue.

In response to this failure, we propose game based approach, which enables a more responsive and accessible system.

We anticipate that it will satisfy a greater portion of the public, and enable the users to have greater control over their own housing.

With greater accessibility and ease of control, users will gain a better control of the parameters of their own environment.

Many games have been searched, examined and developed to find a game that could respond demands of game-based urban planning research. In this regard some, games are based on attack/defence mechanisms whereas, some games target to enhance sharing and social negotiations between users. Besides, some games can achieve powerful user network pattern in a sense.

On the other hand, the common thing among games that all games be set on some simple rules which lead a different kind of patterns.

The games are based on attack/defence mechanisms encourage competitive/hostile playing attitude, and naturally generates a certain amount of void/open spaces. This was also interpreted as spaces which require privacy. On the other hand, the games are based on sharing/trading lead players to make some tactical decisions in order to achieve the target in a limited amount of spaces.

Game case studies chosen.

FIGURE 2.1.1 (right, top) Explorations through board games.

FIGURE 2.1.2 (right, bottom) 18

Go Chess Monopoly Board Game

Scrabble StudioMoniker - Hexagons Connect 4 Othello

In Between

Chain Reaction CityVille Facebook Sims

Video Game

Minecraft Sim City Age of Empires

Study 1

Study 1.5 (+Age)

Study 3 (Profile + Age)

B K T L B K T L GB

L

Board Game

Study 3.5 (+Deformed Grid)

Study 3.5.5 (+Scrabble factor)

K G

B T

B K T L B K T L GB

Study 4 Hedgehog (+Attack & Defense Mechanism + Edge Conditions)

Study 5 Hexagons (+Attack & Defense +Different Weighting)

In Between

Chain Reaction

Video Game

Procedural Cave Generation

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2.1.1 SIM CITY

Simcity is a very well-known city planning game that gained a lot of popularity. The ultimate aim of the game is taking care of and maintaining the happiness of the citizens in a customized city while keeping a stable budget1.

At the beginning of the game player has empty city terrain that can be filled up with three types of zones such as in figure 2.1.1.2:

Residential

-

Green

Commercial

-

Blue

Industrial

-

Yellow

After placing zones on the field, the demand chart (figure 2.1.1.3.) gives hints about what the ratio of demand is for each of these zones. Parallel to the demand ratio, sims start to build their facilities (houses, shops and industrial buildings). In the game, a player has the control of increasing or decreasing the demand for these zones by providing or investing in public services. Certain zones require special attention and have particular needs. To illustrate, to increase the demand for housing units, the player needs to invest in more schools, hospitals, police and fire station...etc.

On the other hand, industrial zones look for population which can become their labour force and/or customers.

1

Krek, A. (2008)

A snapshot of the SimCity gameplay screen

FIGURE 2.1.1.1 (left, bottom)

The beginning of the gameplay, where the city terrain is empty.

FIGURE 2.1.1.2 (right, top)

The demand graph in the gmae interface.

FIGURE 2.1.1.3 (right, bottom) 20

21

22

In order to have a balanced budget, the player has to maintain a balanced demand chart as well. For example, if there are many houses concentrated in a zone and there are not enough job opportunities to cover the entire population, unemployment rate will rise rapidly. As a result, unless sims move to another city, they may start to involve themselves in crime, which decreases the ratio of safety and land value of the city. Additionally, the player cannot collect taxes from these criminals and this situation leads to budget problems.

Value Rate low-rate medium-rate high-rate

Residential

Commercial

Industrial

Taxes = = =

6ÂŁ 9ÂŁ 12ÂŁ

Residential, commercial and industrial facilities have different property values. Corresponding to these values, different taxes are collected.

There are some parameters that are taken into account by the sims to promote their houses in order to turn them into high-rated properties. To illustrate, if an area is facilitated well in terms of safety, health, education, parks and various entertainment zones, it is more likely to become a good zone for wealthy sims to invest and live in. Thus, these well-serviced areas then turn into high value areas (figure2.1.1.6) which then become more attractive for companies, tourists, and investors. Different land values in the city reflect the economy of the population of the sims. If more sims become poor, areas with low value will increase in ratio.

The city opinion pools actively affect the city development.

FIGURE 2.1.1.4 (left, top)

The city opinion polls revealing how the qualoty of the environment in the city is low.

FIGURE 2.1.1.5 (left, bottom) City land value heat map.

FIGURE 2.1.1.6 (right, bottom) 23

2.1.2 THE ‘GO’ GAME

We played a simple version of the ‘Go’ game where a player gets to place a circular piece in one of the grids each turn. When a piece is surrounded by other pieces on all four sides, it is killed. This simple rule led to the formation of diagonal grids as the game went on. In the second version of this game, we included the aspect of age in the pieces - when they reach the age of four cycles, they die of old age. This stopped the diagonal pattern from being formed. We related the game to the aspect of ‘overcrowding’ in the urban environment and buildings perishing with age.

KILLED CENTRAL PIECE

CONNECTIONS

AGE

An illustration depicting the working mechanics of the game.

FIGURE 2.1.2.1 (left, middle)

Diagonal pattern from the gameplay

FIGURE 2.1.2.2 (left, bottom) 24

2.1.3 SCRABBLE GAME

In this game, we reinterpreted the game of scrabble so that it relates more to the spatial requirements of housing. This is because rooms in a house always have to stay connected, like letters in a word, while certain spaces (letters) can be shared with other households (words) like Scrabble. Each letter represents a space in a house. The game rewards players who share spaces with other players’ entries. Points were assigned to each space (letter) and the player with the most points is declared the winner. Players play the game until all the letters in their possession are exhausted.

B

K

T

L

B K Bedroom: 10

Toilet: 8

Kitchen: 6

T

L

G

Living space: 4

B Garden: 2

POINTS

An illustration depicting one cycle of the Scrabble game with three players and the table of assigned points for each space.

FIGURE 2.1.3.1 (right, middle)

The result of a gameplay between three players.

FIGURE 2.1.3.2 (right, bottom)

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2.1.4 THE HEXAGON GAME

The game includes both the creation of the gameboard and operating the pieces. The board is assembled out of triangles, squares and hexagons. The hexagons act as bases from which each players’ coins spawn. The goal of the game is to conquer the board. The construction of the board proves to be very strategic for the player. The game hence was played in two ways - 1. The board was first built and the coins were spawned, 2. The board and the coins were simultaeously laid down each turn. This related more to the real world decisions where plots are bought and then occupied in the manner of strategic investments.2

“Board Game Cut-ups - Hexagons”, Studio Moniker, accessed Sept 8, 2018, https://vimeo.com/274850746 2

The original game depiction from Studio Moniker.

FIGURE 2.1.4.1 (left, middle) A result of the gameplay between 3 players.

FIGURE 2.1.4.2 (left, bottom) 26

RULES OF GAMEPLAY 1 Initially, players are to build the board according to their strategies.

2 All players have one base to start. All players start with 6 game pieces each.

3 Reinforcement pieces are collected in every 3 turns according to the points of conquered spaces.

4 Each player can make one move each turn. Players can only occupy another adjacent cell with their pieces if it is directly connected to the cell(s) that they are already occupying. In order to move the pieces to another cell, players should have at least two pieces occupying the Square spaces, or one in a Triangle or Hexagon Space.

Building the board

FIGURE 2.1.4.3 (right, top)

5 To conquer opponents’ cells, occupy their triangle with 2 pieces, or a square with 3 pieces - basically outnumbering your opponents’ pieces. Once taken over, the opponent will have to move out of that cell.

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GAMEPLAY ILLUSTRATION

Turn 1 beginning with three players spawning 6 of their coins in their respective hexagonal bases, in the built board.

TURN 1 FIGURE 2.1.4.4 (left, top)

Players begin to disperse their respective coins on the board, trying to conquer other players’ bases.

TURN 3 FIGURE 2.1.4.5 (left, bottom) 28

3 pts.

1,5 pts.

1 pts.

0 pts.

Players begin to get reinforcement pieces, i.e. more players spawned from their hexagonal bases.

TURN 6 FIGURE 2.1.4.6 (right, top)

Players get to choose the type of their reinforcement. While green and blue choose to reinforce with more pieces, red lays down an additional hexagonal cell to expand to.

TURN 7 FIGURE 2.1.4.7 (right, bottom)

29

Single orb

Double orb TYPES OF ORBS

Corner cell 2 neighbours

Edge cell 3 neighbours

Inner cell 4 neighbours

Expoding into other player cells

TYPES OF EXPLOSIONS 30

Triple orb

2.2 CHAIN REACTION AND HOW

IT EVOLVED 2.2.1 CHAIN REACTION - THE MOBILE GAME

While looking into games that encouraged consciously made smaller decisions having a larger impact on the game board, we happened upon the game of Chain reaction. It is a mobile phone game features across the iOS and Android platforms. The game is hosted by App Holdings1 on the Google store and by Jumping Pixel Games2 on the Apple store. In each of these platforms, the game has over a million downloads and has a whooping 4+ rating.

It is because of the fact of the unpredictability and fun factor in the game and due to its ability to accommodate up to 8 players at once.

The rules of the game are simple. The game is played on a grid, each cell of which can accommodate coloured orbs placed in by the players in turns. Each orb is in the colour of the player who places it. The maximum number of orbs a cell can hold will be less than the number of face-to-face neighbours it has. When the number of orbs exceeds this critical number, the orbs explode into their neighbours. Thus, as the illustration in the left suggests, a corner cell can be stable only with one orb, an edge cell two and an inner cell three. A special case of explosion occurs when an exploding orb enters a cell that is already occupied. In that case, the occupying orb changes to the colour of the incoming exploded orb. The explosions occur as chains when the maximum numbers are exceeded in the neighbouring cells as a result of explosion. A player who loses his coloured orbs on the board loses and the winner will be the one who conquers the entire board.

Chain Reaction – Apps on Google Play. (n.d.). Retrieved April 24, 2018, from https://play.google.com/store/ apps/details?id=com.BuddyMattEnt. ChainReaction&hl=en_GB 1

Chain Reaction Classic on the App Store. (n.d.). Retrieved April 24, 2018, from https://itunes.apple. com/gb/app/chain-reaction-classic/ id945592570?mt=8 2

Chain reaction rules and actions

FIGURE 2.2.1.1 (left)

Chain Reaction is a mobile game which shows global response to local inputs.

Source: App Store https://itunes.apple.com/gb/app/ chain-reaction-2-online-multiplayer/ id1016209529?mt=8

FIGURE 2.2.1.2 (right, bottom)

31

These images are screenshots of the final stages of a game between two players showing how results can constantly change and how one player who seemingly captures the entire board could lose.

FIGURE 2.2.1.3 32

While reaching final stages of the gameplay, it is observed that cells in their critical masses form a pattern. It is this pattern that is responsible for the chain of explosions, changing the winning and losing chances.

The interesting point of the game is that the winning side of the game can switch in an instant. The concept of explosions occurring in chains endears the fact of any player getting to win at any time. The set of illustrations on the left contain the final set of turns in the game played by two players - pink and blue. It is interesting to note how blue just starts with two cells in the first turn but proceeds quickly to conquer almost three fourths of the board by the sixth turn. When blue seems to be the winner of the game, the pink side makes one clever explosion and conquers the entire grid with the whole board entering a series of chain reactions. This chain reaction, that keeps on going until stability is reached in the board, helps patterns evolve out of gameplay - patterns that quickly shift and respond sensitively to local changes.

FIGURE 2.2.1.4

33

PRIVATE

Player Red:

Player Red:

PUBLIC PUBLIC PUBLIC PUBLIC

-public ic - PUBLIC 2 Public - PUBLIC 2 Public

Player Red:

-public ic - 2 Public - 2 Public

-public ic - 2 Public - 2 Public

34

SEMI-PUBLIC

PUBLIC

8 private - 4 Semi-public - 2 Public

PRIV.

SE-PRV.

SE-PRV.

PRIV.

SE-PRV.

SE-PRV.

SE-PRV. SE-PRV. PRIV.

SE-PRV.

SE-PRV. SE-PRV. PRIV.

SE-PRV.

PRIV.

PRIV.

PUB.

SE-PRV.

PRIV.

PRIV.

PUB.

SE-PRV.

PRIV.

PRIV.

PUB.

PRIV.

PRIV.

PRIV.

PUB.

PRIV. PRIV. SE-PRV. SE-PRV.

PRIV. SE-PRV. SE-PRV. SE-PRV. PUB. SE-PRV. SE-PRV. PRIV.

PRIV. SE-PRV.

SE-PRV. PUB. SE-PRV. SE-PRV. PRIV.

PRIV. SE-PRV. PRIV.

SE-PRV. PRIV.

PRIV.

SE-PRV.

SE-PRV. PRIV.

PRIV.

SE-PRV. PRIV.

PRIV.

SE-PRV.

SE-PRV. PRIV.

2.2.2 APPLYING THE GAME TO ARCHITECTURE A SPATIAL ENVISIONING At each turn, a player places one of his/ her spaces in a cell. The rules of explosion and conquering are similar to the original game.

FIGURE 2.2.2.1 (left)

The pattern evolved out of the game is spatially perceived by converting the orbs into their appropriate spatial categories.

FIGURE 2.2.2.2 (right, top)

As a direct translation of the game into architecture, the orbs were replaced with spaces. Single orbs were translatd to private spaces, double orbs to semi-public and triple orbs to public spaces. At the beginning of the game, each player is given a specific set of spaces - in this instance, each player has 8 private spaces, 4 semi-public spaces and 2 public spaces in the beginning. When there is an explosion, exploding cell becomes an open space that can no longer be inhabited by a player’s move unless inhabited by neighbouring explosions. The pattern of spatial assemblies and open spaces formed relationships and certain patterns emerged out of gameplay.

35

Case 1: -Dots are exploded by adding fourth

Case 2: -Dots are exploded by adding fourth.

-Conquers the opponent dots.

Case 3:

-Dots are exploded by adding fourth -Conquers the opponent dots.

-Starts to colonize since some of oppo dots are ready to explode too.

Case 4:

-Dots are exploded by adding fourth -Reds cannot conquer the opponent d which have a wall in that side.

Case 5: -Dots are exploded by adding fourth -Greens conquer the opponent dots.

Case 6:

-Dots are exploded by adding fourth -Reds conquer the opponent dot with wall since the attack is performed an o side without a wall.

36

WALLS AGAINST EXPLOSION

The previous gamification experiment had a major shortcoming in the aspect that the cells didn’t have a mechanism to prevent incoming explosion. This contradicted with the idea of privacy included in the translation of the orbs as private, semi-private and public spaces. Hence, the idea of walls, that was researched in the game of Hedgehog is tried here. Along with a set of spaces a player is given in the beginning, a set of walls are also given. The player needs to use them in a tactical manner to better colonize the grid.

The game also had features for trade-offs where players between themselves could trade one space or wall for another. However, the exchanges were arbitrary without a fixed value being set to the elements. Also, the fixed number of spaces at the players’ disposal led to the grid being incomplete, leaving no observable pattern. Surprisingly, there is more order when there are no walls.

EXPLODING 3D

The game, when played out on a three-dimensional grid, allowed for explosion along the vertical dimension. This was particularly relevant when considering the implication of the game in an architectural scale, since a building needs to expand vertically as well. The original gameplay, in a two-dimensional grid needed to be revised to proceed in 3D.

Considering two different scenarios where the game is played without walls and with walls, leads to different patterns of evolution and explosion.

FIGURE 2.2.2.3

Chain reaction can be envisioned along a three dimensional grid by explosions occuring in the vertical dimension as well.

FIGURE 2.2.2.4

37

2.2.3 CODIFYING THE GAME CODING THE ORIGINAL GAME

For us to be able to improve on building the game and to understand the patterns it generates, it was essential to first have a codified version of the original game of Chain Reaction.

The game engine Unity3D and its C# scripting support was suitable when trying to combine the game rules as scripts to mesh objects as spheres. The elements in the game are split in three levels and each level has one or multiple scripts associated with it, that give each level a specific behaviour. The sketch on the top-right demonstrates the initial ideas behind the codification of the game. The three levels include the game board, the grid units contained in the game board and the sphere unit accommodated in a grid unit.

The game board controls the player based game-play maintaining the turns and also maintains how the chain reaction occurs by conduction regular counts on the number of spheres in each cell. The grid units recognize click and pass information to the game board to check for explosions. They maintain the number of spheres they hold and periodically inform the sphere unit they enclose to update their mesh and colour.

The spheres can be converted into spatial mesh objects with the floor color denoting the inhabiting player. On explosion, the spaces enclose a green open space in the centre.

FIGURE 2.2.3.1 (left, bottom)

The game when coded in Unity3D, had three main levels of object classes - the Game Board, the Grid Unit and the Sphere. Each of these objects have behaviour scripts attached to operate the game.

FIGURE 2.2.3.2 (right, top)

The game has four main behaviour scripts - the Game Board Controller script, the Player Controller script, the Grid Unit script and the Space Occupant Manager script.

FIGURE 2.2.3.3 (right, bottom) 38

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SPHERES TO SPACES

To have a more accurate representation of the game in a spatial sense, spheres are replaced by spatial units with walls denoting the subdivisions. The rules of the game play remain the same however, one major alteration is made. The idea that an incoming exploding space when coming in contact with another player’s space, changes the ownership of the space being hit, is flawed from seen from a real world perspective, where conquering spaces and rendering a player homeless is brutal. Hence, the concept of shared grids in incorporated. When one player’s space explodes into another place’s space, both players co-exist in that cell.

This concept of co-existence requires two new updates in the coding level of the game function. Each grid unit needs to maintain a list of the players occupying it and the list needs to be in correspondence to which side of the grid is occupied by which player. The C# class Dictionary proves to be an efficient data structure to store the Player ID tagged by the side in the cell. Thus, each grid unit has a dictionary of its occupants. This occupant information is passed on to the Space Occupant Manager script in the enclosed spatial unit to update its floor colors.

Another update is the orientation in which the space unit is instantiated on an incoming explosion. Normally, a spatial unit can be involed in two ways - by a click and by an explosion. A click doesn’t specify a side of instantiation, but an explosion has a specific side of explosion. Hence, the spatial unit invoked needs to understand the side of instationtion to know in which degree of rotation it must be instantiated.

Spaces when exploding onto other spaces create shared grids in which different players can co-exist together in the same cell.

FIGURE 2.2.3.4 (left, bottom)

Upon explosion, the dictionary classes of the neighbouring cells accommodate the exploding spaces in the appropriate side.

FIGURE 2.2.3.5 (right) 40

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If the side of instantiation contradicts with an already occupied side, another side of the next priority is occupied. This is evident in the cell towards the right where the pink player is accommodate at the right instead of left.

FIGURE 2.2.3.6 (left)

This experiment concerns critically populating four adjacent cells with four players. The image suggests how the dictionary classes of the cells are initially empty.

FIGURE 2.2.3.7 (up)

When the four adjacent cells are critically populated, one click on any of these cells, begins a continuous chain reaction that causes multiple explosions at the same time. This hangs up the game play.

FIGURE 2.2.3.8 (pg 44)

Beginning of the chain reaction.

FIGURE 2.2.3.9 (pg 45)

Each cell on the verge of explosion registers its address with the Explosion Scheduler. Based on the order, the scheduler checks if the grid is ready to explode and triggers the explosion one by one.

MULTIPLE INCOMING EXPLOSIONS

The coded version of the game held good when dealing with simple explosions, but when there where situations when critically-massed cells were next to one another and an explosion was invoked, the game crashed because there were multiple incoing explosions into a single cell, with players much exceeding the maximum amount of players that could be accommodated in a cell.

To overcome this issue, a new script called an Explosion Scheduler is added to the game board. SImilar to a Print task manager, this script maintains a list of the explosions incurred in a grid and executes them one-by-one thereby avoiding multiple explosions.

This implementation also required the inclusion of the concept of ‘Spill players.’ It was observed that during continuous chain reactions, there were occurrences when a cell that has only four sides needed to accommodate about six players. In such conditions, the dictionary data structure was insufficient to hold their information. Thus each grid unit was given an extra Array list to hold Spill players when they occurred and later explode them into their neighbouring cells in the order of the sides priority (left - right)

FIGURE 2.2.3.10 (pg 46)

The final outcome of the chain reaction.

FIGURE 2.2.3.11 (pg 47)

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2.2.4 EMULATING THE REAL WORLD INCLUDING COSTS

With the game being able to accommodate multiple players and infinite chain reaction, inclusions of the real worls spatial planning customs were tried. The first of it was costs. As the illustration demonstrates, when a grid is free of cost, inhabitants will only want to sprawl and occupy as many grids as possible without the need to subdivide and share. On the other hand, if the cells are all priced arbitrarily, a situation of overcrowding occurs. Thus to maintain a healthy living condition with the cost situation, the concepts of rules and rewards are important. The rule that a cell can only accommodate a certain number of inhabitants beyond which explosion occurs and the reward that an explosion can award the occupants with a private open garden space in the centre fosters players to try out subdivisions and sharing.

The three grids in the illustration demonstrate how the game play changes when there are no costs, when there are only costs, and when there are cost, rules and rewards involved.

FIGURE 2.2.4.1 (left) 48

PRICING THE GRID

If all the cells in the grid are priced equally, the moves made by players won’t be well thought out. Also, in reality, not every plot of land and not every property is priced the same. In the context of the game, if explosion is considered the reward, it is obvious that the cells with low critical masses have higher chances of explosion, thereby higher advantage. Hence if the price of the cell is made to be inversely proportional to the critical mass, a balance of cost and reward can be obtained.

A cell’s price is determined by dividing a base cost by the number of face-to-face explodable neighbours it has.

FIGURE 2.2.4.2 (right)

49

50

SCORING

At each stage of the game, the residential space a player holds and the area of garden space each player shares is calculated. These help determine the efficieny of the player’s move.

FIGURE 2.2.4.3 (left)

At the end of the game when an infinite chain reaction begins, the scores are calculated as thus - Player 1: 788.69; Player 2: 690.03; Player 3: 1008.55 and Player 4: 655.14.

FIGURE 2.2.4.4 (up)

The code is changed to incorporate spill players in the vertical dimension. It is tested by two players inhabiting four adjacents cells in the grid.

One cell in the grid is found to contain one spill player (blue) who gets accommodates in the top floor of the structure.

FIGURE 2.2.4.5 (pg 52-53)

The rewards of the game are only evident when the players are awarded with scores. A Score calculator script is added to the game board to maintain this. The main algorithm behind the calculation of the scores is: Score = (Spatial spread + Garden space) x Money remaining

The game play is such that the players start with a specific amount of money. In order to build on an unoccupied cell, a player needs to buy the cell. Since there is no bank in the game, the money spent by one player is distributed to the other players. Also, a player can only invoke a space in an occupied cell only if there is at least one of its spaces in that cell. However, this doesn’t invoke a cost. Also, when a cell is conquered by explosion, it doesn’t cost the player.

Since the grids are shared, the grid being conquered can no longer be the finishing point of the game. Also, the sharing system makes sure that the players end up with the same number of spaces. So the winner is decided by how efficient he has spent his money in having the most space and most garden space access.

51

52

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GOING 3D

With the costs and scores resolved, the vertical explosion of the grid and the game going 3D is studied. Rather than directly instantiating a grid in the other floors, it is important for spaces on the top floors being instantiated by explosion. To passively invoke the three dimensional version of the game, the data of the Spill players in the game setup is utilized. The spill players often lead the grid to enter an infinite chain reaction. To break this and to stabilize the spatial planning, the spill players are accommodated in a floor higher than the one of explosion. Thus, a vertical instantiation is done and further explosion is carried out.

INCLUDING NON-BUILDABLE GRIDS

To bring the game more closer to reality, it is important to have grids that are already built on, or grids that cannot be built on. Thus, to have an existing built setup on the game board, the game play is split into two modes - Build mode and Play mode. The build mode allows players to set the scene, i.e., the built condition, on which the game is played. Once the built grids are created, the game is tarted by click on the Play button. The costs of the cells are decided based the number of sides it can explode into. Thus, rather than the corner, edge and inner cell conditions, the prices vary more organically along the grid leading to better patterns being evolved.

2.2.5 IMPLICATIONS

The ability of the game to engender global level changes by means of local level rules and interactions has wider implications when considered on an urban level than on a building level. The ability of the game to accommodate the strategic moves of multiple different players one grid can be exploited to build on the thesis of engendering shared and dense living communities through gamification in urban environments.

Non-Buildable grids declared.

FIGURE 2.2.4.6 (left, bottom) The build mode gets executed first where the existing structures in the scene is created. Then the play mode begins where the buildable grids get priced.

The build mode requires the grid units to have an extra characteristic of Buildability. Coded as a boolean, its values are set in the build mode and are utilized in the Play mode.

FIGURE 2.2.4.7 (right) 54

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2.3 COMPUTATIONAL UNITS 2.3.1 USER PROFILES

The important aspect of gamification is to understand the kind of users it needs to address. Since the game is to understand housing at a micro level as well as the urban level, the user groups need to be defined appropriately. Beginning from the level of a house, to understand the anthropometrics and ergonomics that lead to spatial design, the aspects of age and gender are basic categories to consider. Also, the criteria of physical disability is added to understand their special spatial requirements. Hence the user groups would be babies, boys, girls, men, women and users with special needs. Users of younger ages are added to discriminate their spatial needs. But they do not have spatial decision-making abilities. Hence, they are categorized as ‘passive’ users whereas adults are ‘active’ users.

Neufert standards gives details on the various spaces in a residential unit and the dimensions and clearances each furnishing would require. The standards are used to design modular spatial units that would then serve as basis for the game development.

Given the user groups, it is also important to consider the fact that users don’t use all spaces alone. Spaces are used by many people in most cases and the spatial design would differ with the nature of the people it is accommodating. Hence combinations of the user groups are derived by matching each of the six user groups with one another to see how all the residential units would emerge.

User groups

FIGURE 2.3.1.1 (right, bottom) User group combinations

FIGURE 2.3.1.2 (right, top) 56

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2.3.2 NEUFERT SPATIAL STANDARDS

The primary spaces in a residence would be bedroom, restroom, kitchen, living room and an utility area. Also, there can be common areas, in the case of co-living facilities that might include social spaces like libraries, gyms or play areas. Each space is designed keeping in mind the various activities, that would happen there. In addition to the main activity in a space, ancillary spaces for storage are also added. Many options are created for each of these spaces, with the various user groups that could be accommodated there. Each option is designed in a 2m x 2m square grid which can be readapted into any primitive unit. The furniture sizes are from the Neufert standards.

There are 16 options for the bedroom, 9 for the restroom, 13 for the kitchen, 2 for the living, 2 for utility, one for storage and 7 options for common areas - including, workspaces, creche, gym, play area and library. Each option are is coded with the people who can use it. These act as the basis for the computational units that the game will operate on.

The basic standard 2m x 2m units derived out of Neufert standards, that can be readapted into a primitive shape, decided lateron.

FIGURE 2.3.2.1 (pg 59-66) 58

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2.3.3 SHARING CHOICES

Sharing is an underlying principle in co-living environments. However, sharing cannot be forced and each person have their own physical and psychological approximations of the place they live in. Hence, users need to make conscious decisions of what spaces they wish to share, with whom they wish to share and the level on which they are willing to share. Users also have the option to totally opt out of sharing and enjoy a fully privatized environment.

Also, there are options where one would share the space with only his/her co-inhabitants or family and with no one else. This leads us into splitting sharing choices in two broad categories - Closed and Open sharing. Closed sharing involves two pre-acquainted people sharing a space. Essentialy, they live together and they share the space. Close sharing isn’t exactly social sharing in the fullest sense, however it offers light into how co-inhabiting individuals can share and redefine their spaces. Open sharing refers to the common way of sharing with unknown people. This opens up to new possibilities and better community level interaction.

The sharing choices matrix

Sharing also comes with the added advantage of economy and getting to use more space than you actually buy for yourself. Also, it gives a good sense of community in the neighbourhood.

Hence, the game fosters sharing through offering very subsidized price values for sharing. Thus, the user behaviour tree would be such that, he/she would have to choose each space, i.e., the bedroom, kitchen, restroom, living and utility based on his/her sharing preferences. Also, he/she would have to make decisions for the passive users, i.e., children in their family. Based on their preferences, the game suggests the appropriate spatial unit that would fit their needs.

FIGURE 2.3.3.1 (right, top) 67

Behaviour tree - Adult Male - Bedroom

FIGURE 2.3.3.2 68

Behaviour tree - Adult Male - Restroom

FIGURE 2.3.3.3

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Behaviour tree - Adult Male - Kitchen, Living, Utility

FIGURE 2.3.3.4 70

Behaviour tree - Adult Female

FIGURE 2.3.3.5

Behaviour tree - Adult with special needs

FIGURE 2.3.3.6

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2.3.4 SHARED NEUFERT

Sharing without a proper monitoring can very easily lead into overcrowded spaces especially, in a city like London, where there is severe housing demand. Hence, the game inforces rules on how many people can use a space. Neuferts occupancy data can be handy, in framing these rules.

However, according to the conventional Neufert, the occupancy data only suggest the maximum number of occupants a space can handle at a time. But not all people are going to use the space at all times. Meaning, the space can be used by different people at different time scales. Thereby, a space can actually accommodate more people than it is actually designed for. To have a better hold on this, it is important to understand the time schedules of the various user groups sharing the space. Primary references for this idea are derived from a previous AADRL project called Cloud Living by Leo Bieling, Ariadna Lopez and Basant Elshimy in the Nahmad Bhooshan studio. They have classified users into three groups - constant, sporadic and recurring based on the amount of time they spend in the residence, with constant being the most, sporadic being the least and recurring being frequent. They have drawn detailed time scales of user groups in their spatial model and how that influences the spatial usage.

User profiles Source: Cloud Living – Combinatorial Subscription Living, Leo Cladius Bieling, Ariadna Lopez, Basant Elshimy, AADRL 2018, pg 27 https://issuu.com/leoclaudiusbieling/ docs/170106_finalbooksmall

FIGURE 2.3.4.1 (left, bottom)

Spatial requirements of the various user groups Source: Cloud Living – Combinatorial Subscription Living, Leo Cladius Bieling, Ariadna Lopez, Basant Elshimy, AADRL 2018, pg 37 https://issuu.com/leoclaudiusbieling/ docs/170106_finalbooksmall

FIGURE 2.3.4.2 (right) 72

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2.3.5 SOCIAL CONDENSER

The term Social Condenser is basically from the Soviet constructive theory, but it is highly practiced in Architecture. Central to the idea being architecture’s influence on social behaviour, the intention in advancing the theory would be to break down the social hierarchies and top down planning and make public spaces more socially equitable.

OMA in its book Content describes a social condenser as,

Programatic layering upon vacant terrain to encourage dynamic coexistence of activities and to generate through their interference, unprecedented events.1

We are particularly interested in propagating this idea in relation to the shared spaces that evolve in the design. More than just sharing the basic spaces, there is also a possibility of new spaces emerging. To explain this better, consider occupant A and occupant B existing as neighbours in a set up. They are like minded individuals with the same interest towards sharing. Let’s say A owns a living room, but B doesn’t. In this condition, B asks A if they could share the living room and A agrees. In this scenario, the shared space is the living room, which is already a primitive unit, i.e., no special space has evolved. However, consider an alternate scenario were B too owns a living room. In this case, when they both share their spaces, they are landed with two living rooms. In this condition, the common inclination would be to convert one living room, into a different space. This space would heavily depend on their common field of interest. Say both are students. In that case, they might want to have a space to work after university. So they might want to convert one of their living rooms into a workspace. Now this is a social condenser, which brings neighbours closer and social behaviour evolve.

2.3.6 EXPERIMENTAL GAME

To better perceive the concept of sharing and the decision making tree, a small experimental game was plays, considering four families, amounting to a toal of 12 users. Each user was definied with a particular age group, gender, occupation and social nature and the game was developed. The most important feature of the experiment was how the model can accommodate to change.

Since, housing is not static in a busy city as London, the residence design must be able to adapt and reconfigure to changing user needs. Thus, every family is pre-defined with a situation where it needs to expand. Profile cards are made for each user. Occupation is broadly classified as ‘Employed’, ‘Student’ and ‘Stay at Home’.

McGetrick, Brendan; Koolhaas, Rem, Ed. (2004). “Content”, pp. 73. Taschen, 2004. ISBN 3-8228-3070-4.

1

Profile cards of the twelve users from the first game play.

FIGURE 2.3.6.1 (right) 74

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FAMILY #1

Michael and Caroline are a young working couple with their baby Judith. The baby is fast growing and they are thinking of getting a bedroom space for her.

FAMILY #2

Jenny and Michelle are two students who live together and share all their facilities and spaces. They are later joined by their friend Katie, who will need a new bedroom to stay in.

FAMILY #3

Jenny and Michelle are two students who live together and share all their facilities and spaces. They are later joined by their friend Katie, who will need a new bedroom to stay in.

FAMILY #4

John is a professional who works in London and is very private. He is later joined by his brother Don who also gets his own bed and restroom spaces.

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The spatial choices made by Family 1 from the behaviour tree

FIGURE 2.3.6.2 (right)

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Choices made by the four households, before expansion

FIGURE 2.3.6.3 78

Choices made by the four households, after expansion

FIGURE 2.3.6.4

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2.3.7 DECIDING ON THE PRIMITIVE UNIT

Choosing, hexagons and rectangles as possible primitives for the spatial units, we tested their sharing capacity. After iterations by combining same and different units, it was evident that hexagonal primitives worked better in terms of sharing as they offered more face-to-face connections. Combination of smaller and bigger housing modules, heled create viable communities with expansions and creation of shared social spaces.

UNIT A

1 recurring people

CELLS 1/4 Restroom 1/4 Kitchen 1/2 Bedroom AREA 14 sqm SHARING DESIRE Kitchen (+,+) Living room (+,-) INTEREST Gym

CELLS 1/2 Restroom 1/2 Kitchen 1 Bedroom AREA 28 sqm SHARING DESIRE Kitchen (+,+) Living room (+,-) INTEREST Gym Laundry

UNIT D

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UNIT B

2 sporadic people

UNIT E

UNIT C

2 constant people + 1 baby CELLS 1/4 Restroom 1/4 Kitchen 1/2 Bedroom AREA 14 sqm SHARING DESIRE Kitchen (+,+) Living room (+,-) INTEREST Workspace Laundry

UNIT F

2 recurring people

2 recurring people

2 constant people

CELLS 1/2 Restroom 1 Bedroom AREA 21 sqm SHARING DESIRE Kitchen (-,+) Living room (+,-) Restroom (-,+) INTEREST Entertainment

CELLS 1/2 Restroom 1 Bedroom AREA 21 sqm SHARING DESIRE Kitchen (-,+) Living room (+,-) Restroom (-,+) INTEREST Gym Entertainment

CELLS 1/2Restroom 1 Kitchen 1 Bedroom 1 Living room AREA 14 sqm SHARING DESIRE Living room (+,+) INTEREST Gym Laundry

Spatial units derived out of the rectangle and hexagonal primitives

FIGURE 2.3.7.1 (left)

Expansion with only F units

FIGURE 2.3.7.2

Expansion with different units

FIGURE 2.3.7.3

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2.4 GAMEPLAY SCENARIOS

By simulating the possible scenarios of gameplay, we were able to speculate and observe how the social condensor spaces evolved differently in each case. Options involving user decisions such as if they want to extend their tenure of a shared space offering, or cut it short and make it private are explored and how these decisions affect the block layout, the amount of shared spaces and the connectivity between the people is well understood.

82

In Scenario 1, after one UNIT F invests a open shared living space. Different types of units start to come and create a shared community cluster.

When the shared living room reaches the density limitation according to shared Neufert scale, it expands and creates an additional communal space including gym and laundry space which are the overall interests of this cluster 2.

OPTION 1

SCENARIO 1 FIGURE 2.4.0.1

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In second option, UNIT F privatized a new living space as a reward of the expansion, as the other units keep continue on sharing the old invested living space.

OPTION 2

SCENARIO 1 FIGURE 2.4.0.2 84

This scenario happens when a new user’s grid completely blocks out the daylight access of another user’s grid to reach its interested spaces.

The blocked user as a renumeration for the blockade is given an option to expand vertically with more access to sunlight and connected with the lower floor spaces by a vertical core.

The vertical circulation cores thus formed can be shared by the community resulting in more growth in the upper floors.

SCENARIO 2 FIGURE 2.4.0.3

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In Scenario 3, 2 Unit C groups need to expand for another room, since their children have outgrown the existing volume.

They make a deal with 2 Unit E groups who needs to share a kitchen, by sharing their space with them to form a bigger cluster, in return for expansion upstairs. C groups gain rooms for their children, and E groups share a larger communal space as helping to merge 2 different clusters. Besides, the new big clsuter become able to grow on upstairs.

SCENARIO 3 FIGURE 2.4.0.4 86

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Scenerio 4 illustrates a situation where some initially private kitchens are transformed into different types of use after sharing with a community when Shared Neufert calculations come into the game.

The shared spaces get altered and personalised based on the interests of the sharing users such as a gym, a work space and a laundry.

SCENARIO 4 FIGURE 2.4.0.5 (left) 88

2.4.1 BLOCK BUILDING This illustration shows how the block which is base of previous scenerios, grows and evolves over the years through having different units and clusters.

Clusters 1 and 2 get formed.

FIGURE 2.4.1.1 (right) 89

To illustrate, the diffrrence between Cluster 2 and Cluster 3 comes from having an Unit F which has a living space. Whereas, Cluster 3 has smaller communal space, Cluster 2 able to create a bigger cluster through using their communal living space.

FIGURE 2.4.1.2 (left)

This is the case when 2F Units come together and invest a shared living space.

2 shared living space creates more face to get more people who want to share a living space. Therefore Cluster 5 able to become a larger community.

FIGURE 2.4.1.3 (right) 90

91

And this is after scenario 4, 2 clusters merge again, Connected communal spaces and circulations. Connects the dense community.

FIGURE 2.4.1.4 92

CLUSTER 7 + CLUSTER 8 = CLUSTER 9 2A + 8B + 4C + 2F

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2.5 GAME INTERFACE

The game operates on the data from the users. Hence, it is important for the interface to collect the required information, at the right sequence and pace. But in an age where everyone’s profiles and basic information are already available online, with information of their freidns and relationships, it only makes sense if the game has an interface to connect with the Social networking applications.

2.5.1 SNS CONNECTION

The goal of the game being building houses along with building the community, it is important to take into account the interrelationships existing among people. Rather than explicitly collecting them, it is easier to set up a Social Network login for the game. Most social networks and Web domains offer Login APIs which can be used, and user profiles and data can be accessed at their permission. We chose Facebook because, it is the most famous social network at the moment, and it has facilities to access the user’s connections. Also, the persons’ interests and friends’ compatibilities can be used to identify their housing and community preferences.

The flow chart showing how data can be mined out of a social network connection

FIGURE 2.5.1.1 (left) 94

The start screen of the App featuring the Facebook login button

FIGURE 2.5.1.2 (right, top)

The app requesting access to ‘facebook. com’ for the Sign-in

FIGURE 2.5.1.3 (right, middle)

The redirected Facebook sign in page to connect to the Block party app

FIGURE 2.5.1.4 (right, bottom)

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The welcome screen of the App, using the username got from the user’s Facebook profile

FIGURE 2.5.2.1 (left, top)

The family creating screen, with the main user preloaded with details from the user’s Facebook profile

FIGURE 2.5.2.2 (left, middle)

Additional members added and the profiles updated

FIGURE 2.5.2.3 (left, bottom) 96

2.5.2 CREATING THE HOUSEHOLD

From the Facebook profile, the user’s name, date of birth, gender and friends’ list are collected. The first screen of the game interface would be to collect the household details of the user. The primary member would be the user who logged in and he/she is allowed to add four more members. Occupation of the users are loosely classified into three categories - Employed, Student and Stay at Home. Users are given avatars to associate themselves with and they change with their age group and gender.

2.5.3 FINDING USER INTERESTS

Since the game dwells on the creation of shared spaces between inhabitants, by means of matching interests, we have framed the interface to ask of the users’ interests right after they finalize their household setup. However, the interests that we inquire about cannot be arbitrary, and need to have spatial implications.

Referring the book ‘Living Closer’ by Studio Weave, we came across several case studies of co-housing setups in the UK and the various shared social spaces they offered. We analysed the personal interests that lead upto the development and functioning of those social spaces.

Rather than explicitly offering these social spaces, by a top-down planning, it is better if they are created by the users themselves by mutual sharing. For that to happen, it is important that users with similar interests get together. More than just grouping them with their already existing friends and acquaintances, the game needs to have the sufficient intelligence to suggest users with the same interests and sharing preferences. Hence, we plan on deploying machine learning algorithms, to compute and locate users with similar interests. Since, this would involve trying to group users with similar interests, we use clustering algorithms like Principal Component Analysis (PCA) and t-distributed Stochastic Neighbour Embedding (t-SNE) clustering.

The screen letting the users to connect their respective avatars to the interest bubbles

FIGURE 2.5.3.1 (right)

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READING

LIBRARY

UTILITY

LAUNDRY

FITNESS

GYMNASIUM

CHILD IN THE HOUSEHOLD

CHILD CARE

HEALTH & RELAXATION

SAUNA

WORK

WORKSHOP

WORK

PLAY

CHILDREN’S PLAY AREA

FITNESS N’ PLAY

GARDENING

VEGETABLE GARDEN

GARDEN

FITNESS & RELAXATION

YOGA SPACE

ENTERTAINMENT

WORK

WORK SPACE

FOOD

ENTERTAINMENT

MUSIC STUDIO

ART & CRAFTS

CRAFTS ROOM

ENTERTAINMENT

HOME THEATRE

UTILITY

STORAGE SPACE

FOOD & COOKING

PANTRY

PET IN THE HOUSEHOLD

PET/ANIMAL CARE

ELDER IN THE HOUSEHOLD

ELDERLY CARE

FOOD & COOKING

CAFE

Each social space is condensed to its basic need or interest backing which is then linked to its interest category. Spaces that are an outcome of utility such as Laundry spaces are left out since they do not invoke any personal interest of their own. The interests are grouped under five categories of Work, FItness n’ play, Garden, Entertainment and Food. The values of these interest categories are what will be used to comoute the clusters in the machine learning framework. The game interface allows the user to connect all avatars to his/her personal interest bubbles.

2.5.4 CHOOSING THE SPACES

The users are next led to choose their primary spaces.

The list included will be those from the behaviour tree - Bedroom, Restroom, Kitchen, Living and Utility. Each space when selected needs to be specified with who the main occupant will be. A space needs atleast one occupant. Then, the sharing preferences of the space needs to be mentioned. If it is provided as ‘No sharing’, no additional member can be added. If it is ‘Closed sharing’ additional members can be added. However, this cannot exceed the maximum allowable occupant count of the space, which is maintained by the game.

If the sharing preference is ‘Open sharing’ it opens up more nuanced optioned. Open sharing is a kind of hybrid sharing option, which lets both closed and open sharing to occur at the same time. Hence, members of the household could still be added. The additional option is that, the user can choose whether to share during occupancy, or during absence. Share during occupancy would mean that other people can use your space while you are still there, while during absence would mean that your space is shareable only when you aren’t there. Hence, the latter option, would require you to specify the time that you would use the space.

This is where the Shared Neufert standards come into play. These options can be regularly updated.

The screen letting the users to connect their respective avatars to the interest bubbles

FIGURE 2.5.3.2 (left)

The app screen where the user gets to choose the various spaces for the house

FIGURE 2.5.4.1 (right)

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2.5.5 LEVERAGING APPLE’S ARKIT

Augmented reality is a simulated environment that overlays Computer Generated Imagery onto the Real world footage, got throught the device’s camera. All software platforms support AUgment reality and the support is extended to many compatible table and mobile phone devices. The advantage of AR pver other forms of Artificial reality applications is that, it connects more to the real world.

We have used the ARKIT plugin realeased by Apple for iPad and iPhone devices. The SDK is available for Unity3D Game Engine, using which the game is developed. The ARKIT had three stages of releases. In the 1.0 version, it allowed to detect planes and features and overlay objects on it through Hit testing. The 1.5 versions, lets the plugin to detect Image Anchors - Preset Images that can be identified in the real world and can be used to anchor the simulated world. The 2.0 version is the latest release at the moment allowing Object anchors - Pre-scanned objects serving as Anchor points on which the game can be overlayed. Another big feature with the 2.0 release is the provision for Shared AR. Upto four devices can be connected to the same AR environment, when connected to the same wireless network. Unity offers support through their Shared Spheres framework, where one of the users hosts the AR environment and others join it. This is particularly useful when many users want to co-build their houses.

Since we deal witht he scale of urbanism, it is essential to simulate the whole of London and locate the housing on it. We have used a plugin called WRLD, which offers an Unity SDK for development. The plugin takes data from the OpenStreetMap API and has custom built landmarks and textures for an overall enriched experience. It also lets new objects to be added to the cityscape through Geo-locating. The plugin is also compatible with the ARKIT, making it easy to integrate with the original game interface.

Using the ARKIT in iPhone 6S to detect a plan and overlay the map of London on it.

FIGURE 2.5.5.1 (left, up)

The Shared Spheres framework, that lets users connect into a shared AR environment. Here, the green spheres are by User 1 and the blue spheres are by User 2, in the same shared environment.

FIGURE 2.5.5.2 (left, middle)

A screenshot showing the WRLD map built for iPad and displayed using Augmented reality

FIGURE 2.5.5.3 (left, bottom)

A photograph showing the AR environment where the map of London lies against the real world environment

FIGURE 2.5.5.4 (right, bottom)

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03

BLOCK RESEARCH 3.1

3.2

URBAN PRECEDENTS 3.1.1 BARCELONA 3.1.2 PARIS 3.1.3 VIENNA BLOCK FRAMEWORKS

105 105 115 123 138

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3.1 URBAN PRECEDENTS 3.1.1 BARCELONA

El Raval, an old city, was enclosed by giant city walls until the expansion in the 1850’s (figure 3.1.1.4). With Industrial Revolution, the city started to trade more. As the city became prosperous, existing residents wanted to expand their units while more people flooded into the city. The density of the city skyrocketed with the increasing demand. El Raval became the popular area for factories and high-rise tenement blocks very soon.1 By the time, as El Raval was getting denser and unhealthy living conditions were appearing in streets such as insufficient sunlight, air ventilation. (figure 3.1.1.2) In fact, big arches were firstly placed on streets randomly to serve as bearing structure for further constructions of apartments to answer the demand of growing. (figure 3.1.1.3)

1859, Ildefons Cerdà approached urban planning from a data-driven statistical perspective2. The city wall was demolished to allow the gridal expansion of Cerdà’s l’Eixample which was four times bigger than of El Raval. (figure 3.1.1.1) Cerdà’s plan was the first example of urban planning which was carefully designed scientifically. Data-driven design applied to calculate the optimal distances between neighbouring hospitals, schools, markets & civic administrative nodes. He considered about the professions which are needed by the population. Likewise, he calculated the volume of atmospheric air needed per person3.

“Barcelona Urban Development and Change.” Barcelona Field Studies Centre, geographyfieldwork.com/ BarcelonaUrbanDetail.htm.

1

2

Burry, Mark 2013.

3

Bausells, 2016.

An example plan by Cerda

FIGURE 3.1.1.1 (left, top) Neighborhood in El Raval

FIGURE 3.1.1.2 (left, bottom) A street in El Raval

FIGURE 3.1.1.3 (left, bottom) The city plan of El Raval

FIGURE 3.1.1.4 (right) 105

Cerda’s gridal urban blocks initially were designed as not only being simple island blocks but, serving as open public areas through serials of connected interways between blocks (figure 3.1.1.5).

Moreover, different configurations could be foreseen for the further urban development which is based on various scenarios in mass articulations. Figure 6 demonstrates that, in the scope of Cerda’s interway idea, which is crucial for his plan as serving to the public, providing enough ventilation and sun light penetration. The further mass development had to be referencing those interways.

That explains that Cerda ‘s plan could be disrupted without a control in detail, but foreseeing the further block configuration to offer some visionary possibilities (figure 3.1.1.6).

In general, despite of some of the problems appeared to apply his plan, the autonomous block development demonstrates that Cerda’s urban plan for Barcelona was a basic armature which allows self-propelled, self-organised development in a sense4.

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4

Burry, Mark 2013.

Basic island vs Cerda’s

FIGURE 3.1.1.5 (left, top) Different block configurations

FIGURE 3.1.1.6 (right)

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The interways were designed to provide good quality of air ventilation between blocks, to enable residents to access sufficient sunlight and having a clear view. They also were to serve as a garden to create a feeling of peaceful sub-urban life in the city, besides working as safe path ways for pedestrians. (figure 3.1.1.7)

Although many construction works did not follow Cerda’s building laws through suffering from lack of regulations, Cerda was strict about his building laws about his plan. For him, buildings cannot use than 50% of the block’s surface, and could only use two of the block to allow space for gardens. Likewise, there was a 20 m height and 15 to 20 m depth limit as well5. Further developments of the urban blocks caused a big amount of shrinkage in open interways and gardens. Likewise, uncontrolled development led different articulations and patterns in the urban grid through ignoring Cerda’s regulations (figure 3.1.1.8) and (figure 3.1.1.9).

Although the Plan Cerda remained the official Development Plan for Barcelona, by the time after Cerda’s urban plan, some makeups were applied to urban plan of Barcelona.

5 major makeups after Cerdà:

• 1907 - International competition - Léon Jaussely = Proposals for grand intersections, corniches and generous avenues.

• 1937 - Le Corbusier - the Place Macià = Larger blocks.

• 1980s - Orcol Bohigas et al = Necklace of tiny interventions strung throughout the inner city and outer suburbs.

• 1992 - Olympic Games hosted by Barcelona = Stimulus for rethinking the city’s overall infrastructure. An inner and an outer ring road constructed with one part running invisibly beneath the city’s long-lasting zones of activity.

• Recent - Metro Line 9 (Longest underground tunnel) = Wraps around the city, while the high-speed train line connects Madrid to Paris via subterranean Barcelona.

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“Plan Cerda.” Barcelona, historyofbarcelona.weebly.com/plan-cerda.html.

5

Interways of blocks

FIGURE 3.1.1.7 (left, top) Transformation in blocks

FIGURE 3.1.1.8 (right)

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Although Cerda’s plan was disrupted and not implemented well, grid structure of the city and chamfered corners in urban blocks are evidence of Cerda’s big impact on Barcelona city plan. Chamfered corners of Cerda’s urban plan were more conducive to imprompt for encounters and gatherings in a better way than right-angled corners6. Furthermore, the chamfered corners to enhance the economical perspective of the city by enabling high-value corner shops all around streets. Those shops are happily exposed more attention by inhabitants of the city, yet this geometrical difference enables some different values for properties in the gridal city.

By the time, apart from his plan, blocks gradually developed for more profit. Central spaces grew in height with rooftop extensions, which transformed the city centre into a light-industrial space (figure 3.1.1.10). Blocks started to include small businesses, residences and rooftop extensions.

These unplanned additions on Cerda’s plan led from the need of complex and dynamic social environment of modern cities. In this context, Mark Burry asserts that we still plan the city as a 2D construct which is outdated for today’s cities. In this regard, we must contribute positively to urban design through advanced computation as being a development of Cerda’s data-driven design strategies for the urban living back in time7.

6

Burry, Mark 2013.

7

Burry, Mark 2013.

Transformation of blocks

FIGURE 3.1.1.9 (left)

Transformation of blocks from public to private

FIGURE 3.1.1.10 (right) 110

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BARCELONA SUPERBLOCKS

On the base of Cerda’s Eixample, a new approach for the Barcelona urban area was designed to limit car access in the city.

“Superblcoks” is a solid solution as being a ambitious place which desire to decrease overall traffic by 21% in city of Barcelona. As the core idea of the Eixample of Cerda, city needs breathe for the public health reasons. Superblocks will be particularly selected in the grid to enhance the overall level of environmental conditions of the city instead of focusing in one region (figure 12.). Such as in the figure 11. a superblock will be composed by nine existing blocks of the grid.

“..car, scooter, lorry and bus traffic will then be restricted to just the roads in the superblock perimeters, and they will only be allowed in the streets in between if they are residents or providing local businesses, and at a greatly reduced speed of 10km/h (typically the speed limit across the city is 50km/h, and 30km/h in specific areas).”8

Superblock idea could be use in the Urban Gamification research as being a reward or penalty system for the Chain Reaction game.

8

Bausells 2016

Superblocks consist of nine blocks

FIGURE 3.1.1.11 (left, top)

Superblocks placement in example

FIGURE 3.1.1.12 (right) 112

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3.1.2 PARIS

wrote:

In 1845, the French social reformer Victor Considerant

“Paris is an immense workshop of putrefaction, where misery, pestilence and sickness work in concert, where sunlight and air rarely penetrate. Paris is a terrible place where plants shrivel and perish, and where, of seven small infants, four die during the course of the year.”

The population density was extremely high. In 1840, a doctor described a single room five meters square on the fourth floor of one building in the Île de la Cité, which was occupied by twenty-three people, both adults and children. In these conditions, disease spread very quickly throughout the city. Cholera epidemics consumed the city in 1832 and 1848. In the epidemic of 1848, five percent of the inhabitants of Arcis and Saint-Avoye died.

Traffic circulation was another major problem. The widest streets in the neighbourhoods of Arcis and Saint-Avoye, in the present Third Arrondissement, were only five meters wide; the narrowest were only one or two meters wide. Wagons, carriages and carts could barely move through the streets.

The center of the city was also full of discontent citizens and revolution; between 1830 and 1848, seven armed uprisings and revolts had broken out in the centre of Paris, along the Faubourg Saint-Antoine, around the Hôtel de Ville, and around Montagne Sainte-Geneviève on the left bank. The residents of these these neighbourhoods used paving stones to block the narrow streets with barricades, which were dislodged by the army.

Prior to Haussmann, Paris had only four public parks: the Jardin des Tuileries, the Jardin du Luxembourg, and the Palais Royal, all in the center of the city, and the Parc Monceau, the former property of the family of King Louis Philippe, in addition to the Jardin des Plantes, the city’s botanical garden and oldest park.

A view of Paris.

FIGURE 3.1.2.1 (left) Picture of the narrow streets in Paris; Rue des Marmousets, Charles Marville, 1853-70 Source : State Library of Victoria

FIGURE 3.1.2.2 (right) 115

LOUIS-NAPOLÉON BONAPARTE’S VISION OF PARIS

In the February Revolution of 1848, King Louis-Philippe was overthrown. On the 10th of December 1848, with an overwhelming 74.2 percent of the votes cast, Louis-Napoléon Bonaparte, the nephew of Napoléon Bonaparte, won the first direct presidential elections ever held in France. He was elected largely because of his famous name, but also because of his promise to end poverty and improve the lives of ordinary people.

Napoléon had lived most of his life in exile, and he was especially impressed by London, with its wide streets, squares and large public parks. He wanted to incorporate these aspects in the new Paris renovation.

Though he had been born in Paris, he had lived very little in the city; from the age of seven, he had lived in exile in Switzerland, England, and the United States, and for six years in prison in France for attempting to overthrow King Louis-Philippe. In 1852, he gave a public speech declaring: “Paris is the heart of France. Let us apply our efforts to embellishing this great city. Let us open new streets, make the working class quarters, which lack air and light, more healthy, and let the beneficial sunlight reach everywhere within our walls”. As soon as he became President, he supported the first subsidised housing project for workers in Paris, the Cité-Napoléon, on the rue Rochechouart. He proposed completing the project begun by his uncle Napoléon Bonaparte, the rue de Rivoli from the Louvre to the Hôtel de Ville. He began a project which would transform the Bois de Boulogne (Boulogne Forest) into a large new public park, modelled after Hyde Park in London but much larger, on the West side of the city of France. Let us apply our efforts to embellishing this great city. Let us open new streets, make the working class quarters, which lack air and light, more healthy, and let the beneficial sunlight reach everywhere within our walls”. As soon as he became President, he supported the first subsidised housing project

Emperor Napolean III hands the letter to Haussmann on the right, giving him the rights to renovate Paris.

Painting by Adolphe Yvon, 1893.

Source: Brown University Library via https://repository.library.brown.edu/

FIGURE 3.1.2.3 116

for workers in Paris, the Cité-Napoléon, on the rue Rochechouart.

He proposed completing the project begun by his uncle Napoléon Bonaparte, the rue de Rivoli from the Louvre to the Hôtel de Ville.

He began a project which would transform the Bois de Boulogne (Boulogne Forest) into a large new public park, modelled after Hyde Park in London but much larger, on the West side of the city.

Haussmann created twenty-four new squares; seventeen in the older part of the city, eleven in the new arrondissements, adding 150,000 square meters of green space. Haussmann’s goal was to have one park in each of the eighty neighborhoods of Paris, so that no one was more than ten minutes’ walk from such a park.

HAUSSMANN’S RENOVATION OF PARIS 1853 - 1870

A vast renovation of Paris was carried out by Georges-Eugene Haussmann, the perfect of the Seine under Napoleon III from 1853 to 1870. Napoleon III ordered him to aérer, unifier, et embellir Paris; to bring air and light to the centre of the city, to unify the different neighbourhoods with boulevards, and to make the city beautiful. Haussmann’s renovation can be seen in three phases;

1. The Croisée de Paris (1853–59)

2. A network of new boulevards (1859–1867)

3. The third phase and mounting criticism (1869–70)

While his work is recognised in a positive light in Germany and elsewhere, many of the French have criticized him heavily for his work. 20th-Century historian René Héron de Villefosse - thinking in particular of Haussmann’s transformation of the Île de la Cité, has mentioned that “the old ship of Paris was torpedoed by Baron Haussmann and sunk during his reign. It was perhaps the greatest crime of the megalomaniac prefect and also his biggest mistake…

His work caused more damage than a hundred bombings.”

His plan includes the following;

• A greatly expanded sewer system.

• The construction of wide boulevards.

• The construction of wide boulevards.

• Gas lighting for the streets.

• The formulation of public building regulations.

• The construction of monuments.

• An updated and uniform facade for the city’s buildings.

• A reorganized and symmetrical road system.

• The construction of new parks.

• The division of Paris into arrondissements (Districts) and the expansion of the city’s limits

Paris’ sewer system expanded fourfold from 1852 to 1869.

117

The sewers were made large, so that men could work comfortably, and carry clean waters and waste from the city.

THE CROISÉE DE PARIS (1853–59)

In the first phase of his renovation Haussmann constructed 9,467 metres (6 miles) of new boulevards, at a net cost of 278 million francs. After Berger was dismissed from the position, Haussmann became Prefect of the Seine on 22 June 1853, and on 29 June the Emperor showed him the map of Paris and instructed Haussmann to aérer, unifier, et embellir Paris.

The grande croisée de Paris, a great cross in the centre of Paris, would permit easier communication from east to west along the rue de Rivoli and rue Saint-Antoine, and north-south communication along two new Boulevards, Strasbourg and Sébastopol. The grand cross had been proposed by the Convention during the Revolution, and begun by Napoléon I. Napoléon III wished to complete it.

The French parliament, controlled by Napoléon III, provided fifty million francs, but this was not nearly enough. Napoléon III appealed to the Péreire brothers, Émile and Isaac, two bankers who had created a new investment bank, Crédit Mobilier.

The Péreire brothers organised a new company which raised 24 million francs to finance the construction of the street, in exchange for the rights to develop real estate along the route. This became a model for the building of all of Haussmann’s future boulevards.

In 1855, work began on the north-south axis, beginning with Boulevard de Strasbourg and Boulevard Sébastopol, which cut through the center of some of the most crowded neighborhoods in Paris, where the cholera epidemic had been the worst, between the rue Saint-Martin and rue Saint-Denis. “It was the gutting of old Paris,” Haussmann wrote with satisfaction in his Memoires: of the neighborhood of riots, and of barricades, from one end to the other.”[21] The north-south axis was completed in 1859. The two axes crossed at the Place du Châtelet, making it the center of Haussmann’s Paris. Haussmann widened the square, moved the Fontaine du Palmier, built by Napoléon I, to the center and built two new theaters, facing each other across the square; the Cirque Impérial (now the Théâtre du Châtelet) and the Théâtre Lyrique (now Théâtre de la Ville).

The official parliamentary report of 1859 found that it had “brought air, light and healthiness and procured easier circulation in a labyrinth that was constantly blocked and impenetrable, where streets were winding, narrow, and dark.” It had employed thousands of workers, and most Parisians were pleased by the results.

Postcard of Paris sewer workers under Boulevard Sebastopol, 19th Century.

Scanned by Claude Shoshany

FIGURE 3.1.2.4 (right, top)

Urban achievements during second empire.

Source: Diagram by D. Destugues.

FIGURE 3.1.2.5 (right, bottom) 118

119

THE SECOND PHASE A NETWORK OF NEW BOULEVARDS (1859–1867)

His second phase, approved by the Emperor and parliament in 1858 and begun in 1859. He intended to build a network of wide boulevards to connect the interior of Paris with the ring of grand boulevards built by Louis XVIII during the restoration, and to the new railroad stations which Napoleon III considered the real gates of the city. He planned to construct 26,294 metres (16 miles) of new avenues and streets, at a cost of 180 million francs.

On both the right bank and the left bank, Haussmann directed construction of numerous new boulevards to connect monuments, new railway stations, parks, residential areas and new neighbourhoods.

Île de la Cité became an enormous construction site, where most of the old streets and neighbourhoods were completely destroyed to make way for two new government buildings and two new streets, with two bridges being completely rebuilt along with the embankments nearby. Many of the existing monuments were restored and made better.

THE THIRD PHASE THE DOWNFALL (1869–1870)

Numerous renovations of gardens, extensions of boulevards, and creating more avenues continued in this third phase as well, on both the left bank and the right bank. The plan included the construction of twenty eight kilometers of new boulevards at an estimated cost of 280 m francs. Haussmann was not able to complete the third phase as he came under attack from the opponents of Napoleon III. As soon as Napoleon was removed from power, under attack from the opponents of the emperor, and he was soon dismissed by Napoleon III in January 1870. Eight months later the empire would be overthrown during the Franco-Prussian war.

However, even after his downfall, the successor, JeanCharles Alphand, respected the basic concepts of his plan and finished his renovation projects; • 1875 – completion of the Paris Opéra • 1877 – completion of the boulevard Saint-Germain • 1877 – completion of the avenue de l’Opéra • 1879 – completion of the boulevard Henri IV • 1889 – completion of the avenue de la République • 1907 – completion of the boulevard Raspail • 1927 – completion of the boulevard Haussmann

In Haussmann’s Paris, the streets became much wider, growing from an average of twelve meters wide to twenty-four meters, and in the new arrondissements, often to 18 meters wide.

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ARCHITECTURE OF HAUSSMANN

Street blocks were designed as homogeneous architectural wholes. He treated buildings not as independent structures, but as pieces of a unified urban landscape.

In 18th-century Paris, buildings were usually narrow (often only six meters wide); deep (sometimes forty meters) and tall— as many as five or six stories. The ground floor usually contained a shop, and the shopkeeper lived in the rooms above the shop. The upper floors were occupied by families; the top floor, under the roof, was originally a storage place, but under the pressure of the growing population, was usually turned into a low-cost residence.

In the early 19th century, before Haussmann, the height of buildings was strictly limited to 22.41 meters, or four floors above the ground floor.

The interiors of the buildings were left to the owners of the buildings, but the façades were strictly regulated, to ensure that they were the same height, color, material, and general design, and were harmonious when all seen together.

The reconstruction of the rue de Rivoli was the model for the rest of the Paris boulevards. The new apartment buildings followed the same general plan:

• Ground floor and basement with thick, load-bearing walls, fronts usually parallel to the street. This was often occupied by shops or offices.

• Mezzanine or entresol intermediate level, with low ceilings; often also used by shops or offices.

• Second, piano nobile floor with a balcony. This floor, in the days before elevators were common, was the most desirable floor, and had the largest and best apartments.

• Third and fourth floors in the same style but with less elaborate stonework around the windows, sometimes lacking balconies.

Typical Haussmann buildings.

Picture taken by Thierry Bezecourt, November 2005.

FIGURE 3.1.2.6 (right, top)

• Fifth floor with a single, continuous, undecorated balcony. Originally this floor was to be occupied by lower-income tenants, but with time and with higherrents it came to be occupied almost exclusively by the concierges and servants of the people in the apartments below.

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3.1.3 VIENNA

Vienna is the capital city of Austria. It has topped the world in being the city with the highest quality of life, according to the survey performed by the human resource consulting firm Mercer, for the ninth consecutive year1. The survey evaluates the local living conditions in more than 450 cities worldwide by analyzing 39 different factors grouped in 10 categories, viz political and social environment, economic environment, socio-cultural environment, medical and health considerations, schools and education, public services and transportation, recreation, consumer goods, housing and natural environment.

Of the many reasons leading to the rich life quality in the Vienna, one crucial reason is the availability of affordable yet quality housing2. Widely acclaimed as ‘The Vienna Model’, the social housing practice in Vienna with efficient funding by the Austrian government and diligent architectural practice, is looked up to by other major cities like Vancouver3. Besides the affordability, the housing model in Vienna lays importance on engendering a healthy social environment for its inhabitants through participatory design processes. The book ‘The Vienna Model: Housing for the Twenty-First-Century City’ by Wolfgang Förster and William Menking, describes in detail the housing practice followed in the city and provides 58 examples of social housing projects executed. Examples with most relevance in gaining an insight on urban housing strategies prioritizing social dynamics are discussed and reviewed.

Mercer, “Quality of Living City Ranking” 1

Kate Jackson, “10 Reasons Vienna Was Named City With Highest Quality of Life” 2

Cheung, C. (2017). “Unaffordable Cities, Look to Quality Public Housing in Vienna” 3

Aerial view of Vienna.

FIGURE 3.1.3.1 (left) Mercer conducts an annual survey to determine the liveability of cities.

Source: https://mobilityexchange.mercer.com/insights/quality-of-living-rankings

TOP 10 CITIES

BOTTOM 10 CITIES

FIGURE 3.1.3.2 (right)

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WIEN IST ANDERS

‘Vienna is different’ has been the slogan the city uses to advertise itself1 and with regard to the city’s housing policy, it holds true. The post-World War-I period in the Austrian capital has seen one among the world’s largest public housing programs in history being implemented. Today, almost 62% of the city’s households live in subsidized housing. The city government itself owns 220,000 rental housing units accounting to about 25% of the total housing stock. Moreover, another 200,000 rental units are owned by limited-profit housing associations, thus bringing close to half of the city’s housing stock under affordable housing schemes2. It is noteworthy that despite the units being subsidized, the quality of lviing conditions are uncompromised.

The city’s housing programme serves as a viable learning model, since it faces urban challenges much similar to every other urban region in the world and yet maintains the quality and affordability of its housing stock. In between 2005 and 2015, the city saw a 10% increase in population, thus becoming the EU’s second fastest growing capital city. Also the data from 2016 denotes an immigrant retention of 39,185 in the city3. Also, the city has been hard hit by the global financial crisis. Thus, the housing programme is continuously re-adapted to be resilient towards the wake of population increase and economic deficit.

Vienna is Different | Her Campus. (n.d.). Retrieved April 15, 2018, from https://www.hercampus.com/school/ durham/vienna-different 1

Ludwig, Michael, ”Preface”. In W. Forster & W. Menking (Eds.), The Vienna Model: Housing for the Twenty-First-Century City (p. 5). Berlin: Jovis.

2

Vienna in Figures - 2016. (n.d.).

Retrieved from https://www.wien.gv.at/ statistik/pdf/viennainfigures-2016.pdf 3

The Karl Marx Hof is one of the most famous social housing structures built during Red Vienna period and is operational until now.

Photograph by Bwag, distributed under a CC-BY 2.0 license.

FIGURE 3.1.3.3 (left)

Demographic development surveys in the EU region denote that the population of Vienna expands by over 4.65% between 2010 and 2025.

Source: ‘The Vienna Model’, UN Habitat (2007)

FIGURE 3.1.3.4 (right, top)

With an increasing inflow of migrants and refugees into the city, the number of residents increases proportionally, calling for more housing. Source: ‘The Vienna Model’, Data Source: City of Vienna (2015)

FIGURE 3.1.3.5 (right, bottom) 124

125

SOCIAL HOUSING IN VIENNA

The housing policy in Vienna began development in 1920, with the fall of the Hapsburg monarchy and the start of the Social Democratic Party rule, the first democratically elected government in the country. The party drew on the Marxist philosophies of deciding housing should not be a private property. However, unlike becoming entirely communist, they rather wished to implement socialist perspectives within the thought framework. This caught the interest of architects and design professionals alike leading up to the Vienna International Urban Planning Conference in 1926.

This period of Vienna is populary referred to as ‘Red Vienna’1.

Following the Second World War, the City of Vienna continued with it social housing programme now leading up to more than half its population accommodated within the programme.

What sets up the city’s programme to be a housing model for other cities are the instuments developed to implement its policy of creating a functional and social mix in all residential areas. This encompasses the ‘Four Pillar Model’ that is part of the ‘Developer Competitions’ that the housing council runs to find potential architects to design and develop the delineated housing assignments2.

From Socialism to Fascism - History of Vienna. (n.d.). Retrieved from https:// www.wien.gv.at/english/history/overview/socialism.html 1

Forster, W., & Menking, W. (Eds.).

(2016). The Vienna Model: Housing for the Twenty-First Century City. Berlin: Jovis: 7.

2

Every social housing project take up in the city needs to go through the ‘Four Pillar Model’ to be validated. Source: ‘The Vienna Model’

FIGURE 3.1.3.6 (top) 126

THE FOUR PILLAR MODEL

All architectural firms contesting in the developer competitions to execute housing propositions in the city, need to face an interdisciplinary jury that judges their proposals on four sets of criteria: architecture and planning, costs, ecology and social sustainability. In recent competitions, most importance has been laid on social sustainability. The overall aim is to engender a functional and social mix with emphasis on creating new communities. The jury panel consists of experts from multiple disciplines like architects, landscape planners, ecologists, and sociogists - most of them nominated by independent instituitions like Architects’ associations and universities. They hold the panel for two years.

Forster, W., & Menking, W. (Eds.).

(2016). The Vienna Model: Housing for the Twenty-First Century City. Berlin: Jovis: 10-11.

1

The model particularly gains interest for the reason for its emphasis on the social dimension. With almost every urban region facing an influx of immigration and social and cultural mix, it is almost impossible to neglect social planning from architectural design. In terms of housing, the social mix matters even more when considering the importance of creating resilient communities. Hence, a closer look at how the projects are developed would prove useful in understanding architectural programming to ensure social sustainability1.

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1

CONTINUITY AND INNOVATION -

2

3

4

5

6

7

8

SOCIAL MIXING -

Avoiding social and economic ghettos

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Ensuring social mix in the face of high immigration and financial crisis

DEVELOPING NEW URBAN AREAS -

Connecting to social infrastructure

-

Converting a number of apartments into an urban area

-

Reflecting the former use of an area

-

Creating an identity

DIVERSITY AND INTEGRATION -

Diversity in culture, lifestyle, family size, age groups and special needs

-

Integrating diversity in the floor plan

CITIZEN PARTICIPATION -

Reaching out to citizens, including those ‘hard to reach’

-

Including citizen feedback in the design process

ENVIRONMENTAL & CLIMATE PROTECTION -

Low energy / passive construction

-

Considering mobility patterns

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Healthier consumer patterns

USE AND DESIGN OF PUBLIC SPACES -

Connecting public spaces to housing

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Bigger public areas compensating for smaller floor plans

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Shared community spaces replacing vast private spaces

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Communication and integration of different age and cultural groups

DEVELOPING EXISTING HOUSING STOCK -

9

10

128

Integrating historical heritage and future technology

Connecting old and new housing architecturally and socially

BUILDING ON THE OUTSKIRTS -

Sub-urbanisation: Developing sub-urbs around the city core

-

Figuring alternatives to the single family housing

THE ROLE OF ART -

Art in creating identity and community feeling

-

Involving residents in creating art

-

Art as a medium of history

HOUSING ON AUHOF-CENTRE

TOWNHOUSE

WIMBERGERGASSE

SO.VIE.SO. HOUSING

SOCIAL HOUSING EXAMPLES

Recent times have made the housing model of the city exposed to multiple challenges like the rapid growth of the city, economic crisis, ageing population on one hand and fresh young population through migration, the requirements of environmental and climatic protection and a growing influx of refugees. However the governing authorities have been diligent enough in making extra actions to keep the model resilient enough. Vienna has managed to house and accommodate about twenty thousand refugees within a matter of months proving that the housing programme is indeed a model for other cities to follow1.

Forster, W., & Menking, W. (Eds.).

(2016). The Vienna Model: Housing for the Twenty-First Century City. Berlin: Jovis: 10-11.

1

The social housing model in Vienna has ten main characteristics as laid out in the book ‘The Vienna Model.’

Source: ‘The Vienna Model’

The book ‘The Vienna Model: Housing for the Twenty-First-Century City’ puts forward ten main characteristics of the model and the case examples that are portrayed are organized around it. The characteristics are seen to be more in relation to the criteria mentioned discussed in the four pillar model. Three case examples that have greater relevance in understanding urban housing potentials in London are explored. They include the So.Vie. So. housing at Sonnwendviertel Solidarisch demostrating participatory design process, the housing on top of Auhof Shopping Centre and Housing in Wibergergasse both of which demonstrate infill housing in two different contexts.

FIGURE 3.1.3.7 (left)

Three social housing projects demonstrating user participation and infill design strategies are chosen for literature review.

Source: Google Earth, 2018

FIGURE 3.1.3.8 (right)

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SO.VIE.SO HOUSING SONNWENDVIERTEL SOLIDARISCH, 2013

The So.Vie.So housing is one of the housing projects in the city that places social participation and sustainability in the centre of the design process. The chief target group of the project is the economically weaker section of the society including young people in education, young families, single parents, large families and seniors. Thus the ideal was to create inexpensive, invidualized private spaces combined with various different communal zones.

The building has a total of 111 apartments.

The communal area comes in various gradations and sizes. There are smaller communal spaces like childcare zones, housekeeping areas, media use, educational zones, gaming areas and meeting zones for senior members that are located on every floor. Also, there are bigger communal areas that are distributed all over the building. These include toddler playrooms, laundry rooms, an youth club, bicycle workshop, kitchen and a communal living room1. The building also has an ample amount of green open spaces including a large central courtyard. Passive design techniques have also been employed in the building to pass the ecological contrainsts and offer the residents a low cost of living. Overall, the building received the award Weiner Wohnbaupreis (Vienna Housing Award) in 20152.

Sonnwendviertel solidarisch. (n.d.).

Retrieved from http://www.sovieso.at/ projekt/downloads/SOVIESO-4-Saeulen-Text.pdf 1

Forster, W., & Menking, W. (Eds.).

(2016). The Vienna Model: Housing for the Twenty-First Century City. Berlin: Jovis: 146-47.

2

Each flat has a choice to incorporate a personal balcony toward the street.

Source: Alexander Schindler Š S & S Architekten

FIGURE 3.1.3.9 (left)

The building has a huge garden space in its centre and also terrace gardens for the inhabitants to share.

Source: So.vie.so http://www.sovieso.at/ projekt/downloads.php

FIGURE 3.1.3.10 (right) 130

131

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The structural grid allows for multiple apartment layouts to be incorporated in it. Also the plan is flexible to change.

Source: So.vie.so http://www.sovieso.at/ projekt/wohnung.php

FIGURE 3.1.3.11 (left)

There are four types of apartments in the catalogue, each type featuring different kinds of layouts to opt from. Source: So.vie.so http://www.sovieso.at/projekt/wohnung.php

FIGURE 3.1.3.12 (bottom)

The highlight in the design is the way it includes the inhabitants of the building into the design process. The model allows the user groups to choose the apartment layout from a catalogue that has almost 60 options. There are four different types of flats (B, C, D and E) differing by their floor area and each type has multiple different configurations to pick from. The grid system in the design is flexible enough to accommodate the choices made by the users.

Each apartment also has the option to include a personal balcony.

The layout of the apartment is also flexible to be customized by the household it accommodates.

The goal of participation is to meet the wishes and needs of the people in the given context. It also engenders co-determination where the user groups decide together where they ‘build on one another.’ In this process, young people and children are equally involved. Co-determination also helps better allocate the communal areas. Tenants who make the early registration get the priority in the decision. Thus, the participatory model ensures higher success of the building functionality and communty.

133

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HOUSING ON AUHOF CENTRE 2015

In an innovative attempt to approach the shortage of land space and escalating land values prevailing in a city, infill developments have started becoming a trend in construction. This project demonstrates a unique appoach to an infill development where the housing complex is developed on top of an existing shopping complex.

The project overall comprises of 71 subsidized apartments built over the roof of the Auhof Shopping centre in Vienna.

The apartments also include 25 ‘SMART’ apartments, the special kind of subsidized reconfigurable apartments designed in Vienna. The flats are organized around a large central green courtyard space, that offers seating areas for gatherings and play areas for children.

The projects subsidized rents are due to the symbiosis existing between the shopping centre below and the housing units on top of it. The owners of the site transferred the construction rights to the housing developer making the property costs practically zero. Moreover, the verticall infill reduces the floor area wasted by the single-function shopping centre1.

Forster, W., & Menking, W. (Eds.).

(2016). The Vienna Model: Housing for the Twenty-First Century City. Berlin: Jovis: 186-87 1

The roof of the shopping centre is converted into a public garden and play space around which the apartments are organized. Source: Querkraft http:// www.querkraft.at/?story=1853

FIGURE 3.1.3.13 (left, top)

The three floored housing complex is situated upon the existing shopping centre, Auhof centre. Source: Google Maps, 2018

FIGURE 3.1.3.14 (left, bottom)

The project is an example of an urban infill development, where the building is constructed on another existing structure. Source: Querkraft http://www.querkraft.at/?story=1853

FIGURE 3.1.3.15 (right)

135

TOWNHOUSE WIMBERGERGASSE, 2001

The townhouse at Wimbergergasse stands as an example of how alternative programme strategies like infill development could be used to more comprehensively use pockets of land space.

Delugan Meissl architects have situated this modern building in between two period buildings1.

The design cleverly mixes office and residential spaces in has two dominant features - the space-contained facade feature and the accentuation of the topography. The facade element facing the busy street is made of printed glass, covering the balconies.

This part of the building containts the residential areas. The central part of the building contains the office spaces, stacked in a different fashion than the residential blocks. Displaying an open permeable character, the office spaces are arranged on two to three levels, stacked on top of one another. The roof spaces are green and are accessible. The apartment spaces also have a flexible layout with sliding walls that can be customized to the needs of the users2.

Overall the building displays a diligent formal and fuctional mix of spaces offered at an affordable price for its inhabitants. The building design also received the national architecture award in 2002.

Forster, W., & Menking, W. (Eds.).

(2016). The Vienna Model: Housing for the Twenty-First Century City. Berlin: Jovis: 196-97.

1

State of Flux Delugan_Meissl Architects, Vienna (n.d.). Retrieved from http://www.aoeg.net/state-of-flux/ textsE_flux.pdf 2

The roof of the office spaces are accessible garden spaces for the community to share. Source: http://www.dmaa.at/ projekte/detail-page/townhouse-wimbergergasse.html

FIGURE 3.1.3.16 (left)

The slopping roof of the office space is well integrated with the residential block to provide access. Source: Delugan Meissl Associated Architects http://www. dmaa.at/projekte/detail-page/townhouse-wimbergergasse.html

FIGURE 3.1.3.17 (right, top)

The vertical facade block contains the residential spaces and the horizontal blocks behind have the office spaces.

Source: Delugan Meissl Associated Architects http://www.dmaa.at/projekte/ detail-page/townhouse-wimbergergasse.html

FIGURE 3.1.3.18 (right, bottom) 136

137

3.2 BLOCK FRAMEWORKS

Inspired by the ‘50 Urban Blocks’ Flash Cards by a+t Architecture Publishers, we took different presets of urban blocks and tested our units to obtain which block is better for what kind of units and communities by calculating the density of blocks and how many expansion occurs as the measure of success.

Density is calculated as floor area per person, whilst it is defined in the middle blue chart to demonstrate the final situation after series of explosions(expansions) take place.

Expansion chart at the bottom analyses and provides a measure of success based on the number of total expansions that happened in a block. This is done by averaging the number of expansion to ahieve a mean expansion value per unit.

After the game played out, First test showed that row house block is good for private and repetative units, since it does not offer many opportunites for sharing.

Second test was with L shaped row houses. The game worked better with this scenerio. In the staggered corners we were able to observe how communities with medium density emerged out of gameplay.

U block was the third test in which two staggered corners led to more shared spaces and expansions. This block was better for satisfying people’s interests. U block was also tested with rectangular units which offered less dead space when it is compared with the hexagonal units. However, the number of expansion which is driven by sharing is drasitcally low in comparison to the previous example. We are looking for block typologies that could encourage interaction and expansion, so this typology would be considered a bad choice.

The last test was a challange of creating a tower with the same game. Since creating a dense community that is clustered vertically leads to expansions ( or explosions in the gameplay) that end up blocking daylight to the units below, the process creates smaller vertical circulation cores as the game is played (with the game rule that forbids blocking the sunlight). Therefore, creating a community with horizontally spread units work better when it comes to creating a block which is better suited for fostering interactive communities.

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Row Houses

FIGURE 3.3.0.1 139

Row Houses - L-shaped

FIGURE 3.3.0.2 140

Clustered U Block

FIGURE 3.3.0.3 141

Clustered U Block

FIGURE 3.3.0.4 142

Tower Block

FIGURE 3.3.0.5 143

04

DATA-DRIVEN RESEARCH 4.1

4.2

URBAN DATA RESEARCH PRECEDENTS 4.1.1 A SMARTER SMART CITY 4.1.2 FRIENDLY CITIES 4.1.3 GANGAM POOP MACHINE LEARNING PRECEDENTS 4.2.1 WHAT IS MACHINE LEARNING 4.2.2 THE PRECEDENTS 4.2.3 HOW WE ARE USING MACHINE LEARNING 4.2.4 EMBEDDINGS

146 146 151 157 158 158 160 164 166

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4.1 URBAN DATA RESEARCH PRECEDENTS 4.1.1 A SMARTER SMART CITY

SIDEWALK LABS

Alphabet subsidiary Sidewalk Labs can reshape how we live in cities through intense data gathering.

Sidewalk Labs was founded in 2015 to develop innovations to solve urban problems in today’s cities. They aim to work with a community in the process of tailoring the technology to urban needs. In this context, Quayside, which is mostly owned by a local development agency Waterfront Toronto, was their first big project as expected to be hosted 5,000 inhabitants1.

As a vision, Quayside can be a place where self-driving shuttle buses and cars replace conventional private cars; traffic light ought to be adaptive to track the flow of pedestrians, bicyclists, and vehicles; besides robots can do mail and garbage transportations through underground tunnels; and modular buildings can be unfolded to accommodate growing families and such commercial companies as well2.

Sidewalk believes that self-driving cars surely obey traffic rules more consistently than humans and navigate more precisely.

This helps them provide more room for sidewalks and parks and shared self-driving cars will save about $6,000 for a family in a year3.

1

Woyke, “A smarter smart city”, 2018

2

Woyke, “A smarter smart city”, 2018

3

Woyke, “A smarter smart city”, 2018

A Smarter Smart City - Smarted Vehicles Martina Paukova, 2018

FIGURE 4.1.1.1 (right, top)

A Smarter Smart City - Smarted Vehicles Martina Paukova, 2018

FIGURE 4.1.1.2 (right, bottom) 146

147

In this regard, monitoring public activity and collecting data accurately will be key. To illustrate, autonomous buses or cars on city streets have to know when to give cyclists and pedestrians priority4.

Sidewalk Labs claims the fact that data collection through using sensor information would be also supportive in longterm planning scheme. The data would feed the virtual model of Quayside, in which urban designers and regulators could use to test ideas and infrastructure changes rapidly through simulation processes, at low cost, and without bothering residents5.

For the city which is visioned by Sidewalk Labs, Aggarwala asserts that city as a platform which is designed in the ability for people to change it as quickly people can customize their iPhones, it is authentic to not just reflect a central plan, besides it also reflects the people who live and work6.

A Smarter Smart City project by Sidewalk is a good case study in terms of understanding of data collection strategies and to see how the data can be used for the research which is a game based approach in urban planning through utilizing individual agencies.

4

Woyke, “A smarter smart city”, 2018

5

Woyke, “A smarter smart city”, 2018

6

Woyke, “A smarter smart city”, 2018

A Smarter Smart City - Smarted Vehicles Martina Paukova, 2018

FIGURE 4.1.1.3 (right, top)

A Smarter Smart City - A visualization Martina Paukova, 2018

FIGURE 4.1.1.4 (right, bottom) 148

149

150

4.1.2 FRIENDLY CITIES

A RESEARCH ABOUT HOW PEOPLE SHARE URBAN

SPACES

Social interactions are becoming more important to understand today’s cities in which global urbanization accelerates.

Movement pattern of people in cities and how people are connected through urban spaces provide us with the degree of social diversity and economic vitality. In this context, Friendly Cities is a project conducted at MIT Senseable City Lab, that shows how mobility patterns which are extracted from mobile phone data and social network structure enable us to understand the social roles of urban spaces through social interactions1. To observe the data structure, Call Detail Record (CDR) dataset collected is in Singapore in two matrixes, bonding and bridging capability, to identify places in the city that bring together friends versus the chance of encounters among strangers. The outcomes declare two different kinds of social landscapes in Singapore as well as their evolutions over time2.

Bonding capability measures the overall probability that a urban space is shared by friend pairs in the city during a given time slot (e.g., 11:00 – 14:00). An urban space which has a high value claims that this place has a big potential of bringing friends together3.

Bridging capability refers the overall probability that two randomly selected people tend to encounter at a given place. A place with a high value indicates that it tends to have more chance for encounters among strangers4. Therefore, this data can also give us a hint about the density of a space.

“Friendly Cities.” MIT Senseable City Lab, senseable.mit.edu/friendly-cities/.

1

The overall aim of the research is to understand how urban spaces and people are connected each other and which kind of patterns arise. The collected data can be a base of new policies which benefit the well-being of society5. Besides, the data collection through mobile phones can be also beneficial for the research of Urban Gamification in terms of observing social negotiations and interactions aspects which are aimed to play a key role in urban characteristic.

“Friendly Cities.” MIT Senseable City Lab, senseable.mit.edu/friendly-cities/.

2

“Friendly Cities.” MIT Senseable City Lab, senseable.mit.edu/friendly-cities/.

3

“Friendly Cities.” MIT Senseable City Lab, senseable.mit.edu/friendly-cities/.

4

“Friendly Cities.” MIT Senseable City Lab, senseable.mit.edu/friendly-cities/.

5

Bonding capabilities during the Daytime on Weekdays

FIGURE 4.1.2.1 (left, top)

Bridging capabilities during the Daytime on Weekdays

FIGURE 4.1.2.2 (right, bottom)

151

Bonding capabilities during the Nighttime on Weekdays

FIGURE 4.1.2.3

Bonding capabilities during the Nighttime on Weekdays

FIGURE 4.1.2.4 152

Bonding capabilities during the Daytime on Weekends

FIGURE 4.1.2.5

Bridging capabilities during the Daytime on Weekends

FIGURE 4.1.2.6

153

Bonding capabilities during the Nighttime on Weekends

FIGURE 4.1.2.7

Bonding capabilities during the Nighttime on Weekends

FIGURE 4.1.2.8 154

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FIGURE 4.1.2.9

The correlation between the bonding and bridging capabilities

156

4.1.3 GANGNAM POOP

UNDERWORLDS IN SEOUL

Underworlds in Seoul research investigates the information of public human health through the data of viruses and bacteria gathered from the sewage system of Seoul.

Carlo Ratti, Director of the Senseable City Lab says that “ We imagine a future in which sewage is mined for information that can inform policy makers, health practitioners, designers, and researchers alike.�1

A cross-disciplinary data platform which monitors urban health patterns in order to shape more inclusive and relevant public health strategies, through pushing the boundaries of urban epidemiology2.

MIT Senseable City Lab and the MIT Alm Lab collaboratively developed a prototype of smart sewage platform consisting of physical infrastructure such as biochemical measurement technologies. Besides, the downstream computational tools and analytics are to interpret and act on our findings3.

The Underworlds project is a good way of utilizing wastewater system to do real-time data observing to understand human health and behaviour. The way of collecting data and some probable outputs such as stress and happiness level of people could be helpful for the Urban Gamification research.

Underworlds Seoul Exhibition, senseable.mit.edu/underworlds-seoul.

1

Underworlds Seoul Exhibition, senseable.mit.edu/underworlds-seoul.

2

Underworlds Seoul Exhibition, senseable.mit.edu/underworlds-seoul.

3

Mapped Bacteria of Mapo Source: Senseable City Lab, 2017

FIGURE 4.1.3.1 (left, top)

Mapped Bacteria of Seongbuk Source: Senseable City Lab, 2017

FIGURE 4.1.3.2 (left, middle) Mapped Bacteria of Gangnam Source: Senseable City Lab, 2017

FIGURE 4.1.3.3 (left, bottom) 157

4.2 MACHINE LEARNING PRECEDENTS 4.2.1 WHAT IS MACHINE LEARNING

Our first attempt at learning and implementing machine learning began in Boston, where Alicia and Shajay had arranged a workshop by a machine learning researcher, Cristobal Valenzuela, at Autodesk Buildspace. I would also like to personally thank Dr. doctor Jun-Yan Zhu and Tongzhou Wang, who have been very patient and helpful with my questions on the Pytorch-CycleGAN-and-Pix2Pix Github page. Cristóbal Valenzuela is a technologist, artist and software developer interested in the intersection between machine learning and creative tools. He is a researcher at New York University ITP author of RunwayML, Creative Resident at Paperspace and co-founder of Latent Studio, a creative studio specializing in machine learning and artificial intelligence. He also contributes to OSS and helps maintain ml5.js . His work has been sponsored by Google and the Processing Foundation and his projects has been exhibited in Latin America and the US, including the Santiago Museum of Contemporary Art, GAM, Fundación Telefonica, Lollapalooza, NYC Media Lab, New Latin Wave, Inter-American Development Bank, Stanford University and New York University.

Cristobal Valenzuela’s website https://cvalenzuelab.com/ 1

Source : https://pixabay.com/en/ machine-learning-brain-mindidea-3161590/

FIGURE 4.2.1.1 (left, top) 158

What is Machine Learning? Google Cloud Platform 2

Neural network Source : Neural Networks and Deep Learning by Michael Nielsen - December 2017

The initial learning phase began with understanding what machine learning is. According to Google developer Yufeng Guo , it is “using data to answer questions.” The part where the dataset is used will be training, and the answering part will be the predictions from the computer. After defining the question, data need to be gathered. The dataset, which is relevant and specific to the question that the user wants to have answered, needs to be prepared in an uniform format. The more data there are - in terms of quantity, quality and variety - the better the model will be trained. In the training process itself, the data will usually be processed through layers of matrices which identify the characteristics of the data.

These layers are also known as a Neural network. There are many variants in the Neural networks, and a lot research`h is dedicated to this field. This is because different types and structures are utilised for different tasks. Convolutional neural network is known to be good for image recognition, whereas Long short-term memory network is known to be good for speech recognition. Each ‘neuron’ in a layer holds a value.

FIGURE 4.2.1.2 (right, top)

159

The input layer recognises the given data within the framework of a matrix. Then it with the recognised values, it goes through the hidden layers to process an answer. The output layer at the end holds neurons with values which will become the result of the process, also known as predictions. The hidden layers process the data by recognising certain patterns. One layer influences the next, by detecting small relevant segments of the pattern, and informing the next layer with larger patterns. These patterns are recognised to decide how the given data should be categorised, to give a prediction. The weights (or parameters) are multiplied to the values of the neurons in order to fine-tune the detection of pattern in the data. The sum of weighted values are then adjusted to fit within the 0 to 1 value region, which is done by applying a function such as Sigmoid curve. In order to activate meaningfully only when the weighted sum is over a certain threshold value, a bias is included in the formula.

And in a nutshell, the learning process is referring to having the computer find valid weights and bias values in order to discover the optimal model for solving the problem. It is a process of multiplying matrices.

There are many types and variants of activation functions, and they serve different purposes. The Sigmoid function and the Tanh (Hyperbolic Tangent Function) have become less popular due to certain faults including vanishing gradient problem. These days, activation function ReLU ( Rectified Linear Unit) has become more popular due to its quick learning process. ReLU, however, could result in dead neurons, and so Leaky ReLU has been introduced to avoid this issue. It has a small slope below the zero point.

Another popular function is the Maxout activation function. Different activation functions are applied to different stages of learning.

ReLU is applied to hidden layers, and output layers usually use Softmax for classification or linear for regression.

4.2.2 THE PRECEDENTS Generative design process Source: https://twitter.com/peterhutten/status/787547252327849984

FIGURE 4.2.2.1 (left, bottom)

The deep neural network layers Source : ‘But what *is* a neural network?’ by 3Blue1Brown, https://www.youtube.com/watch?v=aircAruvnKk

FIGURE 4.2.2.2 (right, top)

While there are many precedents of machine learning in various fields, with the most famous one being Alpha-Go, there are not many implementations in the field of architecture or urbanism – especially not in the design process.

160

The sigmoid function Source : ‘But what *is* a neural network?’ by 3Blue1Brown, https://www.youtube.com/watch?v=aircAruvnKk

FIGURE 4.2.2.3 (right, bottom)

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In conversation with our Machine Learning tutor Critobal, we found one of the many implementations of Pix2Pix particularly interesting. Pix2Pix is an ongoing research into image translation through machine learning. Its academic name is Image-to-Image Translation with Conditional Adversarial Nets, and it was initiated

by Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros at the University of California, Berkeley in CVPR 2017. Their abstract is as follows, “We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.”3

In short, their machine learning mechanism allows for image translations according to the desired task, especially synthesizing photos from label maps, reconstructing objects from edge drawings/maps, and colorizing images. There are a few different versions of Pix2Pix depending on the machine learning framework and the cycleGAN mechanism built in. There are Tensorflow models, Chainer, Keras, and Pytorch versions available at this point (September 2018). The Pytorch model is under active development, and promised quicker learning and better results, and hence was the choice for our project. The Pytorch framework is relatively new compared to Tensorflow, but it has certain advantages such as Imperative Programming, and Dynamic Computation Graphs. It is a Define by run framework which enables the codes to be executed in sections before compiling the entire code. Pytorch is gaining popularity in research areas, while Tensorflow still proves to be a better choice for beginners and production level products.

Phillip Isola’s Pix2Pix website - https://phillipi.github.io/pix2pix/ 3

Pix-2-pix demonstration Source : Phillip Isola’s Pix2Pix website https://phillipi.github.io/pix2pix/

FIGURE 4.2.2.4 (left, bottom) 162

During the workshop at Boston, Cristobal had introduced us to a few works related to satellite images and Invisible Cities by Gene Kogan caught our attention. Gene Kogan is a mathematician who is an artist and a programmer who is interested in generative systems, computer science, and software for creativity and self-expression.4 He has been using machine learning tools to generate art, and in this particular project, Invisible Cities, he used a model trained with satellite map tiles and map data to produce artificial satellite images. Inspired by Italo Calvino’s novel Invisible Cities, the project explores the city imagined ( or predicted) by the trained computer. Here they prepared dataset using map tiles and label information generated by Mapbox, and categorised them using VVVV, a data visualisation tool.

Another similar project, called Fake-but-good-enoughfor-robots by a design and technology studio in San Francisco. In this case, the model is trained with census data to generate artificial satellite images that reflect the income level of the neighbourhood.

This shows the potential to analyse, and create cityscapes which are favourable to the residents – by predicting how a neighbourhood can change according to income levels or other factors depending on the input.

Hence the implementation of the Pix2Pix model in urbanism can provide architects and urban planners with an insight into the urban environment like never before. If such insights are utilised in the process of design – whether it is urban planning or architecture – the strength of machine learning will be far greater than any tool or intelligence at an architect or an urban planner’s disposal. With such insight provided beforehand, the architect can make informed decisions, which will also be more persuasive.

Gene Kogan’s CV on his website http://genekogan.com/cv/ 4

Invisible cities demonstration Source : Invisible cities, https://opendot. github.io/ml4a-invisible-cities/

FIGURE 4.2.2.5 (right, up)

Video by Stamen, Source : https://vimeo.com/243607633

FIGURE 4.2.2.6 (right, bottom)

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4.2.3 HOW WE ARE USING MACHINE LEARNING

Based on the two precedents, and with Shajay and Alicia’s insight into machine learning, we are building a dataset of floor plans of the residences in London. Once the model is trained with the satellite images with the floor plans of the houses, we plan to apply a layer in the network, which will generate floor plans that dynamically shift with the input of the data – something that will be vital in predicting how structures shift according to the social dynamics within the house, the block, and the entire city of London. It will be possible to paint the picture of this landscape in real time, in 2D.

At current stage, we have successfully begun training with the latest cycleGAN-Pytorch model of the Pix2Pix, and started testing with satellite map tiles provided as an example. The software Pix2Pix, is run by command in an Anaconda environment, which enables the user to execute software or programs like a Linux operating system environment. The main language of the machine learning model is written in Python 3.7, with Pytorch framework.

Other scripts in Javascript and other languages are involved in processing data. The browser window on the right displays Visom local server window, which is displaying the learning process of Pix2Pix model. Visdom is written by the Facebook Research Group, and is under active development. It is a tool for visualising and plotting data, and can be utilised for various purposes including data mining and machine learning. Given how powerful Pix2Pix model is, processing videos real time and so on, we speculate that depending on how well a dataset can be built, the results can provide powerful insights to the architect or urban planner, even before the schematic design phase begins. This can potentially lead to design decision which have not been seen or heard of before, similar to the way Alpha-Go had shown the world new tactics of Go.

We are also planning on utilising Machine Learning as a feedback tool for designing structures. T-SNE is a visualisation tool built by Google, and we anticipate it can show the architect how the structure needs to be revised in order to meet the shifting demands of the inhabitants within the structure. When inhabitants play the game, and commit changes to the structure, the data of their interaction and negotiations are recorded and mapped out via T-SNE, and will provide a visual feedback to the architect.

Screen capture of Author’s (Taeyoon Kim) screen in the process of training with satellite map tiles using Pix2Pix. The graph displays loss function and training process via Visdom on a browser window.

FIGURE 4.2.3.1 (right) 164

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4.2.4 EMBEDDINGS

Embeddings are means of visualization in machine learning. An embedding is a mapping of discrete objects, such as words, to vectors of real numbers1. In a more common sense, every entity in the world is an information point. Every point has multiple dimensions of information. For example, as explained by Google data visualizers Martin Wattenberg, Daniel Smilkov and Fernanda Viegas, consider a group of scientists. Every scientist has multiple dimensions of personality. Every information about him/her is a dimension. Their date of birth, place of birth, their fields of expertise, their awards, their contributions are all features of their personality that can effectively be converted into vectors. These features are present in all scientists, in varying quantities.2 Now, when we try to map these data points, we are confronted with the issue of high dimesnionality. As humans, we are able to perceive until three dimesnions and hence our mapping and graphic systems only offer visualization tools until the third dimension. But when the dimensions of the data point exceed the three dimensions, mapping them cannot be carried out in the conventional way. The data points need to be reduced to be mapped in the 3D or 2D mapping space.

This process is termed as dimensionality reduction. It forms the basis of how embeddings are performed.

‘Embeddings ¦ Tensorflow’ https://www.tensorflow.org/guide/ embedding 1

‘A.I. Experiments: Visualizing High-Dimensional Space’ by Google Developers, https://www.youtube.com/ watch?v=wvsE8jm1GzE&t=10s 2

Scientists represented as high dimensional points Source: ‘A.I. Experiments: Visualizing High-Dimensional Space’ by Google Developers, https://www.youtube.com/ watch?v=wvsE8jm1GzE&t=10s

FIGURE 4.2.4.1 (left, middle)

Illustration of a person as a high-dimensional vector point Source: ‘A.I. Experiments: Visualizing High-Dimensional Space’ by Google Developers, https://www.youtube.com/ watch?v=wvsE8jm1GzE&t=10s

FIGURE 4.2.4.2 (left, bottom) 166

DIMENSIONALITY REDUCTION

In statistics, machine learning, and information theory, dimensionality reduction is the process of reducing the number of random variables under consideration.3 It involves two processes - feature selection and feature extraction.4 Feature selection is how the most important features of the data points are chosen.

The most important features are the ones that sets the data points most apart. Hence, in statistical terms, those are the features wiht the most variance. Variance determines how spread out the data is.

Variance is the square of the Standard Deviation. Standard deviation is the measure of how deviated a feature is from the mean of all the features. These features with the maximum variances are called the Principal Components. The process of finding these Principal Components is called Principal Component Analysis (PCA).

Roweis, S. T.; Saul, L. K. (2000).

“Nonlinear Dimensionality Reduction

by Locally Linear Embedding”. Science.

290 (5500): 2323–2326. Bibcode:2000Sci...290.2323R. doi:10.1126/science.290.5500.2323. PMID 11125150.

3D MAPPING

2D MAPPING

XYZ AXES PLOTTING

PRNCIPAL COMPONENT AXES

3D MAPPING

2D MAPPING

XYZ AXES PLOTTING

PRNCIPAL COMPONENT AXES

3

Pudil, P.; Novovičová, J. (1998). “Novel Methods for Feature Subset Selection with Respect to Problem Knowledge”.

In Liu, Huan; Motoda, Hiroshi. Feature Extraction, Construction and Selection. p. 101. doi:10.1007/978-1-4615-57258_7. ISBN 978-1-4613-7622-4.

4

Data points mapped without Principal Component Analysis performed Source: ‘Principal Component Analysis explained visually’, by Victor Powell http://setosa.io/ev/principal-component-analysis/

FIGURE 4.2.4.3 (right, middle)

Data points mapped without Principal Component Analysis performed Source: ‘Principal Component Analysis explained visually’, by Victor Powell http://setosa.io/ev/principal-component-analysis/

FIGURE 4.2.4.4 (right, bottom)

167

T-SNE CLUSTERING

The Principal Component Analysis successfully accomplishes the extraction of the important features, a.k.a the principle components from the data points and plots them on a 3D canvas.

However, the visualization doesn’t fully reflect the clusters within the data. This is because, PCA is a linear dimensionality reduction techinique, one that only calculates the variance in the features.

It tends to maintain the global setup and the relationship of the points to one another. Hence, to visualize clusters better, a recent technique called t-SNE clustering is becoming increasingly popular.

t-distributed Stochastic Neighbour Embedding is a non-linear dimensionality reduction technique developed by Laurens van der Maaten and Geoffrey Hinton in 20085. The main difference between this technique and other linear dimensionality reduction techniques is that, it uses probability distributions to split the data into clisters. At first, t-SNE constructs a probability distribution over pairs of high-dimensional objects in such a way that similar objects have a high probablity of being picked, whilst dissimilar points have an extremely small probability of being picked. Second, t-SNE defines a simlar probablity distribution over the points in the low-dimesnional map, it minimizes the divergence between the two probability distributions with respect to the locations of the points in the map.6

While t-SNE plots reveal good clusters, it can be misleading if the parameters aren’t set right. One of the parameters that can be changed is perplexity. Perplexity is the difference between the weightage given to the global and the local parameters. Hence, a lower perplexity leads to a disperse plot, with lower cluster formation and a higher perplexity leads to more distinct clusters getting formed. Sometimes, patterns such as circles and knots evolve out of the data. It is important for the data to be curated before put to clustering, to avoid arbitrary clustering.7

van der Maaten, L.J.P.; Hinton, G.E. (Nov 2008). “Visualizing Data Using t-SNE” (PDF). Journal of Machine Learning Research. 9: 2579–2605.

5

‘t-distributed stochastic neighbour embedding’, https://en.wikipedia.org/ wiki/T-distributed_stochastic_neighbor_embedding 6

‘How to Use t-SNE effectively’ https://distill.pub/2016/misread-tsne/ 7

Different patterns of clustering evolved out of change in perplexity Source: ‘How to Use t-SNE effectively’ https://distill.pub/2016/misread-tsne/

FIGURE 4.2.4.5 (left, bottom)

The MNIST dataset subject to Principal Component Analysis visualised with the Google Embedding Projector

FIGURE 4.2.4.6 (right, top)

The MNIST dataset subject to t-SNE clustering visualised with the Google Embedding Projector

FIGURE 4.2.4.7 (right, bottom) 168

169

170

USING TENSORFLOW EMBEDDING PROJECTOR

Tensorflow has an online Embedding projector that can load tsv files and plot them using PCA and t-SNE clustering. A random dataset was generated based on the game with 10000 data points. There were 7 features included - gender, age group, inclination towards food, towards gardening, towards entertainment, towards work and towards fitness and play. Gender input was initiated from a random value between ‘male’ and ‘female’. Age group was chosen from ‘baby’, ‘child’, ‘teen’, ‘young adult’, ‘adult’, ‘middle aged’, ‘senior citizen’. The other five inclinations all took a binary value of ‘Interested’ or ‘Not interested’.

The t-SNE clustering lead to a few clusters getting created and each data point when clicked on the plot reveals its closest neighbours which can also be isolated and run more clustering on.

Randomized data subject to PCA

FIGURE 4.2.4.8 (left, top)

Randomized data subject to t-SNE clustering

FIGURE 4.2.4.9 (left, bottom)

A point selected in the t-SNE cluster and its closest 101 neighbours get highlighted

FIGURE 4.2.4.10 (right, middle) A point and its 101 neighbours isolated and PCA performed on

FIGURE 4.2.4.11 (right, bottom)

171

05

HOUSE RESEARCH 5.1 BRICK RESEARCH PRECEDENTS

5.1.1 HADRIAN X

5.1.2 MASONRY CONSTRUCTION WITH DRONES

5.1.3 GRAMAZIO KOHLER RESEARCH, ETH

5.1.4 BLOCK RESEARCH GROUP, ETH

5.1.5 ELADIO DIESTE 5.2 BRICK GEOMETRY

5.2.1 GRIPPER DESIGN 5.3 FABRICATION PROCESS

5.3.1 MOULD DESIGN

5.3.2 CREATING A TOOL PATH

5.3.3 CNC CUTTING

5.3.4 MOULDS

5.3.5 MATERIAL STUDIES

5.3.6 CASTING

5.3.7 BRICK 5.4 ROBOTIC ASSEMBLY

5.4.1 KUKA KR30

5.4.2 MHL2-20D GRIPPER

5.4.3 ABB IRB 4600

5.4.4 PZN-PLUS 160-1 GRIPPER

5.4.5 EX-MACHINA

174 174 176 178 180 182 184 184 200 200 200 200 204 206 206 206 210 210 212 214 216 220 173

5.1 BRICK RESEARCH PRECEDENTS

Our research into bricks began with the question of how we can automate brick assembly using a custom geometry for robotic arms or drones. We were referring to both the industrial implementations and the research projects of automated brick assembly.

5.1.1 HADRIAN X

There are quite a few brick assembly robots which are actively being used on construction sites already, but they still need to be driven by human constructors. Hadrian X, on the other hand, can assemble the structure without human interruption or monitoring.

It is under development by a company named FBR (Fast Build Robotics) in Australia, and is expected to build its first real structure in the fourth quarter of 2018.

Hadrian X brick assembly, Source : www.fbr.com.au

FIGURE 5.1.1.1 (left, middle) Hadrian X uses laser detection to minimise error in assembly and application of adhesive, Source : www.fbr.com.au

FIGURE 5.1.1.2 (left, bottom)

On site, the Hadrian X utilises laser sensor to keep track of its coordinates in 3-D. Source : www.fbr.com.au

FIGURE 5.1.1.3 (right, top)

Hadrian X will be deployed to the site as a truck as an automated assembly unit, which feeds the brick, applies adhesive, and places each brick.

Source : www.fbr.com.au

FIGURE 5.1.1.4 (right, bottom) 174

175

5.1.2 MASONRY CONSTRUCTION WITH DRONES

A research project from UCL in collaboration with MIT explored the potential of construction by drones. Masonry Construction with Drones, is a project where the researchers made an attempt to redefine the brick, the beam, columns and the whole construction process with drone assembly in mind. While the feasibility of drone construction is low at this point, due to their sensitivity to weather conditions and other environmental factors, their approach to modifying the brick geometry to better suit transportation by the drones inspired us to rethink what a brick is.

Custom built drone is manually controlled to stack customised bricks. The drone is equipped with suction device to grip onto the bricks.

Source : Latteur P., Goessens S., J.S. Breton, J. Leplat, Ma Z., Mueller C., Drone-based Additive Manufacturing of Architectural Structures. IASS Congress, Amsterdam, August 2015

FIGURE 5.1.2.1 (left)

The brick which was identified to be the most compatible with drones. Sturcture assembly and simulation of load bearing. Source : Latteur P., Goessens S., J.S. Breton, J. Leplat, Ma Z., Mueller C., Drone-based Additive Manufacturing of Architectural Structures. IASS Congress, Amsterdam, August 2015

FIGURE 5.1.2.2 (right, top)

We began by trying to improve what these bricks could to and tried to build structures which could actually function as load bearing parts of a house. Our version of these bricks were cast and tested in Boston, at Autodesk BUILD Space.

176

Drone compatible brick design by Caitlin Meuller. This particular geometry, which is optimised for corbelling, inspired us to attempt various geometries which could be assembled in different ways to maximise structural strength.

Source : Latteur P., Goessens S., J.S. Breton, J. Leplat, Ma Z., Mueller C., Drone-based Additive Manufacturing of Architectural Structures. IASS Congress, Amsterdam, August 2015

FIGURE 5.1.2.3 (right, bottom)

177

5.1.3 GRAMAZIO KOHLER RESEARCH, ETH

This was a 1:1 scale prototypical building structure made out of Styrofoam blocks in a four day workshop. All of the bricks are uniquely shaped and hot wire cut. The constraints of hot wire cutting method, together with the flow of forces and stability issues were considered. While our goal is not to create each unit a unique piece in assembly, this project gives us an insight to how assembly can be pushed to the limit with the constraints of fabrication.

Combined with the research papers regarding flow of forces in polyhedra from the Block Research Group at ETH, we wish to achieve bricks which can function like the components of funicular shell structures.

Packaged in a compact, modified freight container, R-O-B takes advantage of prefabrication with on site construction, utilising short transport routes. It allows for flexibility of fabrication methods and material, making full use of the versatility of the robotic arm. Although this project is almost a decade old, it stays relevant and provides a perspective into the automated construction and prefabrication.

This project shows as a real-life example of how mobile fabrication units can be deployed to sites to construct structures using robotic arm assembly. Hadrian X can be seen as an extension of this idea, and our project shares this construction method.

Smart Geomery Workshop, Explicit bricks, Barcelona 2018. Here they construct a structure out of styrofoam blocks. Source : http://gramaziokohler. arch.ethz.ch/web

FIGURE 5.1.3.1 (left, top)

A mobile fabrication unit, which can be deployed to sites. R-O-B, 2007-2008.

Source : http://gramaziokohler.arch. ethz.ch/web

FIGURE 5.1.3.2 (right, top)

22 meter long public installation. Pike Loop, Manhattan, New York, 2009.

Source : http://gramaziokohler.arch. ethz.ch/web

FIGURE 5.1.3.3 (right, bottom) 178

179

5.1.4 BLOCK RESEARCH GROUP, ETH

MycoTree was exhibited as part of Seoul Biennale for Architecture and Urbanism in 2017.

“It is a spatial branching structure made out of load-bearing mycelium components. Its geometry was designed using 3D graphic statics, keeping the weak material in compression only.

Its complex nodes were grown in digitally fabricated moulds.

Utilising only mycelium and bamboo, the structure represents a provocative vision of how we may move beyond the mining of our construction materials from the earth’s crust to their cultivation and urban growth; how achieving stability through geometry rather than through material strength opens up the possibility of using weaker materials structurally and safely; and, ultimately, how regenerative resources in combination with informed structural design have the potential to propose an alternative to established, structural materials for a more sustainable building industry.

MycoTree is the result of a collaboration between the Professorship of Sustainable Construction at Karlsruhe Institute of Technology (KIT) and the Block Research Group at the Swiss Federal Institute of Technology (ETH) Zürich.” 1

An inspiration in both material and structure, the project inspires us to rethink how material can be used in structures.

Compression only structures are not only aesthetic, but they save material and enhance overall strength. The main principle behind the MycoTree project and the timber component structure of project Cloud Living from DRL, research into flow of forces in polyhedral using graphic-statc based design gives our team an insight into how funicular structures can be built, so that we can design our brick assembly structure to be compression-only structure.

The Armadillo Vault project was the centrepiece for the Venice Biennale ‘Beyond Bending.’ Not only does this structure show how funicular shell can be asymmetrical, it shows how perfectly balanced and strong a structure can be without any mortar or joint material in between the components. This structure is the biggest inspiration for our project, and we aspire to achieve the same level of structural integrity and aesthetics. Our direction involves reducing the uniqueness of the components and composing the units in such way that they are more stackable and flexible - so that communities can be formed or dissolved and the structure can accommodate such changes physically.

Besides these projects, their research papers and the RhinoVAULT software is a great resource for the team to study the fundamentals of funicular structures and how they can be designed.

180

Heisel F., Schlesier K., Lee J., Rippmann M., Saeidi N., Javadian A., Hebel D.E. and Block P.Design of a load-bearing mycelium structure through informed structural engineering,Proceeding of the World Congress on Sustainable Technologies (WCST) 2017,2017 (December) 1

MycoTree - Seoul Biennale for Architecture and Urbanism 2017. Source : http:// block.arch.ethz.ch/

FIGURE 5.1.4.1 (right, top)

Stereotomy of Forces, a research project investigating the flow of forces in polyhedra using graphic-static based design.. Source : Lee J., Van Mele T. and Block P. Disjointed Force Polyhedra, Computer-Aided Design,99: 11-28,2018 (June)

FIGURE 5.1.4.2 (right, middle)

Armadillo Vault, Venice, Italy. The structure is comprised of 399 individually cut limestone pices. the structure is unreinforced, and assembled without mortar.

Source : http://block.arch.ethz.ch/

FIGURE 5.1.4.3 (right, bottom)

181

5.1.5 ELADIO DIESTE

Eladio Dieste (December 01, 1917 - July 29, 2000) was an Uruguayan architect and engineer, who had dedicated his life to building quality masonry architecture for the public of people.

His buildings are largely comprised of churches, markets, sheds and grain silos. He is well known for using double curvature in his masonry structure to resist buckling failure, and hence many of his structures are built with elegance and structural/material efficiency.

His structures are a big inspiration for our research, and we are in the process of developing bricks which can create the type of double curvatures in his creations.

Eladio Dieste.

Source : http://www.hafelegateway. com/2016/11/22/formun-strukturel-potansiyeli-eladio-dieste/

FIGURE 5.1.5.1 (left, top)

The Gaussian Vault of the Central Market Building.

Source: https://benhuser. com/2012/01/31/eladio-dieste-portoalegre-rs/

FIGURE 5.1.4.2 (right, middle)

Double curved masonry Source: Pedreschi, R & Theodossopoulos, D 2007, ‘The double-curvature masonry vaults of Eladio Dieste’ Proceedings of the ICE - Structures and Buildings, vol. 160, no. 1, pp. 3-11. DOI: 10.1680/stbu.2007.160.1.3

FIGURE 5.1.3.3 (right, bottom) 182

183

5.2 BRICK GEOMETRY

The brick is the beginning point of our system. The bricks assemble to create units, which create the community spaces, and together with social condenser spaces and circulation volumes create blocks.

5.2.1 GRIPPER DESIGN

We started with simple grips coupled with bricks that have grooves for the gripper. We made a simple gripper using a linear actuator or a servo motor controlled by Arduino board. At this phase, we explored gripper design and brick geometry in conjunction.

Initial gripper studies and bricks in association

FIGURE 5.2.1.1 (left)

Axonometric drawing of the residential system we propose. From author.

FIGURE 5.2.1.2 (right) 184

BRICK

UNIT

SOCIAL CONDENSOR/ CIRCULATION

COMMUNITY

BLOCK

LONDON

185

Having studied the Caitlin-Mueller bricks from the research project by UCL and MIT, we began to investigate how we can create a brick that can be stacked using a robotic arm or a drone.

The error tolerance of both methods are more than 5mm, and so we had to introduce slopes into the geometry. This is so that even when the robotic arm places the brick slightly off the coordinate, it will slide into place and stack properly. The brick was also designed for corbelling, allowing more flexibility in assembly.

Having studied the Caitlin-Mueller bricks from the research project by UCL and MIT, we began to investigate how we can create a brick that can be stacked using a robotic arm or a drone.

The error tolerance of both methods are more than 5mm, and so we had to introduce slopes into the geometry. This is so that even when the robotic arm places the brick slightly off the coordinate, it will slide into place and stack properly. The brick was also designed for corbelling, allowing more flexibility in assembly.

The initial brick was designed in this process to enable diagonal corbelling and vertical stacking at the same time, while having tolerance for errors of the robotic arm

FIGURE 5.2.1.3 (left, top)

After designing the brick, we designed the first mould for the brick. We introduced notches and grooves to make it easier to take apart after casting, as well as securing that the mould pieces assemble with precision.

FIGURE 5.2.1.4 (left,bottom)

From 3D printing to Casting. The first mould was CNC cut out of wood in London, and then assembled in Boston.

We cast our first brick and learned many lessons from our failure.

FIGURE 5.2.1.5 (right)

After the first cast, we decided to fill the core of the brick with foam material in order to decrease weight. Less weight is crucial in increasing accuracy and viability of automated assembly.

FIGURE 5.2.1.6 (left, middle) 186

We took the wooden mould to Boston, where we assembled the pieces and cast our very first brick. The mixture contained EPS particles to lighten the weight, but it destroyed details, and the de-moulding process was more difficult than we had anticipated.

The mould took some damage in the process, and we quickly shifted to the idea of creating multiple moulds in a quick and easy manner. The mixture would also have to change a lot in order to bring out the details of the brick’s geometry, which was quite intricate.

After the brick design was finished, we designed the mould. We CNC cut the mould out of hard wood using the DPL facilities at the DRL studio. We chose wood as the first material for the mould in the hope that moulds could be reused multiple times for casting. After our first cast, we quickly found out that is not the case.

Reusing moulds was a bigger challenge than we had thought.

187

Initial study in robotic assembly of bricks

FIGURE 5.2.1.7 188

First proposal was to build an arch using bricks that had variants in slope angle

FIGURE 5.2.1.8

189

Second proposal, which we executed in Boston, was to build a cubic volume using the brick geometry we cast. No mortar is used.

FIGURE 5.2.1.9 190

COATED WITH PLASTIC

PAPER BRICK

Third proposal was to use build a volume using bricks that could be attached or detached usiing heat. The plastic coating on the outside is used as adhesive.

FIGURE 5.2.1.10

191

Initial set of brick prototypes designed

FIGURE 5.2.1.11 192

193

The second version of the bricks that we have cast, here at DRL in London, came from the inspiration of the geometry of the mosque architecture in Turkey. While the corbelling mechanism is similar to the previous brick, it stacks in a hexagonal pattern which enables a better efficiency in forming a structure.

Second version of the brick casted ans assembled in London

FIGURE 5.2.1.12 (right)

While studying further into vaults and compression-only structures, we are exploring the possibility of merging the capabilities of RhinoVAULT and Dieste’s double curvature masonry structure to produce mortar-free assemblies, which can be made with bricks that are repeated throughout the whole structure. To redefine the brick for the automated construction is our goal.

194

Initial study into creating compression-only structure using bricks with limited variation. The study continues with the help of RhinoVAULT, a plugin developed by the Block Research Group at ETH.

FIGURE 5.2.1.13 (left)

195

Column Type

FIGURE 5.2.1.14

Wall Type

FIGURE 5.2.1.15

Truss Type

FIGURE 5.2.1.16 196

Column + Corbelling

FIGURE 5.2.1.17

Vault type 1

FIGURE 5.2.1.18

Big Corbelling

FIGURE 5.2.1.19 197

Vault Type 2

FIGURE 5.2.1.20

Vault Type 3

FIGURE 5.2.1.21

Column + Corbelling Type

FIGURE 5.2.1.22 198

Floor - Stairs and Roof Type 1

FIGURE 5.2.1.23

Vault type 1 + Vault type 2

FIGURE 5.2.1.24

V Column + Corbelling

FIGURE 5.2.1.25

199

5.3 FABRICATION PROCESS

5.3.1 MOULD DESIGN

Mould had to be designed in way that could help the mixtures obtain the intricate surfaces of the brick. At the same time, casting and demoulding were other key factors in the mould design - to make the whole process less laborious while increasing the precision of the cast.

5.3.2 CREATING A TOOL PATH

Autodesk Fusion 360 was used to define most convenient tool path of CNC machine in terms of time efficiency and quality.

The tool path design has played an important role in defining sharp edges and complex geometry of the brick. In this context Fusion CAM helped with its great extent of work flows and cleaning strategies for milling. Pocket Cleaning and Parallel Cleaning were two of these strategies that have been used intensely in the milling process.

POCKET CLEANING Pocket Cleaning was used as a conventional roughing strategy for clearing large quantities of material effectively. The stock is cleared layer by layer with smooth offset contours maintaining climb milling throughout the operation.

PARALLEL CLEANING Parallel Cleaning was used as a finishing strategy in which the passes are parallel in the XY plane and follow the surface in the Z-direction. We were able to control the angle and stepover in horizontal direction to give precisely detailed shapes to our moulds. Controlling this parameter was vital in reducing the overall milling time.

The overall fabrication process

FIGURE 5.3.0.1 (right, up)

The detail of how the mould assembles

FIGURE 5.3.1.1 (left, up)

The mould designed as a block

FIGURE 5.3.1.2 (right, top) Pocket cleaning procedure

FIGURE 5.3.2.1 (pg 150) Parallel cleaning procedure

FIGURE 5.3.2.2 (pg 151) 200

Material Studies

Casting

Mould Design

Creating a Tool Path

CNC Cutting

Mould

BRICK

201

202

203

5.3.3 CNC CUTTING

The generated tool path was inserted in to the Shopbot milling machine and the milling process is initiated. Two types of tips were used for two different types of cleaning . Figure 9 demonstrates the tip for Pocket Cleaning whilst Figure 10 is an image of the tip for Parallel Cleaning.

Shopbot at work

FIGURE 5.3.3.1 (left, top) Bottom part of the mould after Pocket Cleaning

FIGURE 5.3.3.2 (left, middle)

Bottom part of the mould after Parallel Cleaning

FIGURE 5.3.3.3 (left, bottom) The tip for Pocket Cleaning

FIGURE 5.3.3.4 (right, top) The tip for Parallel Cleaning

FIGURE 5.3.3.5 (right, bottom) 204

205

5.3.4 MOULDS

Foamboard was used to produce moulds due to being a low cost material. Using a low cost material was crucial in aspect of mass production of bricks. We also chose it because it is a soft material and could be broken off if needed - to make the demoulding process easy.

5.3.5 MATERIAL STUDIES

During the CNC cutting process of moulds, various material mixtures have been tested to find out the most convenient mixture for the fabrication of concrete bricks. The mixtures which needed to go through a special production process due to the intricacy of the geometry.

7 different mixtures were tested with different ratios of cement, sand, nylon fibre and water. It has been observed that all these mixtures have a direct impact on bricks strength, color, texture and geometry. Evaluation criteria of the mixtures were based on these physical features of the brick.

5.3.6 CASTING

Casting took about a hour for mixing and pouring. Since the brick has multiple surfaces, casting process was customized to achieve the intricate geometry of the brick. To illustrate, mixed concrete was put into bottom part of the foamboard mould. After the first pouring, a hexagonal piece of a foamboard which fits into the bottom part of the mould is placed on top of the mixture. Then, upper part of the mould had to be placed on top of the bottom mould to continue pouring the rest of concrete from top of the upper mould. Finally, as the pouring was done, curing process took about 3 to 4 hours minimum.

5.3.7 BRICK

41 pieces of brick were casted in 2 weeks including the cnc milling process. We could experiment different ratios of cement, sand, fibre, water and superplasticizer mixtures to be able to understand which is the optimum mix for our specific brick fabrication process. As the result, mixture 7 was the most reliable mixture among others, since this mixtures gave the most effective results in bricks as it is seen in the table below.

Bottom part of the mould

FIGURE 5.3.4.1 (right, top) Material samples

FIGURE 5.3.5.1 (right, middle) Material mixture ratios and results

FIGURE 5.3.5.2 (right, bottom) Bricks catalogue

FIGURE 5.3.7.1 (pg 156-7) 206

207

208

209

5.4 ROBOTIC ASSEMBLY

We employed robotic arms to lift and place the modular bricks we casted. At the Autodesk BUILD Space, Boston, we used the ABB IRB 4600 and at the AA Digital Prototyping Lab, we use the KUKA KR30. We use pneumatic grippers to lift the bricks.

5.4.1 KUKA KR30

With a payload of 30kg, and reach of up to 3,102 millimeters and flexible mounting position (floor, ceiling, wall or inclined position), the six-axis robot is a true automation professional.1 Available as a pair in the Digital Prototyping Laboratory of the AA School of Architecture, KUKA KR30 provides the main experimentation tool for us to test the limits and constraints of brick assembly using robotic arms. The G-codes for assembly are generated using Grasshopper add-on Robots, which is a open-source software developed by UCL. Schools including the AA and ETH are utilising this add-on, adding to the robot arm library of the program.

1 KUKA company website - https:// www.kuka.com/en-gb/products/robotics-systems/industrial-robots/kr-30# Dimensions and reach of KUKA KR30, Source: www.kuka.com

FIGURE 5.4.1.1 (right, top)

Robot Cell at AA School of Architecture Digital Prototyping Lab, Source: www. aaschool.ac.uk

FIGURE 5.4.1.2 (right, bottom) 210

211

5.4.2 MHL2-20D GRIPPER

At DPL, in combination with the KUKA KR30 robotic arm, we are using MHL2-20D pneumatic gripper, manufactured by SMC pneumatics. MHL air grippers are designed for applications that require a wide travel range of gripper fingers. The MHL is ideal for gripping many different sized parts. Finger motion is synchronized by a rack-and-pinion mechanism. The double piston construction creates a compact gripper with large holding force.2

2 ‘SMC Pneumatics‘ www.smcpneumatics.com Picture and Drawing of SMC MHL2-20D gripper, Source : https://www.smcpneumatics.com/MHL2-20D-Y7PSAPC.html

FIGURE 5.4.2.1 (right, top) 212

213

5.4.3 ABB IRB 4600

The ABB IRB 4600 has Semi-shelf capability. It can reach up to 1.73 m vertically. It has flexible mounting possibilities, and can be mounted in various ways, on the floor, semi-shelf, tilted or even hanging. Payload of 60kg. The ABB IRB4600 was our initial starting point at Autodesk Boston BUILD Space, where we tested different brick assemblies and simple brick feed mechanisms for the robot.

The pneumatic gripper was provided from the Autodesk, and we had an opportunity to develop our first set of grippers which could grip and hold onto our initial prototype bricks, which were quite heavy – 5kg to 7kg.

ABB IRB 4600 reach diagram

FIGURE 5.4.3.1 (right, top) ABB IRB 4600 specifications from the ABB website - https://new.abb.com/ products/robotics/industrial-robots/ irb-4600

FIGURE 5.4.3.2 (right, bottom) 214

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5.4.4 PZN-PLUS 160-1 GRIPPER

At Autodesk BUILD Space, we had used pneumatic gripper PZN-plus 160-1, manufactured by Schunk. This gripper is an universal 3-Finger Centric Gripper with high gripping force and maximum moments due to multi-tooth guidance.

The gripper drawings and specifications Source: https://schunk.com/us_en/gripping-systems/product/2076-0303314pzn-plus-160-1/

FIGURE 5.4.4.1 (left)

The pictures of the gripper Source: https://schunk.com/us_en/gripping-systems/product/2076-0303314pzn-plus-160-1/

FIGURE 5.4.4.2 (right, top)

Photograph of the gripper being attached to the Robotic Arm by Autodesk Staff.

FIGURE 5.4.4.3 (right, bottom) 216

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The first set of gripper fingers designed. They perfprmed well with the 3D printed plastic bricks, but weren’t strong enough to pick up the bricks casted out of concrete.

FIGURE 5.4.4.4 218

The gripper fingers were edesigned to grab and lift concrete bricks. The centric gripper was used like a parallel gripper by designing two of the fingers such that they come close together and grab one end of the brick, while the other finger grabs another. It was successful in lifting the bricks, however, the margin for error was quite limited.

FIGURE 5.4.4.5

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5.4.5 EX-MACHINA

At Autodesk BUILD Space, we used a grasshopper algorithm version of Ex-machina, a plugin developed by the resident researcher Jose Luis GarcĂ­a del Castillo. Ex-machina acts as a bridge between the rhino platform and the RobotStudio software, translating the travel path into appropriate data input according to the model of the robotic arm. It also enables a feedback loop from the RobotStudio software, so that travel path can be adjusted accordingly. Here, we prepare an algorithm in Grasshopper to pickup the brick from the brick-feed shelf and place it at a designated coordinate. Then we feed the prepared data through Ex-machina to RobotStudio, where we simulate the whole routine and check for errors. Once the travel path is confirmed, we transfer the data to the computation component of the ABB IRB4600, and execute it in real-life.

Ex-Machina plugin for grasshopper is used to specify the various actions and generate the RAPID Code.

FIGURE 5.4.5.1 (left, top)

The Machina Bridge App links the RAPID code from the Grasshopper interface to the Controller in ABB RobotStudio software.

FIGURE 5.4.5.2 (left, middle)

When the connection between grasshopper and RobotStudio is established, the actions can be sent to RobotStudio to initiate a simulation. It helps to identify errors in the code before executing the code in the actual robot.

FIGURE 5.4.5.3 (left, bottom)

Once the code is checked to be error free, the code is sent to the FlexPendant of the robot through a LAN connection and it is executed.

FIGURE 5.4.5.4 (right) 220

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06

BIBLIOGRAPHY 6.1 REFERENCES

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BRICK RESEARCH Hadrian X brick assembly, www.fbr.com.au Latteur P., Goessens S., J.S. Breton, J. Leplat, Ma Z., Mueller C., Drone-based Additive Manufacturing of Architectural Structures. IASS Congress, Amsterdam, August 2015 Gramazio Kohler research http://gramaziokohler.arch. ethz.ch/web Block research group, http://block.arch.ethz.ch/ Lee J., Van Mele T. and Block P. Disjointed Force Polyhedra, Computer-Aided Design,99: 11-28,2018 (June) Pedreschi, R & Theodossopoulos, D 2007, ‘The double-curvature masonry vaults of Eladio Dieste’ Proceedings of the ICE - Structures and Buildings, vol.

160, no. 1, pp. 3-11. DOI: 10.1680/stbu.2007.160.1.3 KUKA company website - https://www.kuka.com/en-gb/ products/robotics-systems/industrial-robots/kr-30# ‘Schunk Centric Gripper PZN-plus-160-1’ https://schunk. com/us_en/gripping-systems/product/2076-0303314pzn-plus-160-1/

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