Parametric Architecture: Implementation of Algorithmic Design Process

PARAMETRIC ARCHITECTURE Implementation of Algorithmic Design Process

PARAMETRIC ARCHITECTURE: IMPLEMENTATION OF ALGORITHMIC DESIGN PROCESS

Submitted By Sanath Thomas Samuel Guide Asst Prof Jayaprakash V N B. ARCH DISSERTATION

December 2020

C.A.T

COLLEGE OF ARCHITECTURE TRIVANDRUM Mulayara P.O, Thiruvananthapuram This report is the property of the institution and the author. It should not be reproduced without prior permission

C.A.T

COLLEGE OF ARCHITECTURE TRIVANDRUM Mulayara P.O, Thiruvananthapuram Sanath Thomas Samuel

B.Arch. Dissertation

PARAMETRIC ARCHITECTURE: IMPLEMENTATION OF ALGORITHMIC DESIGN PROCESS

Approval The following study is hereby approved as a creditable work on the subject, carried out and presented in a manner, sufficiently satisfactory to warrant its acceptance as B.Arch Dissertation, a pre-requisite to the B.Arch Degree program for which it has been submitted.

It is to be understood that by this approval the undersigned do not necessarily endorse or approve the statements made, opinions expressed or conclusions drawn therein, but approve the study only for the purpose for which it has been submitted and satisfies as to the requirement laid down in the academic programme.

Dissertation Guide

Dissertation Coordinator

Jayaprakash V N

Reshmi Ravindran

Bijey Narayan

Associate Professor

Associate Professor

Professor

Date:

Date:

Date:

External Invigilator

HOD (Architecture)

Internal Invigilator

C.A.T

COLLEGE OF ARCHITECTURE TRIVANDRUM Mulayara P.O, Thiruvananthapuram

CERTIFICATE

This is to certify that Mr Sanath Thomas Samuel has worked under my supervision on PARAMETRIC ARCHITECTURE: IMPLEMENTATION OF ALGORITHMIC DESIGN PROCESS towards the partial fulfilment of the requirement for the award of the

degree of Bachelor of Architecture under the A.P.J Abdul Kalam Technological University, Kerala. This is his/her original work and can be submitted as a B.Arch. Dissertation.

Guide

Jayaprakash V N Associate Professor Date:

DECLARATION

I hereby declare that the Dissertation titled “PARAMETRIC ARCHITECTURE: IMPLEMENTATION OF ALGORITHMIC DESIGN PROCESS” was carried out by me during the year 2020 in partial fulfilment of the requirement for the award of the degree of Bachelor of Architecture under the A.P.J Abdul Kalam Technological University, Kerala. This dissertation is my own effort and has not been submitted to any other University.

Thiruvananthapuram December 31, 2020

Sanath Thomas Samuel

ACKNOWLEDGEMENT Firstly, I’d like to express my thanks to my patient and supportive supervisor, Asst Prof Jayaprakash V N, who has supported me throughout this research project. I would like to express my gratitude towards him for giving me freedom to explode and generate my own work. I am extremely grateful for to him for being available and open to for meetings any time and on any day. I’d also like to thank the dissertation coordinator for 2020, Prof Reshmi Ravindran. Thank you for providing invaluable feedback on my research and the study report.

I would also like to thank Mr Clifford for his support along with my parents. Finally, I would like to express my gratitude towards my review panel members, Prof Jerry and Prof Abhijath for their time and valuable feedback.

ABSTRACT

In the age of modern technology, an increasing number of architects are exploring means to create efficient, economical, eco-friendly and modern designs. Algorithms have the ability to save architects, designers and planners’ time. Modern technology allows one to connect the human “logic” with the efficiency and speed of computers to create logical systems that can do a large amount of logical thinking in a short period of time.

Creating an algorithm which accepts control variables as user inputs and produces a resultant form which meats the restrictions set by the user the best. The resultant form is tested for efficiency in ladybug climatic analysis software. The study determines the efficiency of an architectural algorithm developed in grasshopper.

Keywords: Algorithmic design, parametric design, logical programming, formfinding

TABLE OF CONTENTS 1

2

3

INTRODUCTION ....................................................................................................................................... 1 1.1

NEED FOR THE STUDY ............................................................................................................................... 1

1.2

AIM ........................................................................................................................................................... 2

1.3

OBJECTIVES .............................................................................................................................................. 2

1.4

METHODOLOGY ........................................................................................................................................ 2

1.5

SCOPE ....................................................................................................................................................... 2

1.6

LIMITATION .............................................................................................................................................. 3

COMPUTER-AIDED DESIGN ................................................................................................................. 4 2.1

INTRODUCTION TO PARAMETRIC MODELLING SYSTEMS ............................................................................ 4

2.2

FORM FINDING .......................................................................................................................................... 7

2.3

ALGORITHMIC/ GENERATIVE DESIGN ........................................................................................................ 8

EVOLUTIONARY SYSTEMS ................................................................................................................ 10 3.1

WHAT ARE EVOLUTIONARY SOLVERS? .................................................................................................... 10

3.2

PROCESSES .............................................................................................................................................. 10

4

PROCESS .................................................................................................................................................. 12

5

FORMULATED ALGORITHM ............................................................................................................. 13

6

7

5.1

PARTS OF THE SYSTEM AND USER INPUTS ................................................................................................ 13

5.2

PROCESSES .............................................................................................................................................. 15

5.3

GALAPAGOS EVOLUTIONARY SOLVER ..................................................................................................... 24

5.4

FORMULATED ALGORITHM FOR CIRCLE, IN GRASSHOPPER INTERFACE.................................................... 27

5.5

FORMULATED ALGORITHM FOR SQUARE FENESTRATIONS IN GRASSHOPPER INTERFACE ......................... 29

CONTROL VARIABLES ........................................................................................................................ 31 6.1

VARIABLES ............................................................................................................................................. 31

6.2

PROCESS ................................................................................................................................................. 31

6.3

TESTING CONDITIONS. ............................................................................................................................. 32

PERFORMANCE ANALYSIS ................................................................................................................ 33 7.1

CASE A ................................................................................................................................................... 33

7.2

CASE B.................................................................................................................................................... 35

7.3

CASE C.................................................................................................................................................... 37

7.4

ANALYSIS ............................................................................................................................................... 39

8

CONCLUSION.......................................................................................................................................... 40

9

REFERENCES .......................................................................................................................................... 41

10

APPENDIX A ............................................................................................................................................ 42

LIST OF FIGURES i.

Figure 1:An upside-down force model of the Sagrada Familia, Source: Sagrada Familia Museum ............................................................................................................ 5

ii.

Figure 2:Sutherland's Sketchpad: (a) Ivan Sutherland working with Sketchpad; (b) A screenshot of Sketchpad drawing of a circle. Source: For an Archeology of the Digital Iconography - Scientific Figure on ResearchGate. ........................................................ 6

iii.

Figure 3Figure 6: Frank Gehry's Guggenheim Bilbao was one of the first buildings to use mathematical algorithms to shape individual panelized elements. Source: Picturealliance/dpa .................................................................................................................... 7

iv.

Figure 4: User Interface of grasshopper, with preview in rhino. source: Author .......... 9

v.

Figure 5:Activity diagram of a typical algorithmic design process. Source: Interactivearchitecturelab.org ...................................................................................... 11

vi.

Figure 6: Algorithmic process expressed as a flow diagram. Source: Author ............. 12

vii.

Figure 7Algorithmic process as expressed in grasshopper user interface. Source: Author .......................................................................................................................... 27

viii.

Figure 8Figure 7Algorithmic process as expressed in grasshopper user interface.

Source: Author ............................................................................................................. 28

ix.

Figure 9Algorithmic process as expressed in grasshopper user interface. Source: Author .......................................................................................................................... 29

x.

Figure 10Figure 9Algorithmic process as expressed in grasshopper user interface.

Source: Author ............................................................................................................. 30

xi.

Figure 11: Process for generated algorithm. Source: Author ...................................... 31

xii.

Figure 12: Ladybug thermal analysis of a room with the facade generated by grasshopper Source: Author ......................................................................................... 33

xiii.

Figure 13: Corresponding test result with a generic opening ...................................... 33

xiv.

Figure 14 generated form with variables applicable to case A. Source: Author ......... 34

xv.

Figure 15: Ladybug thermal analysis of a room with the facade generated by grasshopper Source: Author ......................................................................................... 35

xvi.

Figure 16: Corresponding test result with a generic opening ...................................... 35

xvii.

Figure 17generated form with variables applicable to case B. Source: Author........... 36

xviii.

Figure 18: Ladybug thermal analysis of a room with the facade generated by grasshopper Source: Author ......................................................................................... 37

xix.

Figure 19: Corresponding test result with a generic opening ...................................... 37

xx.

Figure 20generated form with variables applicable to case C. Source: Author........... 38

LIST OF TABLES

xxi.

Table 1: Testing parameters for grasshopper ............................................................... 32

xxii.

Table 2: Climatic considerations to test in ladybug ..................................................... 32

PARAMETRIC ARCHITECTURE: IMPLEMENTATION OF ALGORITHMIC DESIGN PROCESS

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1

INTRODUCTION

In the search for appropriate form for the buildings, architects have always been trying to treat the appearance of their work as a personal signature. (Burry, 2003). The era of using manual methods to formulate 3d forms, has come to an end. Several 3d software programs has been developed to aid the designer, not only during the final presentation stages but also the initial design and form finding1 stage. This is required as the human mind conceive three dimensional forms with great difficulty, unlike the ease at with it frames two dimensional forms.

Visual programming2 languages such as dynamo and grasshopper can save designers and planners large amounts of time during the initial design process. AI can help architects, designers and planners to effectively and quickly create mock-ups of possible iterations of a project. Which can be analysed on efficiency calculators such as Lady Bug and Honey bee3, creating faster work processes and providing the tools for architects and designers to design with higher levels of efficiently. Excluding the above uses for algorithmic processes in design, Future work will include the development of automated tools for conceptual designs with parametric components including energy, material and signal flow concepts. (M. Campbell, 1998).

1.1

Need for the study Algorithmic process has the ability to save designers time in the design process, yet

these systems are not exploited. A lack of exposure to these tools in architecture education hinder the ability for future architects to utilize the power of algorithmic design.

Form finding, in architecture, is a process or method used to generate a geometrical mass of a space.

Is a type of programming language that lets users describe processes through illustrations.

3 Ladybug and HoneyBee are plugins for grasshopper that enable thermal and climatic performance analysis.

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Aim To explore the potential of algorithmic design, using grasshopper and galapagos4 by

designing an algorithm and testing its results.

1.3

1.4

Objectives •

To understand the history of computer aided design.

•

To Design an algorithm to create a façade with given parameters.

•

To Understand the potential of generative design, through Galapagos.

Methodology a. Create an algorithm which designs an efficient façade with given parameters such as; the size of façade, percentage of openings, number of openings and length and directions of openings. b. Use evolutionary solvers such as Galapagos to find the most efficient iteration of the design c. Climatic efficiency calculators, such as ladybug, to be used to calculate the climatic conditions of the design developed by the evolutionary solvers.

1.5

Scope Algorithmic process in design can increase efficiency in the design process by reducing

the time spent by architects on form generation. The algorithm designed in this study can be used to generate façades and can be further adapted to specific user requirements. The use of computer aided design5 can create multiple iterations of a project in a very short period of time, so that testing and analysis can be performed with greater number of iterations.

4 5

A genetic programming plug in used in grasshopper for rhino.

Computer aided design is used by engineers and designers to aid them to visualize their ideas

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Limitation Due to knowledge required in creating and adapting visual programming6 languages,

such as grasshopper, algorithmic design may not be accessible to all designers. Although algorithmic design provides the users with new tools for design, it does diminish the human generated quality in design.

6

Is a type of programming language that lets users describe processes through illustrations.

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2 2.1

COMPUTER-AIDED DESIGN

Introduction to parametric modelling systems The adjective “parametric” refers to “parameter”, which originates from the Greek

‘para’, meaning a subsidiary or assistant, and the word “metron”, which means “measure”7.

The word “Parametric” in its literal terms is used in the field of statistics, referring to the assumption that in a sample data that is derived from a population which can be modelled by a probability distribution which has a fixed set of parameters. Because of the modular properties of parametric design, it allows designers to reuse elements and parts in many different projects.

Parametric modelling systems can be classified into two distinct groups •

Propagation based systems

•

Constraint systems

Form-finding is implemented with principles of propagation.

2.1.1

History of parametric design in architecture.

Although the origins of parametric design date back to not more than 15 years ago, the

process by which parametric design is based upon dates back to when the first attempts of design by computer simulation began. Computers can provide two grounds of exploration, theoretical and practical. Software programs have been developed in both grounds which enable designers with tools to expand their boundary of exploration.

Analogue8 parametric design Some of the oldest examples of analogue parametric systems are the church models by Antonio Gaudi. He used strings with weights on them to create shapes which were not

Definition from Oxford university, Oxford Dictionary of English (Oxford: Oxford University Press, 2010). relating to or using signals or information represented by a continuously variable physical quantity such as spatial position

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C.A.T conceivable by the technology of his time. By adjusting the lengths of the strings and/or the birdshot weights he could change the shape of the resulting arcs.

Figure 1:An upside-down force model of the Sagrada Familia, Source: Sagrada Familia Museum

Gaudi’s process, although allowed for faster calculations than by hand, it still lacked the ability to quickly edit and experiment with parameters, this problem was solved by Ivan Sutherland9, who created sketchpad10, a program he developed as a part of his PhD thesis. We live in a physical world whose properties we have come to know well through long familiarity.

We sense an involvement with this physical world which gives us the ability to predict its properties well. For example, we can predict where objects will fall, how well-known shapes look from other angles, and how much force is required to push objects against friction.

(Sutherland I. E., 1965). This human “condition” restricts our capability to imagine three-

9

American computer scientist, known for his work on computer graphics.

Considered to be the ancestor of modern computer aided design

10

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C.A.T dimensional forms without the help of an external accompaniment. Refer to appendix A for an extract from Ivan Sutherlands PhD thesis technical report.

Figure 2:Sutherland's Sketchpad: (a) Ivan Sutherland working with Sketchpad; (b) A screenshot of Sketchpad drawing of a circle. Source: For an Archeology of the Digital Iconography - Scientific Figure on ResearchGate.

2.1.2

Parametric design in modern society With the advent of algorithmic scripting in architecture, designers were able to design,

create and formulate panels to be used in construction. This led to the possible collaboration of computers and humans in a project. Although current technology does not allow the ability for computers to comprehend aesthetics, computers can help the designer reach the solution faster and more efficiently.

Greg Lynn was the first architect to use a computer-aided design in construction. His “blobitecture11” was the first examples of computer-generated forms. Blobitecture, from blob architecture, also known as blobism and blobismus12 are words used to describe buildings which have an organic, amoeba-shaped building form (Curl, 2006).

11 12

Blobitecture: A category of architectural design style.

A Different way to refer to Blobitecture.

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Figure 3Figure 6: Frank Gehry's Guggenheim Bilbao was one of the first buildings to use mathematical algorithms to shape individual panelized elements. Source: Picture-alliance/dpa

2.2

Form finding During the past two decades, architecture has evolved in its ability to provide its

designers with the freedom to create structures which were otherwise impossible to conceive without the development of a computer-aided design. This has led to the formation of distinct structures that have unique structures and approaches in construction. Free-flowing structures will always have a role in architecture because of the ability to create eye-catching forms as landmarks, providing freedom for design exploration, ranging from initial conception to final fabrication, and dealing with applied geometry, computational design, and practice of case studies (Adriaenssens, 2014) Computational models produce free-forms, shifting from form-making to form-finding (Agkathidis, 2015). Physical models follow a trial-and-error manner, based on experiences and intuition with the point of material minimization and stiffness maximization. Computational models produce free forms, forwarded towards topological differentiation with directly controlled parameters to find an optimal geometry with minimized material or maximized stiffness of the structure. (Hameed, 2020) Dissertation 2020

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C.A.T 2.3

Algorithmic/ Generative design Algorithmic Design allows for the modelling of highly complex geometries that would

pose some challenges for manual design tools. Algorithmic Design entails a parametric modelling philosophy, meaning the design can be manipulated through variable parameters.

This allows the designer to explore a wider range of possibilities rapidly and with little effort.

The degree of control over modelling systems offered by programming enables the normal tool-user to become a tool-maker. (algorithmicdesign, 2020) 2.3.1

Software used in algorithmic design

2.3.1.1 Luna Moth Luna Moth is a HTML5/JavaScript-based web application that harnesses the performance and graphical capabilities available in modern web browsers, complemented with a companion application for allowing models to be exported to traditional CAD applications.

Using this, architects can write their programs and visualize the results without being chained to a particular computer, and can easily integrate results into their normal workflow. (algorithmicdesign, 2020)

2.3.1.2 Khepri Khepri is an algorithmic design tool based on the idea that a single algorithmic description can be used to generate equivalent models in CAD, BIM, analysis, and game applications. It is a direct descendant of Rosetta with considerable improvements in performance, and user interaction capabilities. Currently, it is implemented using the Julia programming language, as it provides a smooth learning curve, fast execution of computationally-intensive algorithms, and the capacity for large-scale development. (algorithmicdesign, 2020)

2.3.1.3 Grasshopper Grasshopper is a visual programming language, which acts as a plugin for popular computer-aided modelling software, rhino. The program was created by David Rutten at Robert McNeel & Associates. Programs are created by dragging components onto a canvas.

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C.A.T Grasshopper allows for real time preview and its integration makes it a powerful tool in architectural design.

Figure 4: User Interface of grasshopper, with preview in rhino. source: Author

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C.A.T

3 3.1

EVOLUTIONARY SYSTEMS

What are evolutionary solvers?

A human conducted design process is defined in literature as a set of technical activities

within a product development process which often include the development of new concepts, embodiment and often other non-managerial activities (Otto, 1999). From this, the definition of evolutionary design immediately follows a computational process. This is because large sums of data have to be tested and checked for fitness or “correctness”. This process would take months, if not years, to be done by a human.

Evolutionary solvers mimic the evolutionary process that happens in the world around us, all living organisms are always evolving and changing, the successful evolutions are the ones which are best suited for survival. “in the natural world the optimization itself is embodied in the core of the evolution because the natural organisms are in a fact by optimization improving over time”. They have been used in many disciples such as computer-aided design, and industrial design.

3.2

Processes Evolutionary solvers or genetic solvers work with the principals of natural selection

and Darwin’s theory of evolution13. Algorithm is developed primarily for problem-solving and optimisation in situations where it is possible to state clearly both the problems and the criteria to be fulfilled for their successful solution (Frazer, 1995).

3.2.1

Selection Selects a pool of iterations from a current set with respect to a fitness criterion. This is

done so that only the best suitable iterations(genes) are used to create the next generation.

Darwin defined evolution as "descent with modification," the idea that species change over time, give rise to new species, and share a common ancestor. The mechanism that Darwin proposed for evolution is natural selection Dissertation 2020 10 13

C.A.T 3.2.2

Crossover Is one of the operators used in algorithmic design, a pair of candidates are chosen from

the pool created in the “selection’ process. This is done by combining the properties of parents to create new potential solutions, the selection process can be completely random or can follow a set of given parameters.

3.2.3

Mutation This is another genetic operator, used to ensure genetic diversity. Genes are modified

to achieve even greater fit, the value for modification usually remains close to the original.

3.2.4

Process

Figure 5:Activity diagram of a typical algorithmic design process. Source: Interactivearchitecturelab.org

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C.A.T

4

PROCESS

Figure 6: Algorithmic process expressed as a flow diagram. Source: Author

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5 5.1 5.1.1

FORMULATED ALGORITHM

Parts of the system and user inputs Wall size

x Size: The length of the façade/Floor area, in meters. y Size: The height of the façade/Floor area, in meters.

5.1.2

fenestrations

size of façade border is the input to be provided in meters Count: Number of openings, area to be calculated. (minimum of 1 to be provided, maximum is not restricted)

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C.A.T Seed: Randomization Seed14, this is a variable used In Galapagos to generate the best fit.

5.1.3

Sunshade

Seed: Randomization Seed15, this is a variable used to generate scattered points.

Min height of Sunshade: The minimum height for the randomized sunshade to be provided, in meters(s).

Maximum height of Sunshade: The maximum height for the randomized sunshade to be provided, in meter(s).

5.1.4

Thickness

Factor: is the depth of the façade/ curtain wall/ wall. In the case of floor area, provide the minimum.

14 15

A seed value specifies a particular stream from a set of possible random number streams.

A seed value specifies a particular stream from a set of possible random number streams.

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C.A.T 5.2 5.2.1

Processes Generating base dimensions

Creation of the wall/ façade, area of test through user-defined variables, x, y.

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C.A.T 5.2.2

Creating border/ boundary surface

Creation of façade boundary: These functions create the border for the façade with the “size of border” variable obtained from the user.

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C.A.T 5.2.3

Generating random population of points on surface.

Populate, Process to populate the data set with 2d population data, count defined by user and seed defined by Galapagos evolutionary solver.

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C.A.T 5.2.4

Calculations for radial dimensions of circle fenestrations

In the case of circle16 fenestrations. The above process is used to calculate the area of a circle, diameter and size relative to the number of openings defined by the user.

16

In case of circle fenestration, algorithm A is to be used

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C.A.T 5.2.5

Calculations for dimensions of square fenestrations

In the case of square17 fenestrations. The above process is used to calculate the area of a square, diameter and size relative to the number of openings defined by the user.

17

In case of square fenestrations, algorithm B is to be used.

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C.A.T 5.2.6

Generation of combined surface

Merging shapes with common surfaces to create a combined poly-surface which can be treated as one surface. This enables the script to Separate the space used as void and the space which will remain solid.

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C.A.T 5.2.7

Adding depth/ thickness to the surface

Factor: In the case of wall, façade or curtain walls, the width of the component is defined here.

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C.A.T 5.2.8

Creating sunshade with user defined variables

Using the randomize function and the domain rage function, a set of several randomized heights for the sunshade can be generated. Else, these values can be user-defined like in the example above. These values are defined in meters as a list and are used to create the length of sunshade by assigning each sunshade to its length parameter.

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C.A.T 5.2.9

Tilting sunshade.

X axis offset: The direction along the x-axis Y axis offset: The direction along the y-axis These inputs determine the angle of the incline for the sunshades to be provided, the designers can specify the angle based on climatic and site data of the project.

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C.A.T 5.3 5.3.1

Galapagos evolutionary solver Input controls for Galapagos

Input controls: The input controls for the % of openings variable defined by the user.

The seed value is the genome in which Galapagos will be comparing for the best fitness function.

5.3.2

Fitness Function

Fitness Function: defines that the area of the openings needs to be as close to the % opening user-defined variable as possible.

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C.A.T 5.3.3

Galapagos User interface

The fitness function is set to find maximum fitness, this will allow Galapagos to find the number which has the highest value of opening.

5.3.4

Processing of data

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C.A.T The evolutionary solver considers each seed value and calculates its total area which is a preamble and finds the seed with the best fit. Or else, manually users are able to select one of the mutations which may serve better considering other variables such as; site conditions, vegetation or aesthetic appeal and designer vision. The results are arranged in descending order on the bottom right of the Galapagos window, from best fit to the worst fit.

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C.A.T 5.4

Formulated algorithm for Circle, in grasshopper interface

Figure 7Algorithmic process as expressed in grasshopper user interface. Source: Author

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C.A.T

Figure 8Figure 7Algorithmic process as expressed in grasshopper user interface. Source: Author

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C.A.T 5.5

Formulated algorithm for Square fenestrations in grasshopper interface

Figure 9Algorithmic process as expressed in grasshopper user interface. Source: Author

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C.A.T

Figure 10Figure 9Algorithmic process as expressed in grasshopper user interface. Source: Author

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C.A.T

6 6.1 6.1.1

CONTROL VARIABLES

Variables Identifying the inputs for the testing process x Size: The length of façade/Floor area, in meters. y Size: The height of façade/Floor area, in meters. size of façade border is the input to be provided in meters Count: Number of openings, area to be calculated. (minimum of 1 to be provided,

maximum is not restricted).

Seed: Randomization Seed, this is a variable used In Galapagos to generate the best fit Min height of Sunshade: The minimum height for the randomized sun shade to be provided, in meters(s).

Maximum height of Sun shade: The maximum height for the randomized sun shade to be provided, in meter(s).

Seed: Randomization Seed, sliding this number slider will generate new variation of the sun shade devices.

Factor: is the depth of the façade/ curtain wall/ wall. In case of floor area, provide minimum.

6.2

Process

Input the desired values of user-

Run Galapagos by selecting the

controlled variables.

maximum fit

Select Iterations with best fit

Iterations with best fit with be generated in descending order

Convert into rhino Model to be imported into ladybug analysis Figure 11: Process for generated algorithm. Source: Author

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C.A.T 6.3

Testing conditions.

Table 1: Testing parameters for grasshopper

Case A

Case B

Case C

Circle

Square

Circle

Length (x-axis)

60m

14m

40m

Height (y-axis)

40m

45m

40m

Size of façade border

1m

2m

3m

Number of openings

10

20

30

Percentage of opening

60

40

50

Thickness (in case of façade)

0.5m

0.3m

0.5m

Offset Along the X-axis

+0.5m

0m

0m

Offset Along the Y-axis

-0.5m

0m

-0.7m

Minimum length of sunshade

1m

.5m

2m

The maximum length of

5m

.5m

4m

Shape

sunshade

Table 2: Climatic considerations to test in ladybug

Case A Location

Trivandrum

Case B Mumbai

Case C Kolkata

Ladybug and Grasshopper tests will be done independently, therefore separate sets of parameters will be needed. Ladybug weather source files are available from https://www.ladybug.tools/epwmap/ 18

18

EPWMAP is an online database which contains climatic data from cities and towns around the world.

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C.A.T

7 7.1

PERFORMANCE ANALYSIS

Case A

Figure 12: Ladybug thermal analysis of a room with the facade generated by grasshopper Source: Author

Figure 13: Corresponding test result with a generic opening

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C.A.T

Figure 14 generated form with variables applicable to case A. Source: Author

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C.A.T 7.2

Case B

Figure 15: Ladybug thermal analysis of a room with the facade generated by grasshopper Source: Author

Figure 16: Corresponding test result with a generic opening

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C.A.T

Figure 17generated form with variables applicable to case B. Source: Author

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C.A.T 7.3

Case C

Figure 18: Ladybug thermal analysis of a room with the facade generated by grasshopper Source: Author

Figure 19: Corresponding test result with a generic opening

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C.A.T

Figure 20generated form with variables applicable to case C. Source: Author

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C.A.T 7.4

Analysis There is not a large disparity between the two tests, both sceneries created about the

same internal condition, although providing a heightened aesthetic quality and visual stimulation, at the same time providing an unconventional design as well as reducing the time.

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CONCLUSION

This paper presents the evolution of parametric design in architecture. Computer aided design methods, strategies and evolution was studied. Several relevant examples where also displayed to illustrate the practical applications of the various processes.

An algorithmic process was developed and scripted in grasshopper, for a parametric façade using genetic analysis. The script has three processes; input, process and output. Input parameters include physical dimensions and properties for the façade, (length, width, depth, number of fenestrations, percentage of opening, size and orientation of sunshades). The process includes process done by grasshopper as well as evolutionary solver, Galapagos, Grasshopper develops a solid based on the input parameters provided by the user. Galapagos tests the fitness criteria for the given condition (area of fenestration openings and the percentage of open area defined by the user). By altering seed values, the seed values are arranged in descending order from best to worst fit. The designer has the ability to decide if he/she would like to proceed with the best fir or choose from the top percentile of results. The results were tested for its thermal performance using ladybug climatic analysis, the results showed no disadvantages to the algorithm compared to conventional façade design, to the contrary the grasshopper generative output developed a façade with relative complexity but using very little input from the designer, therefore it is safe to conclude that algorithmic design can provide designers with a powerful tool that can save time, money and effort which otherwise would have to have been exhausted by the users. Introducing algorithmic tools into architecture education will enable the future architects of India to create, at least for specific cases, the opportunity to save time, materials and the ability to create forms unique to each project.

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10 APPENDIX A Abstract: The Sketchpad system uses drawing as a novel communication medium for a computer. The system contains input, output, and computation programs which enable it to interpret information drawn directly on a computer display. It has been used to draw electrical, mechanical, scientific, mathematical, and animated drawings; it is a general-purpose system.

Sketchpad has shown the most usefulness as an aid to the understanding of processes, such as the notion of linkages, which can be described with pictures. Sketchpad also makes it easy to draw highly repetitive or highly accurate drawings and to change drawings previously drawn with it. The many drawings in this thesis were all made with Sketchpad. A Sketchpad user sketches directly on a computer display with a “light pen.” The light pen is used both to position parts of the drawing on the display and to point to them to change them. A set of push buttons controls the changes to be made such as” erase,” or “move.” Except for legends, no written language is used. Information sketched can include straight line segments and circle arcs.

Arbitrary symbols may be defined from any collection of line segments, circle arcs, and previously defined symbols. A user may define and use as many symbols as he wishes. Any change in the definition of a symbol is at once seen wherever that symbol appears. Sketchpad stores explicit information about the topology of a drawing. If the user moves one vertex of a polygon, both adjacent sides will be moved. If the user moves a symbol, all lines attached to that symbol will automatically move to stay attached to it. The topological connections of the drawing are automatically indicated by the user as he sketches. Since Sketchpad is able to accept topological information from a human being in a picture language perfectly natural to the human, it can be used as an input program for computation programs which require topological data, e.g., circuit simulators. Sketchpad itself is able to move parts of the drawing around to meet new conditions which the user may apply to them. The user indicates conditions with the light pen and push buttons. For example, to make two lines parallel, he successively points to the lines with the light pen and presses a button. The conditions themselves are displayed on the drawing so that they may be erased or changed with the light pen language.

Any combination of conditions can be defined as a composite condition and applied in one step. It is easy to add entirely new types of conditions to Sketchpad’s vocabulary. Since the conditions can involve anything computable, Sketchpad can be used 10 for a very wide range of problems. For example, Sketchpad has been used to find the distribution of forces in the members of truss bridges drawn with it. Sketchpad drawings are stored in the computer in a Dissertation 2020 42

C.A.T specially designed “ring” structure. The ring structure features rapid processing of topological information with no searching at all. The basic operations used in Sketchpad for manipulating the ring structure are described. (Sutherland I. E., 2003)

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