Community Housing In The Indian Context - A Generative Approach_Darshi Kapadia

Community Housing in The Indian Context - A Generative Approach

Darshi Kapadia 15BAR08 Guided by Prof. Jinal Shah

July 2020

Institute of Architecture & Planning Nirma University Ahmedabad 382481

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Declaration I, Darshi Kapadia, 15BAR08, give an undertaking that this research thesis entitled “Community Housing in The Indian Context - A Generative Approach� submitted by me, towards partial fulfillment for the Degree of Bachelor of Architecture at Institute of Architecture and Planning, Nirma University, Ahmedabad, contains no material that has been submitted or awarded for any degree or diploma in any university/school/institution to the best of my knowledge.

It is a primary work carried out by me and I give assurance that no attempt of plagiarism has been made. It contains no material that is previously published or written, except where reference has been made. I understand that in the event of any similarity found subsequently with any published work or any dissertation work elsewhere; I would be responsible.

This research thesis includes findings based on literature review, study of existing scientific study, other research works, expert interviews, documentation, surveys, discussions and my own interpretations.

Date: 2 July, 2020

Name : Darshi Kapadia Roll number : 15BAR08 Institute of Architecture and Planning, Nirma University, Ahmedabad

Institute of Architecture & Planning, Nirma University Approval The following study is hereby approved as a creditable work on the subject carried out and presented in the manner, sufficiently satisfactory to warrant its acceptance as a prerequisite towards the degree of Bachelor of Architecture for which it has been submitted.

It is to be understood that by this approval, the undersigned does not endorse or approve the statements made, opinions expressed or conclusion drawn therein, but approves the study only for the purpose for which it has been submitted and satisfies him/her to the requirements laid down in the academic program.

Thesis Title

: Community Housing in The Indian Context - A Generative Approach

Student Name : Darshi Kapdia Roll Number : 15BAR08 Date : 2 July, 2020

Guided by Prof. Jinal Shah Visiting Professor CEPT Unitversity,Ahmedabad Nirma University, Ahmedabad

Prof. Nishant Kansagra & Prof. Purvi Jadav Assistant Professor and Thesis Co-ordinator, Institute of Architecture & Planning, Nirma University, Ahmedabad

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Acknowledgments This study on 'Community Housing in The Indian Context - A Generative Approach' was a great learning experience for me to understand and describe the generative approach to designing. I consider myself fortunate to be provided with an opportunity to present my work in this field. I am also grateful for having a chance to interact with everyone who gave me guidance for my study.

I express my deepest gratitude to my guide, Prof. Jinal Shah who assisted and guided me with patience and understanding. Her experience in the subject was a great help to shape the research process and always directing me to the required path.

I also extend my gratitude to Prof. Arpi Maheshwari who introduced me to the concepts of computational and generative design process with great passion and also helped to streamline my intentions of the conducted study.

I am deeply thankful to all my teachers and mentors at IAPNU, who shaped our understanding and notion of architecture as young students. We were blessed to be taught with so much love and motivation in our initial years. Prof. Sharad Panchal and his teachings will always be remembered. I choose this moment to acknowledge all of their contributions gratefully.

It is my radiant sentiment to place on record my best regards, deepest sense of gratitude to the institute itself, Institute of Architecture and Planning, Nirma University for providing me with this opportunity to present my effort and knowledge through this research thesis.

Towards the end, I want to thank all my friends, who have always supported me and my work over the course of the past five years. This would have especially been difficult to do without Palak and Himanshu.

My family has been extremely kind and understanding to me and my goals. This would have been impossible without their constant care. Thank you.

Table of Contents Abstract 1. Introduction 1.1 1.2 1.3 1.4

-

Aim Objective Research Questions Scope and Domain

2. Background 2.1 Community Dwellings 2.2 The Need to Redefine how we Design Community Dwellings 2.3 Relevance of Generative Design in Community Housing 3. Methodology 3.1 Strategy of Methodology 3.2 Strategy of Analysis 3.3 Strategy of Algorithm 4. Case Studies 4.1 4.2 4.3 4.4

The Case of Mumbai Strategy of Selection Case Studies Extracted Principles

5. Tools 5.1 Interactive Evolutionary Algorithms 5.2 Biomorpher

02 04 04 04 05 07 07 07 10 12 13 13 14 16 16 17 17 32 34 34 34

6. The Algorithm 6.1 6.2 6.3 6.4 6.5

Modular Approach to Algorithm Modules Used Optimization Strategy Analysis of Belapur Housing through Optimization Strategy Generated Cases

39 30 41 46 48 50

7. Case Comparison

77

8. Conclusion

81

9. Way Forward

82

10. List Of Figures

83

11. References And Bibliography

87

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Abstract:

Generative Design is a domain within the larger Umbrella of Computer Aided Design.

A set of iterations are generated based on constraints identified by the designer in the form of a required quantity, orientation, geometry, or any design intent.

The study demonstrates a generative approach to design community dwellings, taking Mumbai as a case. Principles are extracted from existing dwelling designs that form the basis of an algorithm or a system. The study focuses more on the process of designing this system rather than the final design itself. This system of generative design can then be replicated and reused for arriving at unique results for different contexts and requirements.

It takes into consideration the cues from the site - climate, topography, connectivity, and more important the design considerations set by the designer.

The designer's role in generative design processes is emphasized. The algorithm is designed in a modular fashion to enable the designer to understand, alter, and analyze parameters at each step. The primary tools used are Rhinoceros, Grasshopper3D (and its plug-ins), and Biomorpher. Biomorpher in an Interactive Evolutionary Algorithm that allows for the designer to have more control and understanding over the generated output. Hence, it better represents a designer’s process of going back and forth and emphasizes the analytical role of an architect in the process.

Generated outputs are then analysed and compared with respect to each other and the original cases studied.

1. Introduction 1.1 1.2 1.3 1.4

Aim Objective Research Questions Scope and Domain

Overview: This chapter discusses Generative design and its potential with respect to community design in the Indian context.

It establishes the aim of focusing on the process rather than the final output based on this understanding.

1. Introduction The architecture of an era often reflects the tools available at the time. The possibilities of Computational Design are not yet explored to the maximum in the Indian context.

However, it is to be noted that in reference to this study, Computational Design and Generative Design refers to the process of designing and not an architectural style.

Parameters are governed by a set of rules to generate results. These are modified to generate a number of outcomes that are analyzed based on a set of criteria. These processes need not always be digital or computational. However, computation has made these processes faster, more efficient, and given the architect more control. “The computer did not invent parametric design, nor did it redefine architecture or the profession; it did provide a valuable tool that has since enabled architects to design and construct innovative buildings with more exacting qualitative and quantitative conditions. “ Generative design can be further used to optimize these parameters by introducing constraints.

Constraints are identified by the designer in the form of a required quantity, orientation, geometry, or any design intent.

Fig.1.

Example of Iterations from Generative Design Processes

Much like any other design process, computational design processes start with an idea, concept, or intent. Digital tools are used to aid this conventional design process.

It makes the process faster, precise and helps analyze the design and compute data.

Housing has always been a crisis in Independent India. Not only is there a rising demand for new houses but slum rehabilitation projects have also become an integral and unavoidable part of all urban cities.

Often, in the pressure to execute a quantum lot at the earliest, not enough considerations are given to low cost housing designs. Residents are stripped of their way of living and have to make do with unadaptable Community Housing in the Indian Context

2

conditions. There is hardly a sense of identity and a repetitive nature can be associated with their design.

Here, generative design seems like a potential process of design. It has an ability to generate multiple unique solutions, from the same system, all following a certain set of rules. This system can have its roots in the existing dwellings and living conditions of the residents.

In the context of this study, Mumbai is taken as case and principles are extracted from existing community dwellings in the city to arrive at an algorithm. The designer's role in generative design processes is emphasized . The algorithm is designed in a modular fashion to enable the designer to understand, alter and analyze parameters at each step. Further, Optimized solutions are generated using an Interactive Evolutionary Algorithm. These algorithms as opposed to the conventional ones, allow artificial selection (by the designer). Thus, a branched tree of generated output is formed. It represents the back and forth nature of a designer's process which the linear nature of conventional Evolutionary Algorithms processes lag. The generated outputs are then compared and analyzed with respect to each other and the original cases studied.

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1.1 Aim The study aims to demonstrate the process of designing a system that uses principles and parameters that govern the design of Indian community settlements to generate a design that is relevant and responding to the contemporary context today.

The study focuses more on the process of designing the system rather than the design itself. This system of generative design can then be replicated and reused for arriving at unique results for different contexts and requirements. It takes into consideration the cues from the site - climate, topography, connectivity and more important the essence of the traditionally built dwellings.

1.2 Objectives a. To arrive at a strategy for analysis and decision making b. To explore how these principles can be applied in different conditions (environmental, topographical, geometrical) c. To compare the generated results to the original results

1.3 Research Questions a. What are the principles and parameters that govern the design of Indian community dwellings? b. How can these principles be used as the basis of a design for the same community or context? c. What role can generative design play in this process of designing?

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1.4 Scope and Domain As mentioned, this study takes into account the city of Mumbai and it's case studies. The same process can thus be applied to any city or any case. The focus here lies on the structure and understanding of the process to reach the final output. The study takes one of the case studies analyzed, Belapur Housing, and generates five optimized cases to compare to the original scenario designed without digital tools.

The same process can be applied to any given study. Alternatively a principles from a number of cases can be implemented in different permutations and combinations for different results.

The process is seen from a lens of affordable housing units. The strategy of selection of cases, the nature of the algorithm, the optimizing strategy and decision making skills are all based on the same premise. This study does not discuss the internal layouts of a single unit but focuses on the patterns and massing when a number of units come together.

The algorithm takes the footprint of the unit as an input and a starting point. This isn't t say that the process of designing a community housing goes linearly from a unit to a cluster. The process can still go back and forth. The algorithm acts as a tool to efficiently test one particular unit size and it's effects on the overall massing. One can make an alteration to the original unit by analyzing previous iterations.

The study is conducted using Rhinoceros , Grasshopper3D and its plug-ins. The algorithm is scipted in grasshopper, Ladybug is used for environmental analysis and Biomorpher is used as the Evolutionary Algorithm.

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2.

Background

2.1 Community Dwellings 2.2 The Need to Redefine how we Design Community Dwellings 2.3 Relevance of Generative Design in Community Housing Overview: This chapter discusses the concerns of the current state of community housing and contemporary architecture in the Indian Context establishing the premise for the relevance of the study.

2. Background 2.1 Community Dwellings A dwelling represents the culture, way of life and values of the community that it houses. There is a sense of belonging and safety – both to the community and the dwelling. This created not only in the way a single house is designed but also by how they come together.

Traditional houses or houses that have been designed by the people themselves best represent the needs and values of the community. It is their way of representing themselves, interacting with the community and creating a safe abode. Moreover, these typologies have arrived on the basis of familiarity of the context and climate.

Fig.2.

[ Untitled Photograph of Aranya Housing by B.V Doshi]

https://www.sangath.org/projects/aranya-low-cost-housing-indore/

2.2 The need to redefine how we design community dwellings 2.2.1 Post Disaster Reconstruction Traditionally, PDR does not take into consideration the complexities of the society it has to house.

Its cultural and architectural values are overshadowed by the practical task of efficiently building the required number of houses. It is to be noted that these reconstructed houses are not a temporary solution. The socio-economic condition of the residents might lead this house being the only one the family is able to construct for them and the generations to come. Thus, much like any project, their design requires the architectural sensitivity that must sustain the community in years to come without hindering their cultural well-being.

The state, recognizing these issues, pushed for process- related rather than outcome- related evaluation Community Housing in the Indian Context

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of these reconstructed houses.

The relatively new focus on ‘disaster resilience’ echoes earlier suggestions that PDR (Post Disaster Reconstruction) can provide an opportunity to “Build Back Better” 1 and can play a role in “building a culture of safety and resilience” for the long term. 2 The ‘Owner Driven Reconstruction’ is a model that seeks to empower and dignify the process of reconstruction for residents. Each family is provided with funding for their houses and are aided and approved by engineers based on local context (urban, rural, construction technology).

This approach although, proven to be successful, comes with its own challenges. The most important being – scaling up. It required more people and communities to cooperate and agree to. The process also began to be corrupted as it grew larger, leaving socially and economically weaker masses more vulnerable in the wake of the disaster. It also led to decrease in quality and repetition of modules. A cluster-based repetition was also tried. But it eventually came down to the same problem it was meant to solve – not considering the local context, culture, livelihood and daily activities of the people.

A new approach to Post Disaster Reconstruction that considers the regional context and needs of the community that does not necessarily require their full involvement in every decision making is required for solutions that are both efficient and sensitive.

2.2.2 Contemporary Architecture in India An architectural transformation is often an answer to a cultural, societal, political or climatic uprising at a given time.

India post-independence created significant circumstances for Modernism to find its roots. A vision for a new India that looked past the centuries of submission and backwardness was critical. It was optimistic approach of reconstruction of society through the reconstruction of built form. It was an aspiration to create a new identity after years of resistance and struggle. Modernism was seen as a representation of a nation with new beginnings and new hope. Le Corbusier’s Chandigarh was one of the first steps to realise this vision.

Through 1960s and 1970s a resistance to these western ideologies had begun. Manifestation of modernism in the Indian context did not respond to the cultural values. Chandigarh as a city did not offer significant existing contextual challenges to respond to. Ahmedabad, with its rich heritage and 1

Sendai framework for Disaster Risk Reduction 2015–2030

2

(Hyogo Framework for Action 2005–2015) (IFRC, 2004; UN-Habitat, UNHCR, and IFRC, 2012; UNISDR, 2005,

2015).

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cultural past, became home to Modernism where Le Corbusier did not mimic but rather absorbed, recreated and pointed toward the potential remaking of the context.

Eventually economic and social struggles overwhelmed India. It was still divided by caste, economy and class. Architectural Modernity had come before social Modernity. Cultural, societal and architectural values began to fall prey to the required pace of development.

By this time global trends had also started influencing India. Indian architects struggled to assert regional identities in the face of globalization. The first two decades of the twenty first centuries saw establishments of several cooperates. This brought economy to the country but also started to change the landscape of Indian cities to glass and metal clad models borrowed from a different context and culture. The priority now was to respond to the required density and parking. This was most prominent in office buildings. Cooperate buildings began to be designed for desks and not people. Manicured lawns, central air conditioning, sky scraping manifestations of a pseudo-culture dominated over regionalism.

Still struggling to find an identity- India began to identify these predictable landscapes to the notions of development. Urbanizing and urbanized city have kept adopting these models till date. The era of homogeneity continues.

The morphology of a city represents the forces that are influencing the larger social meanings of the city. The morphology should, in other words, go back not so much to the ever-changing forms of the city, but rather to the rationale underlying its process of urban transformation over the centuries. Here, morphology is seen as more than a visual expression. It is seen as an identity representing the regional context and culture.

These times, much like modernity in the 1950s, provide a basis for another transformation. An opportunity for establishing an identity that values humanization over density. Here, the identity is not realized by superficial form or dominance by scale but a sensitive intervention that has been derived from contextual and cultural principals that it is set in. It challenges but does not shy from global advances taking place but embraces it within the rich inheritance the city has to offer.

It also addresses the fact that identities much like culture in today’s context is fluid and dynamic. The transformation and its identity are focused on not as a final physical manifestation but the intent, reasoning and basis of it is to be propagated through architecture.

Here culture is seen as a driving force for this transformation. The approach of “critical regionalism” is redefined. It is a response to the society with which it interacts. The study aims to situate itself in the shift from the architecture of yesterday to the culture of tomorrow by drawing cues from the immediate context of it is a part of. It investigates the role of architecture in the contributing to as well as “interrupting” the force of globalization.

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2.3 Relevance of Generative Design in Community Housing Architecture of an era often reflects the tools available at the time. The possibilities of Computational Design are not yet explored to the maximum in the Indian context. Often, in the pressure to execute a quantum lot at the earliest, not enough considerations are given to low - cost housing designs.

Here, generative design is seem as a potential process of design. It has an ability to generate multiple unique solutions, from a same system, all following a certain set of rules. This system can have it's roots in the existing dwellings and living conditions of the residents.

Similar attempts have been made by architects earlier. Haegler, MĂźller and van Gool created 190 design rules to model complete city of Pompeii including the streets and placement of trees taking cues from ground plans and drawings/sketches of building types in ancient Pompeii provided by archaeologists.

Fig.3.

Bojan Tepavcevic and Vesna Stojaković ( 212) ,Various (hypothetical) views of the ancient Pompeii, based on real building foot-

prints modelled with CGA shape grammars from Facta universitatis - series Architecture and Civil Engineering

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3.

Methodology

3.1 Structure of Methodology 3.2 Strategy of Analysis 3.3 Strategy of Algorithm

Overview: This chapter discusses the process of arriving at the algorithm and the decisions involved in the process.

3. Methodology 3.1 Structure of Methodology Strategy of Selecting Case Studies

Case Studies

Initial Analysis

Extracting Principles

Designing the Algorithm

Optimization Strategy

Generating Optimized Outputs from altering Inputs

Analyzing Original Case Study Through Optimization Strategy Comparing Generated Outputs to Original Case Study

Comparing Generated Outputs to each other

Concluding Analysis Fig.4.

Structure of Methodology

The selected studies are chosen on the basis of a defined strategy that comes from the intent. Here, it is to design affordable housing. For arriving at an optimizing strategy, one needs to first have an understanding of the principles behind the design of these case studies. These principles are extracted after an initial analysis. An algorithm is designed based on this the optimizing strategy. At same time, The initial case study is analyzed through this optimizing strategy. Optimized solutions are then generated using the deigned and evolutionary algorithm. The resultant outputs are compared with each other as well as the original case to arrive at a concluding analysis.

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3.2 Strategy of Analysis Case Studies

1. Primary Data

1.1 Location 1.2 Climate 1.3 Topography 1.4 Density of People

2. Geometric/Spatial Parameters

3. Quantitative Parameters

4. Environment Comfort

2.1 Cluster Patterns 2.2 Street Patterns 2.3 Vertical Connections 2.4 Attractor Points (For views, Placement of common and open spaces, Entries, Amenities) 2.5 Hierarchy of Open Spaces

4.1 Ventilation 4.2 Views 4.3 Sunlight Radiation 4.4 Daylight Hours 4.5 Natural Drainage 4.6 Proximity ( to attractor points)

3.1 Ratio of Built vs Open 3.2 Percentage of Open Spaces 3.3 Percentage of Streets 3.4 Ratio of Street Width to Building Height 3.4 Variance of Open Spaces

GLOBAL Fig.5.

LOCAL REGIONAL GLOBAL

Strategy Of Analysis

LOCAL Fig 5. elaborates the ways in which various cases can be analyzed.

Here, local implies unit level, regional implies cluster level and global implies site level.

REGIONAL

GLOBAL

Ventilation Proximity to Open Spaces View To/From Attractors Sunlight Hours Sunlight Radiation

Fig.6.

Community Housing in the Indian Context

and

Scale Of Analysis

13

3.3 Strategy of Algorithm INPUTS Default Inputs 1. Primary Data: Constant for particular Context

ALGORITHM

Unit Footprint lowest level

on

the

GENERATIVE OUTPUT

FINAL OUTPUT

ANALYSE

Parametric Inputs 2. Geometric/Spatial Parameters: used as basis of massing

3. Quantitative Parameters:

may or may not be used as inputs

Fitness Criterion

3. Quantitative Parameters 4. Environment and Comfort

Spatial Qualities Qualitative analysis by the designer

The combination of these different parameters generates unique outputs

Fig.7.

Strategy Of Algorithm

The Primary Data is the default input in the algorithm. It stays constant for a particular unit footprint.

Geometrical, spatial and quantitative and parameters can be used are used as basis of massing.

Combinations of these different parameters will generate unique outputs following the same rules.

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4.

Case Studies

4.1 The Case of Mumbai 4.2 Strategy of Selection 4.3 Case Studies

Belapur Housing, Charles Correa CIDKO Housing, Raj Rewal Haji Kasam Chawl Udaan Housing, SP+A

4.4 Extracted Principles

Overview: This chapter explains the selected case studies are the intent behind selecting these among many. It also covers the initial analysis of these studies and principles extracted from this analysis.

4. Case Studies 4.1 The Case of Mumbai Density and economic dwellings have been issues Mumbai has been dealing with since a long time.

Slum rehabilitation often comes at the expense of quality of life for its residents. Hence Mumbai seen as a potential to test t this design process as it shares the same concerns from which the need for this research arrives.

Developers are given extra FSI as a compensation for building these projects. In most cases, these become dense but lifeless towers with little or no consideration for the habitants.

Fig.8.

A case of Slum Rehabilitation in Mumbai

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4.2 Strategy of Selection

1. Climate and Context: Selecting cases with the same climate is essential for comparison of different cases and also to try and testing different combinations of extracted principles extracted. The cases selected here all belong to the same climate and geographical location, Mumbai. One can carry out the same process for a different climatic zone It should also be noted that the built form is often influenced by the context. Taking cases from the same climate but a different contexts would require another layer of analysis.

2. Geometrical Organization: The selected cases have a radial cluster- a common space is surrounded by built units.

3. Affordable Housing: The potential for this design process can be best explored in the domain of affordable housing as it offers challenges that can be overcome by generative design. Hence the size of the unit is not more than 50 M 2 4. Height: As the study discusses the issue of density vs the quality of life, cases with varied heights are considered.

5. Relationship of the solid and the void: When the spaces inside the houses are limited, most of the time is spent outside. These open and semi open spaces hence become the life of this community. This factor is seen of utmost importance in the domain of this thesis. The selected cases have managed to respond to this relationship and make it work.

4.3 Case Studies 1.

2.

3.

4.

Belapur Housing, Charles Correa CIDKO Housing, Raj Rewal Kaj Kasam Chawl Udaan Housing, SP+A

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1. Belapur Housing, Charles Correa

Fig.9.

[ Untitled Photograph of Belapur Housing]

http://www.charlescorrea.net/projects/belapur -a

Fig.10. [ Untitled Photograph of Belapur Housing]

http://www.charlescorrea.net/projects/belapur -b

Fig.11. Cluster, Belapur Housing

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Fig.12. Section 1 Through Cluster , Belapur Housing - a

Fig.13. Section Through Cluster Belapur Housing - b

Fig.14. Elevation, Belapur Housing - a

Fig.15. Elevation, Belapur Housing - b

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2. CIDKO Housing, Raj Rewal

Fig.16. [ Untitled Image of CIDKO Housing, Belapur] http:// www.rajrewal.in/projects/housing-cidco.htm -a

Fig.17. [ Untitled Image of CIDKO Housing, Belapur] http:// www.rajrewal.in/projects/housing-cidco.htm -b

Fig.18. Elevation, CIDKO Housing - a

Fig.19. Elevation, CIDKO Housing - b

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Fig.20. CIDKO Housing

Fig.21. Section, CIDKO Housing

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3. Haji Kasam Chawl

Fig.22. Corridor Space in Haji Kasam Chawl (2007)

CRIT

Fig.23. [ Untitled Image of Haji Kasam Chawl ] https://www. dnaindia.com/topic/haji-kasam-chawlChawl - b

Fig.24. Haji Kasam Chawl

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Fig.25. Common Space, Haji Kasam Chawl

Fig.26. Elevation, Haji Kasam Chawl

Fig.27. Section, Haji Kasam Chawl -a

Fig.28. Section, Haji Kasam Chawl -b

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3. Udaan Housing, SP+A

Fig.29. [Untitled Rendering of Udaan Low- Cost Housing] https://architecturelive.in/udaan-low-cost-mass-housing-projectat-mumbai-by-sameep-padora-and-associates/

Fig.30. Udaan

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Fig.31. Internal Streets, Udaan - a

Fig.32. Internal Streets Udaan - b

Fig.33. Elevation , Udaan

Fig.34. Section, Udaan

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Belapur Housing CIDKO Housing Year of Completion No of Houses

Unit Size Cluster Size Number of Houses in a cluster Density Maximum No. Of Floors

Haji

Kasam Udaan Housing Chawl

1986

1993

-

2015

600

1048

500

552

45 M2

30 M2

15M2

30M2

20Mx20M

50MX50M

20MX25M

10MX36M

7

42

80

100

500/ha

550/ha

6250/ha

1395/ha

2

3

4

5

Fig.35. Case Studies - Quantitative Analysis

The above mentioned data is for a particular kind of cluster (in cases where there are more than one kind of clusters or units). Clusters and units with the maximum number of floors are selected for the study.

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Belapur Housing Charles Correa

CIDKO Housing Raj Rewal

Haji Kasam Chawl

Udaan Housing SP+A

Fig.36. Case Study Analysis - 1

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Belapur Housing Charles Correa

CIDKO Housing Raj Rewal

Haji Kasam Chawl

Udaan Housing SP+A

Fig.37. Case Study Analysis - 2

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Belapur Housing Charles Correa

CIDKO Housing Raj Rewal

Haji Kasam Chawl

Udaan Housing SP+A

Fig.38. Case Study Analysis -3

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Belapur Housing Charles Correa

CIDKO Housing Raj Rewal

Haji Kasam Chawl

Udaan Housing SP+A

Fig.39. Case Study Analysis -4

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All the case studies, as intended, have been designed to have an integral connection to the open spaces.

All units have a direct or indirect view/physical connection with an open space. The nature of these vary.

As we go from Belapur Housing to the Haji Kasam Chawl, the density and height both increase.

Udaan takes the nature of interactions in the typical chawls of Mumbai and tries to recreate them within a healthier environment to reside in. The relationships between the corridors and the courtyard reimagined. Streets are formed alternatively, facing each other, at various levels. To compensate for the lack of ground space on the upper level, double height common spaces are integrated. This space also becomes the connecting element to the larger common open space the built form surrounds. However, like the chawls, there is an absence of private open spaces. Although the density is not comparable to the chawls, it manages to achieve almost double the density of both Belapur and CIDKO. Belapur Housing offers a unique potential for repetition thus allowing an interesting street network for pedestrians. A hierarchy of common open spaces are organically integrated in the street network as well. Space around the cluster become streets in CIDKO. Corridors become the streets in the case of Haji Kasam Chawl and Udaan.

The built spaces decrease as we go up in thecase of Belapur and CIDKO, forming terraces. The same layout is repeated on all floors in Haji Kasam Chawl. It the vertical repetition in Udaan that makes its characteristic open spaces.

In Belapur Housing, all units are placed inwardly around a common open space. However, the open spaces on the first floor does not much possibility of interaction with it. Whereas in the case of CIDKO, as each plot is a multi family, house not all units have access to the common space, some are outward looking. In Haji - Kasam chawl all units face either of the courtyards formed. Similarly, all units in Uddan face the inner courtyard.

This initial analysis of case studies form the basis of the Extracted Principles (4.4, Pg 32) and Optimization Strategies (6.3 ,Pg 46). Belapur Housing is seen as a potential case to take forward, for its unique modular approach , street networks and their integration of a hierarchy of open spaces within it. This is reflected in the next section, Extracted Principles (4.4, Pg 32). A similar approach can be used for any chosen case study.

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4.4 Extracted Principles (to design algorithm for Belapur Housing in this case) 1. Pattern Of Repetition

Module

Repeating modules to Generate different Patterns Fig.40. Pattern Of Repetition

2. Ratio of Hierarchy of Open Spaces

16:4:1 Fig.41. Ratio of Hierarchy of Open Spaces

3. Vertical Repetition

Fig.42.

Vertical Repetition

4. Relationship of Private Open Spaces to Common Open Spaces

Fig.43. Relationship of Private Open Spaces to Common Open Spaces

Community Housing in the Indian Context

5. Logic Of Street Network

Fig.44.

Logic Of Street Network

32

5.

Tools

5.1 Interactive Evolutionary Algorithms 5.2 Biomorpher

Overview: This chapter discusses the interactive nature of the tool used for the evolutionary algorithm, Biomorpher.

5. Tools 5.1 The need for Interactive Evolutionary Design Processes Quantitative parameters are mathematically computed thus and can be compared by the computer. The qualitative analysis is up to the decision maker and their ability, skill, intuition and experience. Thus, a computer cannot make a decision between a number of optimal solutions generated by an evolutionary algorithm.

Evolutionary Design Processes are a linear approach to design processes. Fitness criterions, gene pools and a genotype are taken as an input and generation of solutions are computed one after other. These iterations are then analyzed. The process is repeated till the designer arrives at a preferred solution.

It can be argued that the way we think and design is far from linear. A design process is always a back and forth development. A computer aided design process which reflects this structure, sequence and pattern of the designer’s thinking process can lead to a better understanding and conviction of the final design solution chosen. This non-linear design process takes the designer’s preference as an input. iterations are generated, the designer’s preference is taken into account, more interactions are generated based on this preference. The same process is repeated till the designer finds a preferred design solution. This leads to a branched generation of iterations. The designer gets a comprehensive understanding of the entire process.

The consequences of each preference of the designer on the solutions generated and its performance can be analyzed and the preference itself would be go through a process of refinement and clarity.

G0

G1

G2

G3

G4

G5

P 0.0 P 0.1 P 0.2 P 0.3 P 0.4 P 0.5

P 1.0 P 1.1 P 1.2 P 1.3 P 1.4 P 1.5

P 2.0 P 2.1 P 2.2 P 2.3 P 2.4 P 2.5

P 3.0 P 3.1 P 3.2 P 3.3 P 3.4 P 3.5

P 4.0 P 4.1 P 4.2 P 4.3 P 4.4 P 4.5

P 5.0 P 5.1 P 5.2 P 5.3 P 5.4 P 5.5

Fig.45. Linear Evolutionary design process

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G0

P 0.0 P 0.1 P0.2

P 0.3 P 0.4

P 0.0.0 P 0.0.1 G 0.0 P 0.0.2 P 0.0.3 P 0.0.4

G 0.0.0

G 0.0.1

P 0.0.0.1 P 0.0.0.1 P 0.0.0.2

P 0.0.0.3 P 0.0.0.4

P 0.0.1.0 P 0.0.1.1 P 0.0.1.2

P 0.0.1.3 P 0.0.1.4

G1

P 1.0 P 1.1 P 1.2

P 1.3 P 1.4

G2

P 3.0 P 3.1 P 3.2

P 3.3 P 3.4

P 1.0.0 P 1.0.1 G 1.0 P 1.0.2 P 1.0.3 P 1.0.4

G 2.0 G 2.1

G 1.0.0

P 1.0.0.0 P 1.0.0.1 P 1.0.0.2

P 1.0.0.3 P 1.0.0.4

P 2.0.0 P 2.0.1 P 2.0.2 P 2.0.3 P 2.0.4 P 2.1.0 P 2.1.1 P 2.1.2 P 2.1.3 P 2.1.4

Artificial Selection Fig.46. Non- Linear Evolutionary design process

A designer might have some architectural aspirations and visions that cannot be communicate to the computer. These are based on the designer’s experiences, knowledge and perspective that is unique to them.

IEA enhances this individuality of the designer by this preference-based model. It seeks to create a balance between a high performing design and a preferred design.

5.2 Biomorpher Biomorpher is a plugin for Grasshopper3D developed by Jon Harding. It allows artificial selection in Evolutionary Algorithms for a more involved experience of the designer.

The tool classifies the generated populations based on similar prototypes and considers one prototype that represents that category. It also gives a graphical representation on the deviation of each prototype from the average. A user can then generate further iterations or artificially select one preferred prototype and achieve further iterations similar to those. Eventually a larger branched tree of prototypes is achieved and the designer can always go back and forth to reinstate previous generations.

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Fig.47. Clusters in Biomorpher

Fig.48.

Community Housing in the Indian Context

Generations In Biomorpher

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Fig.49.

Interface , Biomorpher

Fitness criteria are derived from certain needs - to achieve maximum space beneath, to use minimum material, to have a larger shadow etc. It must however be noted that these should be opposing fitness criterion to generate any kind of results. A set of criteria like minimizing surface area and minimizing height would only generate flat panels.

Certain fitness criterion can be given more preference than others based on need. It. Here however, they are all given equal preference.

Each variation is known as a ‘phenotype’ and the set of parameters in known as ‘genome’. This study, however does not aim to demonstrate how to use a certain a plug-in or software.

These outputs are generated by the plug – in. The designer has little role once the fitness criteria are set. They can control if the outputs are ‘elite’ – satisfying only one fitness criteria but to the maximum or ‘parento fronto’ – an optimized mix. However, analyzing these results is based on the complete understanding and skill of the designer.

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6.

Algorithm

6.1 6.2 6.3 6.4

Modular Approach to Algorithm Modules Used Optimization Strategy Analysis through Optimization Strategy 6.5 Generated Cases Overview: This chapter discusses the approach and intent of the algorithm designed.

The selected case study is analyzed after arriving at the optimized strategy.

5 Cases with the same site area and dimensions but different unit sizes and number of floors are generated.

6. Algorithm 6.1 Modular Approach to Algorithm The most common critique and misconception about generative design processes is regarding the involvement and role of the designer. The entire process needs to be seen beyond the input and the output. The relationship of each parameter with the output needs to be established and analyzed. Only then can one make design decisions. The reasoning behind these decisions becomes crucial when you have an infinite number of options made available to you.

To enable this approach of decision making in the process, the algorithm is scripted in a modular fashion.

The design process, from the unit to overall massing is broken down into several steps. Each step becomes a module. The output of one becomes the input for the next but they can also function independent of each other. This allows the designer to have more interaction with the design process. The input at each step can be altered if required. The designer has more control and understanding of the process. The modular approach also enable one to try different permutations and combinations from different cases

Fig.50. Modular Approach To Algorithm

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An Algorithm is designed based on Extracted principles (4.4 Pg. 32), keeping the case of Belapur housing in context. Each step of the working of the algorithm is demonstrated again with the case Belapur Housing.

The working of the algorithm also forms the basis of the next step. An Optimization Strategy (6.3 , Pg 46) is formed to generate the overall massing through the algorithm. These can also be called fitness criteria. These are parameters that would determine how "desirable" of "fit" an output is. In this case, they are focused on environmental analysis, views and proximity to open spaces.

The original case of Belapur Housing is analyzed through the same principles as the optimizing strategy.

These results are considered the starting point for generating cases. The goal would be to achieve the same quality of spaces for a denser massing. Inputs of the generated cases are chosen accordingly.

Cases are generate using the algorithm and the optimizing strategy. The inputs in each case is varied such that the relationship between the parameters and the output can be analyses. Several iterations are generated fro each case and the optimal is selected. These are then compared to each other and also to the original scenario of Belapur Housing.

Extracting Principles

Designing the Algorithm

Optimization Strategy

Generating Optimized Outputs from altering Inputs

Analyzing Original Case Study Through Optimization Strategy Comparing Generated Outputs to Original Case Study

Comparing Generated Outputs to each other

Concluding Analysis

Structure of Methodology, Excerpt from Fig 4. , Pg 12

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6.2 Modules 6.2.1. Inputs For Modules

Entry Points

• Unit Length • Unit Width • Floor Height • Plot Length • Plot Width • Angle Of Rotation • Plot Margin • Type of Street Network • Street Width • Maximum Number Of Floors • Plot Boundary • Entry Points • Trees • Location EPW file

Fig.51. Site Geometry

6.2.2 Scripted Modules 2. Cluster Repetition

1. Plot Repetition

Input : Cluster Pattern Output : Cluster Repetition

Centre of Pattern

Input : Size of Plot Output : Cluster Pattern

Fig.52. Cluster Pattern - Belapur Housing

Fig.53. Cluster Repetition - Belapur Housing

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3. Pattern On Plot Input : Cluster Repetition

Plot Boundary

Plot Margin

Angle Of Rotation

Output : Pattern on Plot

Centre of Open Space I

Centre of Open Space II

Centre of Open Space III

Fig.54. Pattern On Plot

4. Remove Trees Input : Pattern on Plot

Trees as Curves Output : Pattern On Plot

Fig.55. Trees On Plot before and after removing Trees

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5. Vehicular Street Network Input : Centre of Open Space II

Centre of Open Space III

Plot Boundary

Type of Street Network

Entry Points Output : Street Network

Fig.56. Types of Street Network

6. Street Network On Plot Input : Street Network

Pattern On Plot

Street Width Output : Street Network On Plot

Fig.57. Final Plot Configuration

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7. Vertical Repetition Input : Unit Length -x

Unit Width -y

Floor Height -z Output : Unit Width Of Private Open Space

z x y Fig.58. Vertical Repetition

8. Massing Input : Unit

Pattern On Plot

Floor Height

Width Of Open Space

Maximum Number of Floors Output : Massing

Fig.59. Massing - a

Fig.60. Massing -b

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6.2.3 Other Variations

Fig.61. Manually Added Street Networks Fig.62. Removing Points to Alter Street Network

Fig.63. Altering Entry Points and Type of Street Network

Fig.64. Other Variations

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6.3 Optimization Strategy 1. Minimize Ratio of Built Space: Private Open Space This would create more private open spaces.

Fig.65. Built Space and Private Open Space

2. Maximize FSI

Fig.66. Massing

3. Minimize Solar Radiation (with Ladybug) 4 .Maximize Sunlight Hours(with Ladybug)

Optimum Sunlight Hours= 6

Fig.67. Sunlight Hour Analysis

Community Housing in the Indian Context

Fig.68. Sunlight Radiation Analysis

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5. Maximize Views From Private Open Spaces to Common Open Space (with Ladybug) This would ensure interaction between units in the clusters.

Fig.69. View Analysis

6. Maximize sky view from Private Open Space (with Ladybug) Increasing the sky view from Private Open Spaces ensures healthy qualitative space.

Fig.70. Sky View Analysis 3D Projection(left) and Plan (right)

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6.4 Analysis of Belapur Housing through Optimization Strategy Original case studies are analyzed through the lens of the optimization strategy. In the scope of this study, the following process is conducted one of the cases studies, Belapur Housing. Same set of analysis is conducted on the original as well as generated outputs to further compare them.

Built : Private Open Space = 1:3 Fig.71. Built :Private Open Space, Belapur Housing

View To Common Open Space = 42.97% Fig.72. View to Common Open Space, Belapur Housing

Sky View from Private Open Spaces= 50.31% Fig.73. Sky View, Belapur Housing

Sunlight Hours = 5.9

Sunlight Radiation = 1232 kWh/M2

Fig.74. Sunlight Hours, Belapur Housing

Fig.75. Sunlight Radiation, Belapur Housing

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Built : Private Open Space = 1:3

Fig.76. Built :Private Open Space, Belapur Housing

View To Common Open Space = 42.97%

Fig.77. View to Common Open Space, Belapur Housing

Sunlight Hours = 5.9 Fig.79. Sunlight Hours, Belapur Housing

Community Housing in the Indian Context

Sky View from Private Open Spaces = 50.31% Fig.78. Sky View, Belapur Housing

Sunlight Radiation = 1232 kWh/M2

Fig.80. Sunlight Radiation, Belapur Housing

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6.5 Generated Cases Using the designed algorithm (6.2 Pg 41) and the Optimization Strategy (6.3 Pg 46), cases are generated through Biomorpher (5.2 Pg 35).

A site area of 1 Ha (120x80M) is considered. The maximum number of floors is increased from the original case study, Belapur (2 floors) to arrive at a denser solution. However, this is only the maximum number of floors, the massing generated will all built forms varying from 2 floors to this number. A terrace is considered on every floor. Different features and street networks are considered in each case to test the algorithm. The cases are optimized for 3 generations each. Hence, a total of 180 cases have been generated for this process. Outputs which satisfy the fitness criteria and spatial qualities are selected for each case.

Fig.81. Generation 1, Case 1

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Case 1: The maximum number of floors are doubled to 4 floors but the unit (6Mx3.5M) and plot size (6x6, 6x8) is the maintained as the original. Some of the open spaces are considered are a parking space and a vehicular street network is chosen to reach these spaces. It is observed that the open spaces on the perimeter might not be used optimally.

Case 2: Keeping the unit size, plot size and maximum number of floors same as case 1, the street network is changed to a peripheral one with small pockets for parking. Trees are introduced instead.

Case 3: The unit size is increased to 8Mx4.5M and the plot size proportionately to 8MX8M and 8MX10.5M. The maximum number of floors are 4 (same as case 1 and 2). The peripheral street network in maintained.

Case 4: Number of floors are increased to 5. Other parameters are kept same as the case 3.

Case 5: A case leaning towards an extreme case is considered fro comparison. The unit size of 6x4.5 is disproportionately placed in a plot of 6x6 and 6x8. The maximum number of floors considered are 5. A road connecting the two ends of the peripheral street network is introduced.

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1. Case 1 Input: Unit Size : 6x3.5 M Plot Size: 6X6, 6X8 Maximum Number Of Floors: 4 Street network: To Parking

Fig.82. Massing, Case 1 - a

Fig.83. Massing, Case 1 - b

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Fig.84. Layout, Case 1

FSI = 0.79

Fig.87. Built: Private: Open Space, Case 1 -a

Built: Private Open Space = 0.98

Community Housing in the Indian Context

Fig.85. Street Network and Common Open spaces, Case 1

BUILT:OPEN =41:49

Fig.88. Sunlight Hours, Case 1 -a

Average Sunlight Hours=6.03 Hrs

Fig.86. View to Common Open Space, Case 1 -a

Average View to Common Open Space: 43.67%

Fig.89. Sunlight Radiation, Case 1 -a

Average Radiation= 951.63 kWh/M2

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Fig.90. Massing, Case 1 - c

BUILT:OPEN =60:40 FSI = 0.92

Fig.91. Built: Private: Open Space, Case 1 -b

Built: Private Open Space = 0.98 Community Housing in the Indian Context

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Fig.92. Sunlight Hours, Case 1 -b

Average Sunlight Hours=6.03 Hrs

Fig.93. Sunlight Radiation, Case 1 -b

Average Radiation= 951.63 kWh/M2

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Fig.94. View to Common Open Space, Case 1 -b

Average View to Common Open Space: 43.67%

Fig.95. Sky View from Private Open Spaces, Case 1 -b

Average Sky View: 33.89% Community Housing in the Indian Context

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2. Case 2 Input: Input: Unit Size : 6x3.5 M Plot Size: 6X6, 6X8 Maximum Number Of Floors: 4 Street network: Along Perimeter

Fig.96. Massing, Case 2 - a

Fig.97. Massing, Case 2 - b

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Fig.98. Layout, Case 2

FSI = 0.93

Fig.101. Built: Private: Open Space, Case 2 -a

Built: Private Open Space = 0.98

Community Housing in the Indian Context

Fig.99. Street Network and Common Open Fig.100. View to Common Open Space, Case spaces, Case 2 -a 4 -b

BUILT:OPEN =47:43

Fig.102. Sunlight Hours, Case 2-a

Average Sunlight Hours=5.5 Hrs

Average View to Common Open Spav: 41.75%

Fig.103. Sunlight Radiation, Case 2 -a

Average Radiation= 863.42 kWh/M2

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Fig.104. Massing Case 2 -c

BUILT:OPEN =47:33 FSI = 0.93

Fig.105. Built: Private: Open Space, Case 2 -b

Built: Private Open Space = 0.98 Community Housing in the Indian Context

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Fig.106. Sunlight Hours, Case 2 -b

Average Sunlight Hours=5.5 Hrs

Fig.107. Sunlight Radiation, Case 2 -b

Average Radiation= 863.42 kWh/M2

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Fig.108. View to Common Open Space, Case 2 -b

Average View to Common Open Space: 41.75%

Fig.109. Sky View from Private Open Spaces ,Case 2 -b

Average Sky View:35.54% Community Housing in the Indian Context

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3. Case 3 Input: Unit Size : 8x4.5 M Plot Size: 8X8, 8X10.6 Maximum Number Of Floors: 4 Street network: Along Perimeter

Fig.110. Massing, Case 3 - a

Fig.111. Massing, Case 3 - b

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Fig.112. Layout, Case 3

FSI = 0.93

Fig.116. Built: Private: Open Space, Case 3 -a

Built: Private Open Space = 0.95

Community Housing in the Indian Context

Fig.114. Street Network and Common Open Fig.115. View to Common Open Space, Case spaces, Case 3 4 -a

BUILT:OPEN =49:51

Fig.117. Sunlight Hours, Case 3-a

Average Sunlight Hours=4 Hrs

Average View to Common Open Space: 42.55%

Fig.113. Sunlight Radiation, Case 3 -a

Average Radiation= 638.00 kWh/M2

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Fig.118. Massing. Case 3 -c

BUILT:OPEN =49:51 FSI = 0.93

Fig.119. Built: Private: Open Space, Case 3 -a

Built: Private Open Space = 0.98 Community Housing in the Indian Context

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Fig.120. Sunlight Hours, Case 3 -a

Average Sunlight Hours=4 Hrs

Fig.121. Sunlight Radiation, Case 3 -a

Average Radiation= 638.00 kWhr/M2 Community Housing in the Indian Context

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Fig.122. View to Common Open Space, Case 3 -a

Average View to Common Open Space: 42.55%

Fig.123. Sky View from Private Open Spaces , Case 3 -a

Average Sky View: 40.76% Community Housing in the Indian Context

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4. Case 4 Input: Unit Size : 8x4.5 M Plot Size: 8X8, 8X10.6 Maximum Number Of Floors:5 Street network: Along Perimeter

Fig.124. Massing, Case 4 - a

Fig.125. Massing, Case 4 - b

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Fig.126. Layout, Case 4

FSI = 0.90

Fig.130. Built: Private: Open Space, Case 4 -a

Built: Private Open Space = 0.95

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Fig.128. Street Network and Common Open Fig.129. View to Common Open Space, Case spaces, Case 4 -a 4 -a

BUILT:OPEN =49:51

Fig.131. Sunlight Hours, Case 4-a

Average Sunlight Hours=5.3 Hrs

Average View to Common Open Space: 48.46%

Fig.127. Sunlight Radiation, Case 4 -a

Average Radiation= 830.56 kWh/M2

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Fig.132. Massing, Case 4 - c

BUILT:OPEN =49:51 FSI = 0.90

Fig.133. Built: Private: Open Space, Case 4 -b

Built: Private Open Space = 0.95 Community Housing in the Indian Context

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Fig.134. Sunlight Hours, Case 4 -b

Average Sunlight Hours=5.3 Hrs

Fig.135. Sunlight Radiation, Case 4 -b

Average Radiation= 830.56 kWh/M2 Community Housing in the Indian Context

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Fig.136. View to Common Open Space, Case 4 -b

Average View to Common Open Space: 48.46%

Fig.137. Sky View from Private Open Spaces, Case 4 -b

Average Sky View: 32.99% Community Housing in the Indian Context

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5. Case 5 Input: Unit Size : 6x4.5 M Plot Size: 6X6, 6X8 Maximum Number Of Floors:5 Street network: Along Perimeter and Through The Mid

Fig.138. Massing, Case 5 - a

Fig.139. Massing, Case 5 - b

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Fig.140. Layout, Case 5

FSI = 1.51

Fig.144. Built: Private: Open Space, Case 5 -a

Built: Private Open Space = 1.18

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Fig.142. Street Network, Case 5

BUILT:OPEN =62:38

Fig.145. Sunlight Hours, Case 5-a

Average Sunlight Hours=3.12 Hrs

Fig.143. Built: Private: Open Space, Case 5 -a

Average View to Common Open Space: 24.4%

Fig.141. Sunlight Radiation, Case 5 -a

Average Radiation= 555.41 kWh/M2

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Fig.146. Massing Case 5 -c

BUILT:OPEN =62:38 FSI = 1.51

Fig.147. Built: Private: Open Space, Case 5-b

Built: Private Open Space = 1.18 Community Housing in the Indian Context

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Fig.148. Sunlight Hours, Case 5 -b

Average Sunlight Hours=3.12 Hrs

Fig.149. Sunlight Radiation, Case 5 -b

Average Radiation= 555.41kWh/M2 Community Housing in the Indian Context

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Fig.150. View to Common Open Space, Case 5 -b

Average View to Common Open Space: 24.4%

Fig.151. Sky View, Case 5 -b

Average Sky View: 5.05% Community Housing in the Indian Context

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7. Case Comparison CASE 1 Unit Size Maximum No of Floors

6x3.5 4

CASE 2

CASE 3

CASE 4

CASE 5

6x3.5

8x4.5

8x4.5

6x3.5

4

4

5

5

Layout

FSI Density No. Of Houses

0.79 878/Ha 176

0.93 10319/Ha 206

0.90 1000/Ha 200

1.05 1171/Ha 234

1.51 1678/Ha 336

41:49

47:43

48:42

48:42

62:38

43.67%

41.75%

42.55%

48.46%

24.40%

Street Network and Common Open Spaces

Built:Open

View to Common Open Space

Fig.152. Case Comparison 1

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CASE 1

CASE 2

CASE 3

CASE 4

CASE 5

32.99%

5.05%

Sky View

33.89%

35.54%

40.76%

Built Space:Private Open Space

0.98

0.98

0.95

0.95

1.18

Sunlight Hours (Hrs) 6.03

5.50

951.63

863.42

4.00

5.34

638.01

830.56

3.12

Radiation kWh/M2 555.41

Fig.153. Case Comparison 2

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Original Study

Case 1

Case 2

Case 3

Case 4

Case 5

6x3.5

6x3.5

6x3.5

8x4.5

8x4.5

6x3.5

2

4

4

4

5

5

Density

500/Ha

878/Ha

10319/Ha

1000/Ha

1171/Ha

1678/Ha

No. Of Houses

100/Ha

176/Ha

206/Ha

200/Ha

234/Ha

336/Ha

View to Open Space

42.97%

43.67%

41.75%

42.55%

48.46%

24.40%

Sky View

50.31%

33.89%

35.54%

40.76%

32.99%

5.05%

1:3

0.98

0.98

0.95

0.95

1.18

5.9

6.03

5.50

4.00

5.34

3.12

1232.00

951.63

863.42

638.00

830.56

555.41

Unit Size Maximum No. of Floors

Built :Private Open Space Sunlight Hours (Hrs) Radiation (kWh)

Optimal Case for a particular parameter Fig.154. Case Comparison 3

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1. Unit Size For the same number of floor, a smaller unit size enables more density. However, as seen it comes at the cost of the views to the common open spaces and the amount of sky visible as is drastically visible in Case 5.

2. Maximum No. of Floors The entire site in the generated cases has varied number of floors. This gives a dynamic character to the site and creates different kinds of interactions when these come together. Optimization allows the forms with higher number of floors also provide shade to the lower ones. This decreases the Sunlight Radiation drastically.

3. Density Through optimization, we are able to achieve more density without a fair compromise on Sunlight Hours (Optimal 6Hrs) , and Views to Common Open Spaces. Although as the density increases, a sharp decrease is seen in the amount of sky visible from private open spaces.

4. Built : Private Open Space The generated cases , except case 6, have a lower ratio .In the case of affordable housing where built space in already , this ratio can be seen as an opportunity for activities to flourish and build the community. People adapt to these open spaces in a diverse way and it reflects their lifestyles. Inversely, it can also be seen as an scope of further development.

5. Sky View Staggering the terraces formed increases the sky view. Although, this means not all of these can face the open space and interaction to is compromised. However, interaction with other open spaces is possible. As sky view increases, views to the common space decreases. This can be viewed as a design decision an architect can make on the requirements and intent of the community that is to be residing there.

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8. Conclusion The study demonstrates a comprehensive approach to generative design processes. The methodology is not limited to inputs and outputs. Each step is an informed decision of the designer governing the process and various such decisions need to be taken before we come to the input. The generated outcomes are the result of the skill, experience and intuition of the designer, the computer being just an aid.

The process is a way to analyze a number of iterations efficiently. It would be almost impossible to do so manually. The idea here is to not completely replace conventional design methods but to integrate these new design processes in them but to aim for a more a better understanding of the final outcome. The initial methodology for analyzing case studies could still be the same in conventional design methods.

Once a system is established, it can be repeated on various sites with different constraints. Each design is unique to that constraint and that site. One does not always need to start from scratch. Alternately, the system itself can be altered to create new results. Thus, the case of affordable housing taken here proves to be of great potential for generative design.

The interactive nature of Biomorpher aids the designer to be more in control with the process. Reflecting the thinking process of a designer could lead to more people accepting and using this design process.

As summarized by the generated cases, the emphasis here becomes to understand the effect of each parameter on the final outcome. The relationship between parameters is also highlighted. This is becomes crucial in decision making. The designer can decide what fitness criteria to trade off for which one.

One can also start relating environmental factors that are quantitative to the qualitative nature of space after analyzing generated outputs. As we generate more iterations, not only do we achieve valuable design exploration but also several learning outcomes. The learnings from initial outcomes can then be used to set new goals for the next iteration. This cyclic process can continue till a "desirable" and "fit" generation of outcomes is achieved. In a conventional method, this process would be slow and cumbersome.

However, The process has its limitations. The argument of generative design is to have hundreds of iterations to choose from. Thus, the aim is to have a variety of outputs to maximize design exploration.

At the same time, the other argument is to have optimum results - the basis on which iterations are generated. One might find it difficult to balance between the two. Varied outputs tend to also vary in fitness criteria.

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9. Way Forward For the case of community housing, several other factors can be considered based on the intent of the designer.

Incrementality is an integral aspect of affordable housing. One builds as and when they can afford to.

The next step in this process would be to consider the same aspects of optimization at different stages of incrementality.

The same process with different principles from different case studies can yield interesting results.

This would further break the repetitive nature of architecture in community housing designs. A design palette, much like a physical kit of parts can be envisioned, A similar process can be scaled down to the unit size. One can generate optimal built forms considering the internal layouts of units. A more cultural context can be analyzed by taking into account the materials used to build. It would give a detailed analysis of thermal comfort in the interior spaces as well.

One can have different concerns for community housing other than affordability. The process would still be the same, the principles and optimization strategy would change.

The process can also be scaled up to an urban level. Nodes, streets, walkability, amenities, landmarks would now be considered as parameters. Pedestrian and vehicular movement can also be simulated to predict the future of built cities.

Most of our time of our urban live is spent at workplaces. In order to investigate into healthier lifestyles, one needs to investigate into the nature of workplaces. Here, much like community housing, density is the reason the quality of spaces are compromised. Generative design can help achieve an optimal solution.

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10. List Of Figures Figures, unless specified otherwise, are generated by the author.

Pg 2

Fig.1. Example of Iterations from Generative Design Processes

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Fig.2. [ Untitled Photograph of Aranya Housing by B.V Doshi] https://www.sangath.org/projects/aranya-lowcost-housing-indore/

10

Fig.3. Bojan Tepavcevic and Vesna Stojaković ( 212) ,Various (hypothetical) views of the ancient Pompeii, based on real building footprints modelled with CGA shape grammars from Facta universitatis - series Architecture and Civil Engineering

12

Fig.4. Structure of Methodology

13

Fig.5. Strategy Of Analysis

13

Fig.6. Scale Of Analysis

14

Fig.7. Strategy Of Algorithm

16

Fig.8. A case of Slum Rehabilitation in Mumbai

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Fig.9. [ Untitled Photograph of Belapur Housing] http://www.charlescorrea.net/projects/belapur -a

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Fig.10. [ Untitled Photograph of Belapur Housing] http://www.charlescorrea.net/projects/belapur -b

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Fig.11. Cluster, Belapur Housing

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Fig.12. Section Through Cluster, Belapur Housin - a

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Fig.13. Section Through Cluster Belapur Housing - b

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Fig.14. Elevation, Belapur Housing - a

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Fig.15. Elevation, Belapur Housing - b

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Fig.16. [ Untitled Image of CIDKO Housing, Belapur] http://www.rajrewal.in/projects/housing-cidco.htm -a

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Fig.17. [ Untitled Image of CIDKO Housing, Belapur] http://www.rajrewal.in/projects/housing-cidco.htm -b

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Fig.18. Elevation, CIDKO Housing -a

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Fig.19. Elevation, CIDKO Housing -b

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Fig.20. CIDKO Housing

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Fig.21. Section, CIDKO Housing

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Fig.22. Corridor Space in Haji Kasam Chawl (2007) CRIT

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Fig.23. [ Untitled Image of Haji Kasam Chawl ] https://www.dnaindia.com/topic/haji-kasam-chawlChawl - b

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Fig.24. Haji Kasam Chawl

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Fig.25. Common Space, Haji Kasam Chawl

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Fig.26. Elevation, Haji Kasam Chawl

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Fig.27. Section, Haji Kasam Chawl -a

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Fig.28. Section, Haji Kasam Chawl -b

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Fig.29. [Untitled Rendering of Udaan Low- Cost Housing] https://architecturelive.in/udaan-low-cost-mass-housingproject-at-mumbai-by-sameep-padora-and-associates

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Fig.30. Udaan

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Fig.31. Internal Streets, Udaan - a

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Fig.32. Internal Streets Udaan - b

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Fig.33. Elevation , Udaan

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Fig.34. Section, Udaan

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Fig.35. Case Studies - Quantitative Analysis

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Fig.36. Case Study Analysis - 1

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Fig.37. Case Study Analysis - 2

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Fig.38. Case Study Analysis -3

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Fig.39. Case Study Analysis -4

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Fig.40. Pattern Of Repetition

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Fig.41. Ratio of Hierarchy of Open Spaces

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Fig.42. Vertical Repetition

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Fig.43. Relationship of Private Open Spaces to Common Open Spaces

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Fig.44. Logic Of Street Network

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Fig.45. Linear Evolutionary design process

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Fig.46. Non- Linear Evolutionary design process

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Fig.47. Clusters in Biomorpher

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Fig.48. Generations In Biomorpher

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Fig.49. Interface , Biomorpher

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Fig.50. Modular Approach To Algorithm

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Fig.51. Site Geometry

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Fig.52. Cluster Pattern - Belapur Housing

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Fig.53. Cluster Repetition -Belapur Housing

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Fig.54. Pattern On Plot

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Fig.55. Trees On Plot before and after removing Trees

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Fig.56. Types of Street Network

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Fig.57. Final Plot Configuration

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Fig.58. Vertical Repetition

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Fig.59. Massing

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Fig.60. Massing

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Fig.61. Manually Added Street Networks

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Fig.62. Removing Points to Alter Street Network

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Fig.63. Altering Entry Points and Type of Street Network

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Fig.64. Other Variations

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Fig.65. Built Space and Private Open Space

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Fig.66. Massing

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Fig.67. Sunlight Radiation Analysis

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Fig.68. Sunlight Hour Analysis

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Fig.69. View Analysis

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Fig.70. Sky View Analysis 3D Projection(left) and Plan (right)

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Fig.71. Built :Private Open Space, Belapur Housing

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Fig.72. View to Common Open Space, Belapur Housing

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Fig.73. Sky View, Belapur Housing

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Fig.74. Sunlight Hours, Belapur Housing

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Fig.75. Sunlight Radiation, Belapur Housing

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Fig.76. Built :Private Open Space, Belapur Housing

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Fig.77. View to Common Open Space, Belapur Housing

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Fig.78. Sky View, Belapur Housing

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Fig.79. Sunlight Hours, Belapur Housing

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Fig.80. Sunlight Radiation, Belapur Housing

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Fig.81. Generation 1, Case 1

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Fig.82. Massing , Case 1 - a

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Fig.83. Massing, Case 1 - b

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Fig.84. Layout, Case 1

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Fig.85. Street Network and Common Open spaces, Case 1-a

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Fig.86. View to Common Open Space, Case 1 -a

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Fig.87. Built: Private: Open Space,

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Fig.88. Sunlight Hours, Case 1 -a

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Fig.89. Sunlight Radiation, Case 1 -a

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Fig.90. Massing, Case 1 - c

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Fig.91. Built: Private: Open Space, Case 1 -b

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Fig.92. Sunlight Hours, Case 1 -b

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Fig.93. Sunlight Radiation, Case 1 -b

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Fig.94. View to Common Open Space, Case 1 -b

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Fig.95. Sky View, Case 1 -b

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Fig.96. Massing , Case 2 - a

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Fig.97. Massing, Case 2 - b

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Fig.98. Layout, Case 2

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Fig.99. Street Network and Common Open spaces, Case 2-a

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Fig.100. View to Common Open Space, Case 2 -a

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Fig.101. Built: Private: Open Space,

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Fig.102. Sunlight Hours, Case 2 -a

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Fig.103. Sunlight Radiation, Case 2 -a

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Fig.104. Massing, Case 2 - c

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Fig.105. Built: Private: Open Space, Case2 -b

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Fig.106. Sunlight Hours, Case 2 -b

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Fig.107. Sunlight Radiation, Case 2 -b

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Fig.108. View to Common Open Space, Case 2 -b

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Fig.109. Sky View, Case 2 -b

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Fig.110. Massing , Case 3 - a

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Fig.111. Massing, Case 3 - b

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Fig.112. Layout, Case 3

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Fig.113. Street Network and Common Open spaces, Case 3-a

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Fig.114. View to Common Open Space, Case 3 -a

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Fig.115. Built: Private: Open Space,

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Fig.116. Sunlight Hours, Case 3 -a

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Fig.117. Sunlight Radiation, Case 3 -a

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Fig.118. Massing, Case 3 - c

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Fig.119. Built: Private: Open Space, Case 3 -b

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Fig.120. Sunlight Hours, Case 3 -b

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Fig.121. Sunlight Radiation, Case 3 -b

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Fig.122. View to Common Open Space, Case 3 -b

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Fig.123. Sky View, Case 3 -b

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Fig.124. Massing , Case 4 - a

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Fig.125. Massing, Case 4 - b

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Fig.126. Layout, Case 4

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Fig.127. Street Network and Common Open spaces, Case 4-a

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Fig.128. View to Common Open Space, Case 4 -a

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Fig.129. Built: Private: Open Space,

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Fig.130. Sunlight Hours, Case 4 -a

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Fig.131. Sunlight Radiation, Case 4 -a

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Fig.132. Massing, Case 4 - c

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Fig.133. Built: Private: Open Space, Case 4 -b

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Fig.134. Sunlight Hours, Case 4 -b

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Fig.135. Sunlight Radiation, Case 4 -b

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Fig.136. View to Common Open Space, Case 4 -b

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Fig.137. Sky View, Case 4 -b

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Fig.138. Massing , Case 5 - a

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Fig.139. Massing, Case 5 - b

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Fig.140. Layout, Case 5

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Fig.141. Street Network and Common Open spaces, Case 5-a

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Fig.142. View to Common Open Space, Case 5 -a

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Fig.143. Built: Private: Open Space,

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Fig.144. Sunlight Hours, Case 5 -a

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Fig.145. Sunlight Radiation, Case 5 -a

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Fig.146. Massing, Case 5 - c

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Fig.147. Built: Private: Open Space, Case 5 -b

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Fig.148. Sunlight Hours, Case 5 -b

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Fig.149. Sunlight Radiation, Case 5 -b

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Fig.150. View to Common Open Space, Case 5 -b

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Fig.151. Sky View, Case 5 -b

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Fig.152. Case Comparison 1

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Fig.153. Case Comparison 2

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Fig.154. Case Comparison 3

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Case 4 -a

Case 5 -a

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11. References Barenstein, J. D., & Iyengar, S. (2010). India: From a culture of housing to a philosophy of reconstruction. Building Back Better, 163.

Vahanvati, M., & Mulligan, M. (2017). A new model for effective post-disaster housing reconstruction: Lessons from Gujarat and Bihar in India. International Journal of Project Management, 35(5), 802-817.

Barenstein, J. E. D. (2015). Continuity and change in housing and settlement patterns in post-earthquake Gujarat, India. International Journal of Disaster Resilience in the Built Environment.

Mehrotra, R., Shetty, P., & Gupte, R. (2009). Architecture and contemporary indian identity. Constructing identity in contemporary architecture: Case studies from the South, 12, 199.

Botz-Bornstein, T. (2016). Transcultural architecture: The limits and opportunities of critical regionalism. Routledge.

Gangwar, A. G., & Kaur, P. Charles Correa: Seeking new Identity of Indian Architecture through “Critical Regionalism”.

Mehrotra, R. (2011). Architecture in India Since 1990 Kenney, S. F. (1994). Cultural influences on architecture (Doctoral dissertation, Texas Tech University).

Frazer, J. (1995). An evolutionary architecture Heidari, A., Sahebzadeh, S., Sadeghfar, M., Taghvaei, B. E., Adedayo, O. F., Oyetola, S. A., ... & Adebayo, O. A. (2018).

PARAMETRIC ARCHITECTURE IN IT’S SECOND PHASE OF EVOLUTION. Journal of Building Performance ISSN, 9(1), 2018.

Moretti, L., Bucci, F., & Mulazzani, M. (2002). Luigi Moretti: works and writings. Princeton Architectural Press.

Juchnevic, K., Juchnevic, R., Radziszewski, K., & Marcinowska, E. (2015). Architektura Parametryczna/Parametric Architecture. Archivolta, (4 (68), 66-74.

Erdine, E., & Kallegias, A. (2017). Interwoven reinforced concrete structures: Integration of design and fabrication drivers through parametric design processes. Design Studies, 52, 198-220.