MCM Serpentine Pavilion

MCM : STUDIO 1 : SERPENTINE PAVILION

This MCM design research project seeks to improve understanding of the generative design process by documenting a design proposal for the serpentine pavilion. The diagram above represents the pattern sequence of spaces we seek to optimise as a design problem.

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MCM PROJECT

MCM is the architecture supergroup formed by Michael O’Reilly, Carol Sun, and Maxine Zhou during their time at the Manchester School of Architecture

This design research project is a pavilion design proposal that uses generative design techniques to optimise a complex sequence of pre-determined spaces. The space sequence problem is complex because we consider multiple hierarchical factors; scale, porosity, and openess. To address all these factors in one high performing solution, we are using generative design. This allows us to produce many iterative solutions to our design problem which can be evaluated and selected. The pavilion typology is an ideal testbed for experimenting with the generative design process and testing an algorithm for complex space sequences.

MICHAEL O’REILLY CAROL SUN MAXINE ZHOU

ADVISORS SOLON SOLOMOU FILIPPOS FILIPPIDIS ULYSSES SENGUPTA ROBERT HYDE SAMUEL BLAND

CRITICS

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DAVID CONNOR KATHRYN TIMMINS

DOCUMENT INFO

FILE NAME: MCM_Studio 1_Serpentine Pavilion CREATION DATE: 30/09/19 MODIFICATION DATE: 20 January 2020 11:38 am PUBLICATION DATE: 20/01/20

ALGORITHMIC SEQUENCE OF SPACES

ARCHITECTURAL TEAM

SPACE PATTERN

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THESIS STATEMENT

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CONTEXT

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PUBLIC SPACE THEORY

// III :

SPACE ARCHETYPES

// IV :

GENERATIVE DESIGN

// V :

DESIGN RESOLUTION

RESEARCH AREA RESEARCH POSITION DESIGN PROBLEM

PROJECT AND SITE

HUMAN CENTERED DESIGN POROSITY

HIERARCHIES OF SPACE PATTERN LANGUAGE

PARAMETRICALLY DEFINING SPACES FOR OUR ALGORITHM SEQUENCE

FORM FINDING

DESIGN SPACE - PARAMETERISATION OF THE DESIGN PROBLEM DESIGN EVALUATION DESIGN OPTIMISATION

GEOMETRY REFINEMENT

DIGITAL FABRICATION AND ASSEMBLY CONSTRUCTION DRAWING PACKAGE

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REFLECTION BIBLIOGRAPHY APPENDIX

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PORTFOLIO STRUCTURE

THESE ARE SPACE PATTERNS

(They are based upon Christopher Alexander’s pattern language)

We use them throughout this portfolio to represent different space archetypes.

We define these space archetypes in detail in Part III

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// I : CONTEXT

RESEARCH AREA RESEARCH POSITION DESIGN PROBLEM PROJECT AND SITE

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RULE OF THUMB / MODERNIST

NOVEL, YET HIGH PERFORMING

EXPRESSIONIST / POSTMODERNIST

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Glendinning, Miles. (2019)

IMPROVE UNDERSTANDING OF THE GENERATIVE DESIGN PROCESS TO CREATE NOVEL, YET HIGH PERFORMING ARCHITECTURE Artificial Intelligence (AI) has deeply affected the world we live in by becoming a core technology in many industries, such as finance, linguistics, statistics, transport and engineering. Architectural design practice however, has been stagnant in leveraging AI until very recently. Emergent design processes such as generative design leverage AI to offer powerful emergent workflows. Compared to traditional intuitive processes, these workflows can allow designers to explore a wider space of design, quantifiably evaluate their designs, and evolutionarily search for high performing designs. A workflow such as this can lead not only to the discovery of novel and unexpected solutions, but to a deeper understanding of the design problem itself.

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RESEARCH AREA : THE GENERATIVE DESIGN PROCESS

STUDY DESIGN PROCESS

REFELECTION

EXPERIMENT

INSIGHT

KNOWLEDGE

UNDERSTANDING

RESEARCH FOR DESIGN seeks to provide information and insights to enable design (Downton, 2003).

It provides information and insights to create better results in specific design projects.

RESEARCH BY DESIGN seeks to create knowledge through participatory action and reflection (Jonas, 2007).

It involves participatory action and reflection and aims to provide results in the form of an explanation or theory in a broader context for use in future design projects.

RESEARCH INTO DESIGN seeks to improve understanding of the processes of design and/ or designers (Buchanan, 2007).

It is a theoretical approach that focuses on improving understanding of methods, thinking, and processes in design.

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WE WILL TAKE A RESEARCH INTO DESIGN APPROACH TO IMPROVE UNDERSTANDING OF THE GENERATIVE DESIGN PROCESS

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DESIGN (ACTION)

GATHER INFORMATION

The following assumes the frameworks for design research as: research for design, research by design, and research into design, as distinguised by Frayling (1993).

Our position is research into design because we are focused on developing understanding of emergent design processes such as generative design.

However, in order to develop our success metrics, we will also operate briefly within a research for design framework.

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RESEARCH POSITION : RESEARCH INTO A DESIGN PROCESS

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COMPLEX SEQUENCE OF SPACES

TAKES INTO ACCOUNT NO FACTORS, OR IF IT DOES, ONLY ONE AT A TIME IN A SLOW PRIMITIVE MANNER

TAKES INTO ACCOUNT MULTIPLE FACTORS IN THE FORM OF AN INSTRUCTIONAL COMPLEX SYSTEM AND MANAGES THEM DYNAMICALLY

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SIMPLE SEQUENCE OF SPACES

HOW CAN WE OPTIMISE THE SEQUENCE AND ARRANGEMENT OF A SET OF PRE-DETERMINED SPACES? When it comes to designing the sequence of spaces, the designer will traditionally make intuitive decisions; a process dependent on the experience of the architect.

This project seeks to challenge this notion by defining the design problem of complex space arrangement as an algorithm to be solved quantifiably.

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DESIGN PROBLEM : COMPLEX SPACE SEQUENCES

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OUR ARCHITECTURAL STRATEGY IS TO DEMONSTRATE OUR SPACE SEQUENCE ALGORITHM THROUGH THE DESIGN OF A PAVILION It will consist of a massing formed of a sequential hierarchy of contrasting spaces (“closed, semi-open and open spaces”) that conform to pre-determined rules regarding connectivity, access, exposure, containment and environmental internal conditions. We will use this massing as a basis for creating a variably porous monocoque shell. This will lead us to a digtial manufacturing and assembly study.

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STRATEGY : ALGORITHMIC SEQUENCE PAVILION

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MCM

2019

JUNYA ISHIGAMI

2018

FRIDA ESCOBEDO

2017

DIÉBÉDO FRANCIS KÉRÉ

2016

BJARKE INGELS

2015

SELGAS CANO

2014

SMILJAN RADIC

2013

SOU FUJIMOTO

2012

AI WEIWEI + HERZOG & DE MEURON

2011

PETER ZUMTHOR

2010

JEAN NOUVEL

2009

SANAA

2008

FRANK GEHRY

2007

OLAFUR ELIASSON

2006

REM KOOLHAAS

2005

ÁLVARO SIZA

2004

MVRDV

2003

OSCAR NIEMEYER

2002

TOYO ITO

2001

DANIEL LIBESKIND

2000

ZAHA HADID

MCM

THE SERPENTINE PAVILION IS AN IDEAL PROJECT FOR AN EXPERIMENT TESTBED

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2020

The serpentine pavilion has been selected as an ideal testbed opportunity for our research aims and scope.

Its suitability is based on the following factors:

• Flexible brief - suits our emergent interests • Small scale - suits our focused scope • Temporary nature - ideal for a design experiment

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PROJECT + SITE : WHY SERPENTINE PAVILION?

DECEMBER

NOVEMEBER

OCTOBER

SEPTEMBER

AUGUST

JUNE

MAY

APRIL

MARCH

FEBRUARY

JANUARY

JULY

OPERATION

DECONSTRUCTION

Visitor numbers

Serpentine Gallery Pavilion 2013 Photo by David Hawgood

Serpentine Gallery Pavilion 2012 Photo by Iwan Baan

Frida Escobedo 2018 Pavilion Photo via Serpentine Galleries

COS x Serpentine Park Nights 2019: Jakob Kudsk Steensen, The Deep Listener

COS x Serpentine Park Nights 2019: Cecilia Vicuña, Clit Nest

COS x Serpentine Park Nights 2019: 1010 Benja SL, KINDLIG: The Two House Shuffle

CAFE

MEETING

VISITING & EXPLORING

TALKS & DEBATES

EXHIBITIONS & SCREENINGS

PERFORMANCES

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~300m2 TEMPORARY STRUCTURE: CAFÉ & MEETING SPACE BY DAY, FORUM FOR LEARNING, DEBATE AND ENTERTAINMENT AT NIGHT

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CONSTRUCTION

The temporary nature of the pavilion makes it an ideal project for a design experiment. The pavilion is built and open for 3-4 months before being taken down.

The pavilion serves to host to many extravagant events and programs during the summer that require specific spatial qualities. It is also open to the general public when there are no events on. This “downtime” can be considered and event in itself and the space should respeond to these demands too.

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PROJECT + SITE : BRIEF AND PROGRAM

Carl Andre, Dawn Gallery, 1967 During the 19070s when the notion of ‘Real’ turned into the era’s perspective, artists and architects expressed different understanding in the phenomenology of a ‘room’ in the form of ‘pavilions’. Artists utilised room space as the horizon of instalments whilst architects expressed rooms without actuality and program.

Greg Lynn & Fabian Marcaccio, The predator, 2001 With the introduction of digital tools and computation, materiality and form were allowed to be explored across mediums.

ARCHITECTURE

Henry Flitcorft, Temple of Apollo, 1765 Pavilion in history appears as a pure architecture discipline with elements in nature transformed into ornaments

Peter Einsenman, House 1, 1968 Dynamic between artists and architect’s product start to meet. Contemporary pavilions are no longer a purview of architects and are often collaborated between architects and artists. Pavilions surge in number.

ICD/ITKE, Bizarre Pavilion, 2014 Between artists and architects, pictorialization of nature separated art and architecture, where architecture used nature as precedents of systems, working themselves into orders and earning autonomy.

Rules and mathematics were adopted to computation principles such as evolutionary design.

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ART

THE PAVILION TYPOLOGY SHOULD BE TREATED AS AN OPPORTUNITY TO INNOVATE AND EXPERIMENT Once an opportunity for pioneers to experiment and demonstrate new techniques, materials or theories, the pavilion typology has lost some of its original meaning as it became more sculptural and ego-driven.

We wish to use the pavilion as a testbed for researching emergent design processes.

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PROJECT + SITE : WHAT SHOULD A PAVILION BE?

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// II : PUBLIC SPACE THEORY

HUMAN CENTERED DESIGN POROSITY HIERARCHIES OF SPACE PATTERN LANGUAGE

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PUBLIC

SEMI PUB.

SEMI PRIV.

PRIVATE

STREET/SIDEWALK

YARD

PORCH

L.R./D.R/K

PUBLIC

SEMI PUB.

SEMI PRIV.

PRIVATE

SEMI-INT.

INTIMATE

ST./HALL

B.R./BA.

SEMI-INT.

INTIMATE

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TOP: Intimacy Gradient (adapted from Christopher Alexander) BOTTOM: Gradient of Privacy (adapted from Julia W. Robinson)

DESIGN GUIDELINES FOR SPACE HIERARCHIES HAVE BEEN DOCUMENTED, BUT DON’T HELP US WITH OUR DESIGN PROBLEM. “Lay out the spaces of a building so that they create a sequence which begins with the entrance and the most public parts of the building, then leads into the slightly more private areas, and finally to the most private domains.” from page 613 of “A Pattern Language: Towns, Buildings, Construction” by Christopher Alexander This rule is highlighted becasue although it may in fact be a good design principle, it is a simple one. Our design problem involves multiple factors and therefore cannot be solved by a simple rule of hierarchy.

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PUBLIC SPACE THEORY : HIERARCHIES

Mystery and surprise stimulate the human mind and senses and invite discovery.

CHAPTER 10.

HUMAN-CENTERED DESIGN GUIDELINES Lori Gee Herman Miller, Inc.

HEALTHFUL

LIGHTING

Ergonomic considerations STIMULATING

Sensory Cues

ELEMENTS OF SURPRISE

TRANSPARENCY, VISUAL ACCESS

Connection to nature

Colour and texture

DIVERSE SHAPES BALANCING COMMUNITY AND SOLITUDE

SOCIAL, COMMUNTIY SPACE

OPPORTUNITIES AND SPACES FOR SOCIALISATION

REFUGES, PRIVATE SPACES

Consider the potential of hallways and pathways that provide unexpected spaces for group events, casual conversations, or hiding away for quiet reflection.

Consider areas that support chance encounters or lingering after an event.

Connecting visually lets people feel a part of something bigger. To see others engaged can energize the individual.

Consider adjacent areas and how to connect contrasting spaces.

Corridors, too, become part of the learning experience when they invite activity and have interesting views, as opposed to long, stark, and linear places.

Vistas into and out of spaces need not cause distraction, instead enhancing cognitive activities.

Spaces that offer visual choices of shape and form create visual and tactile interest; a rectangular box is rarely the correct answer in human centered design.

Consider the influence of geometry on the activities within the space: A circle, for example, suggests collaboration and communication, much like a campfire did for early generations.

Balance the dual and opposite human needs for community and solitude.

Because learning happens both in quiet, private moments and in lively, social settings, environments need to offer a spectrum of private and interactive places.

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Lighting has a psychological impact, such as reducing stress and elevating mood. Tuning the mood and stimulation levels can be achieved through a mixture and variety of lighting types (including natural light)and conditions.

HUMAN CENTERED DESIGN FOR LEARNING SPACES ALIGN WITH THE IDEA-EXCHANGE FOCUSED PROGRAM AT SERPENTINE Taking this forward into our own design we’re focused on certain headlines which are outlined above. We’re looking at how the human-centered design guidelines can inform our form finding. It talks about transparency and visual access even if there isn’t direct circulation, and also diverse shapes (there’s research on why box shaped rooms are bad). There’s also a section on balancing community and solitude; it talks about providing a diverse range of spaces: social and community spaces, as well as refuges and private spaces.

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PUBLIC SPACE VALUES : HUMAN-CENTERED DESIGN

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High porosity high degree of visual connection (Isovist) high degree of triangulation (access points from circulation) high degree of exposure (daylight infiltration) high degree of fragmentation low degree of containment low degree of isolation

Contextual definition of porosity, by Steven Holl Architects, NY

Porosity and Permeability - quantifaible measures

Potential implications of porosity on spatial geometry

WE ARE INTERESTED IN POROSITY BECAUSE IT CAN BE A QUANTIFIABLE MEASURE OF CONNECTIVITY

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Low porosity low degree of visual connection low degree of triangulation low degree of exposure low degree of fragmentation high degree of containment high degree of isolation

Porosity or void fraction is a measure of the void spaces in a material, and is a fraction of the volume of voids over the total volume, between 0 and 1, or as a percentage between 0% and 100%. This numerical value attachment means it is an ideal parameter for our design space because it is measureable and computable.

Pore (from Greek poros) means “a minute opening”. Porosity or “the state of being porous” in the context of organic chemistry and the study of plants and animals indicates the existence of small openings. In biology and in medicine porosity is defined as: “the attribute of an organic body to have a large number of small openings and passages that allow matter to pass through”. The forms, sizes and distribution of pores are arbitrary.

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POROSITY : DEFINITIONS AND MEASUREMENTS

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MIT Dormitory (Steven Holl)

Marc-Fornes-Theverymany_Hyparbole_02

MACRO POROSITY (URBAN SCALE)

MESO POROSITY (BUILDING/ROOM SCALE) A porous morphology has been used to produce positive effects at a building scale, i.e. better air and light circulation, better accessibility and visibility, and better communication between interior and exterior spaces. It has also been used to establish new relationships between the various building programs, and served as a methodology for controlling and measuring these relationships.

MICRO POROSITY (MATERIAL SCALE)

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Sliced Porosity Block in Chengdu, China (Steven Holl)

POROSITY HAS TRAJECTORY AND PRECEDENT OF BEING APPLIED IN ARCHITECTURE Althought porosity is a concept found originally within geology, biology, medicine and organic chemistry, it has been transferred and implemented in the built environment, as a from finding methodology, a design concept or principle, or as a paradigm of how a design concept can be converted into a system of production rules to generate designs.

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POROSITY : APPLICATIONS IN ARCHITECTURE

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Patterns by Christopher Alexander

CHRISTOPHER ALEXANDER’S PATTERN LANGUAGE IS A USEFUL FRAMEWORK FOR DEFINING PRE-DETERMINED SPACES IN OUR ALGORITHM A pattern language is a method of describing good design practices or patterns of useful organization. We will loosely adopt a pattern language method to describe our predetermined spaces in the next section.

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PUBLIC SPACE VALUES : PATTERN LANGUAGE

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// III : SPACE ARCHETYPES

PARAMETRICALLY DEFINING SPACES FOR OUR ALGORITHM SEQUENCE

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It functions as a withdrawal space, coupling it nicely with more public spaces such as the campfire or watering hole.

The Mountain Top is a destination space for gaining perspective and experience.

It should be one of the hardest to reach spaces, and should require one to go on a journey before reaching.

The Campfire is a gathering space for storytelling. The campfire responds to the Serpentine program of learning through lectures and recitals.

The Watering Hole is a social space for conversation, crosspollination, chance encounters and idea-exchange in groups.

The Gallery is characterised by it’s low, rectalinear space that is specifically designed for particular lowlight programs that the other spaces cannot accomodate.

It has the lowest sunlight exposure and high containment.

It is a curious, contained space.

Through human centered design principles and proximity rules it should only be big enough to accomodate a couple of people at a time.

Like the cave, it is small and isolated, but unlike the cave it is open and exposed.

Therefore it lacks the intimacy and privacy of the cave, making it a short-stay, destination space where one can experience their surroundings from a unique perspective.

Brother Klaus Field Chapel by Peter Zumthor Photo by Pietro Savorelli

Pinohuacho Observation Deck in Chile Photo by Rodrigo Sheward & German Valenzuela

International Festival of Digital Arts Greece Photo via Athens Digital Arts

Barbican Martini Bar in London Photo via Barbican

“Artificial Nature” Exhibition display

by Haru Ji and Graham Wakefield

The Burg square in Bruges, Belgium Painting by Jan Baptiste van Meunincxhove

CAVE

MOUNTAIN TOP

CAMPFIRE

WATERING HOLE

GALLERY

SQUARE

PRIMORDIAL

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The Square is characterised by it’s openness, high porosity and large area. It functions as a town square might; a gathering and meeting space for events, commerce and socialising.

It is an especially interesting space in theory because it is both public and intimate at the same time. Public and intimate are usually opposite characteristics, so it seems paradoxical.However, camfires like lecture halls achieve harmony by keeping those present engaged with an expert storyteller.

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The Cave is a space for quiet self relfection, characterised by it’s small, intimate atmosphere.

WE HAVE DETERMINED A SET OF ARCHETYPAL SPACES THAT FOLLOW GEOMETRIC AND CHARACTER PATTERNS

// III : 020 PRE-DETERMINED SPACES : ARCHETYPES

It still has a sense of enclosure, but is the most open and public of all the spaces.

MODERN

SIZE

SIZE

SIZE

SIZE

SIZE

SIZE

POROSITY

POROSITY

POROSITY

POROSITY

POROSITY

POROSITY

OPENESS

OPENESS

OPENESS

OPENESS

OPENESS

OPENESS

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Brother Klaus Field Chapel by Peter Zumthor Photo by Pietro Savorelli

Pinohuacho Observation Deck in Chile Photo by Rodrigo Sheward & German Valenzuela

International Festival of Digital Arts Greece Photo via Athens Digital Arts

Barbican Martini Bar in London Photo via Barbican

“Artificial Nature” Exhibition display

by Haru Ji and Graham Wakefield

The Burg square in Bruges, Belgium Painting by Jan Baptiste van Meunincxhove

CAVE

MOUNTAIN TOP

CAMPFIRE

WATERING HOLE

GALLERY

SQUARE

PRIMORDIAL

MODERN

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SIZE

WE HAVE QUANTIFIED THE CHARACTERISTICS OF EACH SPACE INTO PARAMETRIC RULES

// III : 021 PRE-DETERMINED SPACES : CHARACTER PARAMETERS

Pinohuacho Observation Deck in Chile Photo by Rodrigo Sheward & German Valenzuela

International Festival of Digital Arts Greece Photo via Athens Digital Arts

Barbican Martini Bar in London Photo via Barbican

“Artificial Nature” Exhibition display

by Haru Ji and Graham Wakefield

The Burg square in Bruges, Belgium Painting by Jan Baptiste van Meunincxhove

CAVE

MOUNTAIN TOP

CAMPFIRE

WATERING HOLE

GALLERY

SQUARE

PRIMORDIAL

MODERN

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Brother Klaus Field Chapel by Peter Zumthor Photo by Pietro Savorelli

WE DESIGNED GEOMETRY FOR EACH SPACE ACCORDING TO THEIR PARAMETERS - THIS HELPS US IDENTIFY THEIR PATTERN

// III : 022 PRE-DETERMINED SPACES : GEOMETRY

Pinohuacho Observation Deck in Chile Photo by Rodrigo Sheward & German Valenzuela

International Festival of Digital Arts Greece Photo via Athens Digital Arts

Barbican Martini Bar in London Photo via Barbican

“Artificial Nature” Exhibition display

by Haru Ji and Graham Wakefield

The Burg square in Bruges, Belgium Painting by Jan Baptiste van Meunincxhove

CAVE

MOUNTAIN TOP

CAMPFIRE

WATERING HOLE

GALLERY

SQUARE

PRIMORDIAL

MODERN

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Brother Klaus Field Chapel by Peter Zumthor Photo by Pietro Savorelli

WE DESIGNED GEOMETRY FOR EACH SPACE ACCORDING TO THEIR PARAMETERS - THIS HELPS US IDENTIFY THEIR PATTERN

// III : 023 PRE-DETERMINED SPACES : GEOMETRY

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// IV : GENERATIVE DESIGN FORM FINDING DESIGN SPACE - PARAMETERISATION OF THE DESIGN PROBLEM DESIGN EVALUATION DESIGN OPTIMISATION

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Evaluation and Optimisation - Airbus Bionic Partion (for Airbus, by The Living)

Novel, high performing solution - Airbus Bionic Partion (for Airbus, by The Living)

EXPLORE A WIDE RANGE OF OPTIONS Compared to traditional design processes where we might use hand crafts and intuition to design an object and maybe explore a few options, in the generative design process we design a computational system we call a ‘design space’ from which we can generate hundreds of iterations, showing us the full range of possibilities of a particular design concept.

EVALUATE AND OPTIMISE DESIGNS The design space can be set up to evaluate the performance of each design automatically. We can then iterate and optimise the design system, revisiting the approach to the design problem if necessary.

We can optimise for digital fabrication; paticular materials or manufacturing methods, set goals and parameters, regenerate the iterations and tweak if necessary.

NOVEL, YET HIGH PERFORMING Generative design uses an evolutionary process to search the design system to find novel and high-performing designs.

New geometries and possibilities await; generative design lets us create optimized complex forms, which could be impossible with traditional designing and manufacturing methods.

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Iteration Generation - Airbus Bionic Partion (for Airbus, by The Living)

GENERATIVE DESIGN ALLOWS US TO PRODUCE MANY ITERATIVE SOLUTIONS TO OUR DESIGN PROBLEM WHICH CAN BE EVALUATED AND SELECTED.

// IV : 025 GENERATIVE DESIGN : USES, AND VALUE TO THE DESIGNER

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DESIGN SPACE

DESIGN EVALUATION

DESIGN EVOLUTION

DESIGN SPACE

Create a model that parameterises the design problem and defines all possible solutions that can be searched by an algorithm.

DESIGN EVALUATION

Define the objectives and contraints of the model, and appraise solutions according to measures.

DESIGN EVOLUTION & SELECTION

Run the model through a series of optimisation ‘experiments’ to derive novel and high-performing solutions to the design problem.

// IV : 026 GENERATIVE DESIGN PROCESS : THREE STEP SYSTEM

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START

The principle of LEARNING is key to the generative design process.This involves feedback and EVOLUTION through ITERATIVE and evolutionary OPTIMISATION.

DEFINE PARAMETERS

DESIGNER

GENERATE GEOMETRY

EVALUATION

DESIGNER sets and can modify EVALUTION criteria

DESIGNER can manually select and refine OUTPUTS from a set of iterations

OUTPUT

EVOLVE

The output can be continuously optimised against the set criteria. The generative deign process can produce many iterative solutions, some more succesful than others.

The fittest of these can then be selected by the designer as a final output, or as parents for a further iterative study.

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DESIGNER sets and can modify PARAMETERS

// IV : 027 GENERATIVE DESIGN : FLOWCHART PROCESS

the design space and how designs within the space are related to each other. Complexity refers to the potential of a design space to create

the design space and how designs within the space are related to each other. Complexity refers to the potential of a design space to create

Nagy, 2017

OVER SIMPLIFICATION

Nagy, 2017

CONTINUITY AND COMPLEXITY

OVER COMPLICATION

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simplify adjacent the model, parametrizing at make the levelvalid of larger structures such as between simplify the model, parametrizing at the level of larger structures between designs and predictions about the adjacent designs and make valid predictions about the such as walls, ßoors, door openings, etc. Because they are too ßexible, highwalls, ßoors, door openings, etc. Because they are too ßexible, highperformance of designs onthrough, the designs around them. performance The ofvariance designs based on thetodesigns around them.

The variance models are difÞcultbased to search often resulting in too models are difÞcult search through, often resulting in too opposite extreme of continuity is chaos Ñ where the conÞguration opposite of extreme of continuity is chaos Ñ where the conÞguration much unproductive exploration without the discovery of any good much unproductive exploration without the discovery of any good of solutions exploit. solutions to exploit. This should be avoided at all costs designs is to completely random. This should be avoided at all designs costs is completely random.

As with most trade-offs, the goal of the designer should be to create As with most trade-offs, the goal of the designer should be to create because with such a model the search process devolves more because or lesswith to such a model the search process devolves more or less to models somewhere in between — not so biased that they don’t fully models somewhere in between — not so biased that they don’t fully random sampling, without thenot ability to learn theare design space random or Þndsampling, without the ability design space or Þnd represent the solution space, but so variant that they impossible represent the solution space,to butlearn not sothe variant that they are impossible to search. search. better solutions over time. In fact, one of the only concrete rules better forsolutionsto over time. In fact, one of the only concrete rules for design space design is the avoidance of any random variables design within space the design is the avoidance of any random variables within the model. model.

The complexity vs. continuity trade-off tells us that our design The spaces complexity vs. continuity trade-off tells us that our design spaces should be as complex as possible without being too discontinuous. should be as complex as possible without being too discontinuous.

However, this objective is not clear cut, and there is no formal However, deÞnitions this objective is not clear cut, and there is no formal deÞnitions for ‘complex enough’ or ‘too discontinuous’. In fact, most stochastic for ‘complex enough’ or ‘too discontinuous’. In fact, most stochastic search algorithms including GA’s are able to navigate some search level of algorithms including GA’s are able to navigate some level of discontinuity in the design space, and creating a degree of discontinuity discontinuity in the design space, and creating a degree of discontinuity is a great way to create complexity in the design space. In practice, is a great way to create complexity in the design space. In practice, Bias vs. variance trade-off Bias vs. variance trade-off there are no concrete rules for how discontinuous any given there design are no concrete rules for how discontinuous any given design space mayHIGH be.

The best approach is typically to try to searchspace a variety mayofbe. The best approach is typically to try to HIGH search a variety of Complexity vs. continuity BIAS “RIGHT MODEL” Complexity vs. continuity VARIETY models and monitor the behavior of the algorithm each models and monitor the behavior of the algorithm with one.

The complexity vs. continuity trade-off describes the internal with structure of one.

The complexity vs. continuity trade-off describes theeach internal structure of

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BIAS VS. VARIANCE/CONTINUITY VS. COMPLICATION High bias refers to models with a strong preferenced solution, which limits exploration and could lead to an early exploitation of sub-optimal results. However, on the other hand models are also hard to control when there is too much variety. Too much flexibility will result in models to be hard to search through and create unproductive exploration.

Continuity and complication refers to the design space structure and their relation to each other. A space of too low complexity is not worth using generative designing methods. A design space should avoid total randomness as this does not allow the ability to learn. Complexity should be built within the principles of a certain degree of continuity.

// IV : 028 GENERATIVE DESIGN : BALANCING THE DESIGN SPACE

Access Points≥2 Outdoor Tall

Transient

Intimate Flexible Enclosed

Square Chamber Tower

Tunnel

Campfire

Gallery

Geometry-Program Match

Level of Contrast 2

D2=V2-

V3

low

4 e)3-V4

(olum

e)3=V

erenc

D(iff 3

V4

V1-

5-

V2

D1=

=V

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Connectivity/Number of Surface=1

D4

1

5-V6

6

D5=V

5

Level of Contrast=|D1|+|D2|+|D3|+|D4|+|D5|

Maximising Level of Contrast between Adjecent Rooms

Minimising Surface Area/Minimising Material Use

Minimum Num of Panels/ Fabrication Efficiency

high Surface Area(㎡)

Spatial Arrangement 1

Spatial Arrangement 2 Evolution Criteria

Evolution Criteria

...

Area<350㎡

Spatial Arrangement N

Generative Design (iteration 1,000,000)

Evolutionary Design (iteration 100)

Manual Pick (iteration 1)

Generative Design (iteration 15)

Evolutionary Design (iteration 1)

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Predetermined Spaces

Geometry Design

// IV : 029 GENERATIVE DESIGN MAP

Paneling

VALUE

PATTERN

CODE

A

B

C

D

E

F

SPACE ARCHETYPE

CAVE

MOUNTAIN TOP

CAMPFIRE

WATERING HOLE

GALLERY

SQUARE

SIZE

1

2

3

4

5

6

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PARAMETER

USING A SINGLE PARAMETER IN OUR SEQUENCE- SCALE As we assigned parameters to our space archetypes already, we will now see how our design space interprets a single parameter of scale.

Each space: A, B, C, D, E, F, representing our space archetypes act as nodes. The spaces behave collectively, and we can calculate the sequence of the parameter, in this case scale, to see if it is hierarchical or contrasting.

// IV : 030 DESIGN SPACE : PARAMETERISE THE DESIGN PROBLEM

VALUE

PATTERN

CODE

A

B

C

D

E

F

SPACE ARCHETYPE

CAVE

MOUNTAIN TOP

CAMPFIRE

WATERING HOLE

GALLERY

SQUARE

SIZE

1

2

3

4

5

6

SPATIAL POROSITY (OPENESS)

2

4

1

5

3

6

MATERIAL POROSITY

2

4

3

6

1

5

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PARAMETER

USING MULTIPLE PARAMETERS IN OUR SEQUENCE - SCALE, OPENESS, AND POROSITY We can now see how our algorithm intereprets the parameters as a set of instructions, triangulating the spaces in a sequence to accomodate all three parameters. This is where the complexity of our design problem comes in. We have designed the algorithm to compute the parameters into a sequence. The algorithm sequences the spaces randomly, it is not intelligent like the designer. It’s value is that it can produce many will now see how our design space interprets a single parameter of scale.

Each space: A, B, C, D, E, F, representing our space archetypes act as nodes. The spaces behave collectively, and we can calculate the sequence of the parameter, in this case scale, to see if it is hierarchical or contrasting.

// IV : 031 DESIGN SPACE : PARAMETERISE THE DESIGN PROBLEM

// IV : 032 DESIGN SPACE : ALGORITHMIC SEQUENCE ITERATIONS

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(PhoenixKaio via CestLaJu )

(Usuario u2toyou via Pinterest)

GOTHIC CATHEDRALS are characterised by the vaulting systems’ dynamic equilibrium in the transverse section.

ANTONI GAUDI’S HANGING-NETS involve finding the equilibrium shape of a structure given applied loads.

FREI OTTO’S MINIMAL SURFACES assume a shape in which the surface tensions are equal at every point and in every direction.

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Canterbury Cathedral (Edwin Smith via RIBA Collections)

FORM AS A COLLECTIVE & INTELLIGENT BEHAVIOURAL EVENT WHICH CAN BE QUANTIFIED AS INSTRUCTIONS (AN ALGORITHM) Matter is not benign: by looking at natural formations such as soap-film aggregations, snowflake crystals, and bee-cell morphologies, we can see that form is driven by the agency of one molecule upon another while simultaneously negotiating internally produced (surface tension) and externally imposed (surface pressure) influences.

As such, we come to understand form as a collective and intelligent behavioural event. This leads us to algorithmic form finding, where we can compute the factors at play into a finite sequence of instructions. Algorithmic forms have a well-established trajectory within architecture; Gothic Cathedrals, Antonio Gaudi’s hanging-nets and Frei Otto’s minimal surfaces.

// IV : 033 FORM FINDING : ALGORITHMIC METHODOLOGY

The notion of form

Advantages and Limitiations

Form is isolated,inert as a solely genetic event

Takes advantage of positive formal constraints of specific design mediums

Speciation of mutable architecture models

Algorithmic notation

Collective & intelligent behavioural event that senses and responds to its conditions

Undergoes selective adaptive process

Limited by immutable physical laws and material properties

Limits it to extrinsic design or structural and material investigation

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Object-centric application of typology

THE ALGORITHMIC FORM FINDING METHODOLOGY CAN LEAD US TO BETTER UNDERSTANDING AND SOLVING THE DESIGN PROBLEM An algorithm is a finite sequence of well-defined, computer-implementable instructions, typically to solve a class of problems or to perform a computation.

Digitally enacted algorithmic methods of solving architectural problems can extend the capacity of the form finding tradition through a decoupling of its processes from the limits of physics, enabling a broader examination of the number and nature of negotiable inputs. Through the application of algorithmic notation, the conception of form-production within design shifts from object-centric applications of typology, to the speciation of mutable architectural models.

// IV : 034 FORM FINDING : ALGORITHMIC METHODOLOGY

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GEOMETRIES INFLUENCE ONE ANOTHER IN A SYSTEM OF FORCES CALLED A FIELD

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Example - Christaller Diagrams of Urban Growth. [©Allen, Stan. Essays. Agrest Diana.

Commentary.2000. Practice, Architecture, Technique and Representation. Amsterdam: OPA, p. 35.]

According to Stan Allen, the urban environment exists in field, from one towards many, accelerating, merging, fragmenting and formed by forces. The system of geometry in an established field exists in a way in which none of the parts could be added nor taken away. The fragmented parts forms a system in which they are connected and establish relationships between each other. Applied to architecture, this is expressed by geometry and pattern. (Allen, 1985)

// IV : 035 FORM FINDING : FIELD THEORY

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BEHAVIOURAL FIELD CHARGES APPLIED

WE DESIGNED AN ALGORITHMIC SYSTEM THAT GENERATES GEOMETRY ACCORDING TO OUR INPUT AND RULES Based on the algorithmic form finding methodology and field theory, we have designed a behavioral computational system that is controlled by us through the input of rules and parameters. It responds dynamically to the sequence of spaces generated in our design space. The geometry of each space is controlled by a set of parameters which communicate the geometry characteristics such as height, openness, radius, containment.

Furthermore, the geometry of each space affects it’s neighbours based on behavioural field charges. These charges respond to parameters such as strength and radius. Thus we have created a geometric system which generates a collective behavioural form.

// IV : 036 FORM FINDING : ALGORITHMIC ISO-SURFACE SYSTEM

DEMONSTRATION OF EFFECTS OF GEOMETRIC PARAMETERS

ALGORITHMIC SEQUENCE INSIDE BOUNDING BOX

POROSITY

POROSITY

OPENESS

OPENESS

SIZE POROSITY OPENESS

SIZE POROSITY

SIZE

OPENESS

POROSITY OPENESS

SIZE POROSITY OPENESS

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SIZE SIZE

WE APPLIED THE GEOMETRY RULES FROM OUR SPACE ARCHETYPES ONTO THE SPACE SEQUENCE SYSTEM Using the algorithmic iso-surface system, we applied the geometric rules of our pre-determined space archetypes to the parameters of the algorithmic form system.

This realises the actual geometric properties of our pre-determined spaces in an algorithmic behavioural geometry.

// IV : 037 DESIGN SPACE : APPLICATION OF ARCHETYPE GEOMETRY

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LOW

LOW

HIGH

HIGH

HIGH

SURFACE AREA Optimal distribution of material, less material is better for obvious reasons, but this may conflict with the accuracy of the space types.

CONTRAST OF SPACES Optimal sequence and arrangement of the spaces.

Contrast is based on measuring the size, material porosity, and spatial porosity (openess) of each space and comparing it to that of it’s neighbours.

OPENESS The openess is measured as a away of evaluating total spatial porosity of the geometry.

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LOW

WHAT ARE THE MEASURES FOR EVALUATION AND HOW DO THEY RELATE TO THE PRIORITIES OF THE DESIGN SYSTEM?

// IV : 038 DESIGN EVALUATION : DEFINING MEASURES

Attributes

Level of Contrast low

Size(Volume)V4=90m³

(B)

Coordinate (x4,y4,z4) Material Porosity P4=30% Spatial Porosity S.P4= 6

(A,B,C)

high Surface Area(㎡)

(A) 2

3

5

(C)

Total Openess

4 7 8

9

6

1

Value A

Panel1 Panel2 Panel3

...

Paneln

1 2 3 4 5 6 7 8 9

x8

x3

x2 x7

x6 x5

x9

x1

x4

Sequence Rearraged by Location

8 3 2 7 6 5 9 1 4

Number of Total Points=1695

Value C

Contrast of Size X=|V8-V3|+|V3-V2|+|V2-V7|+|V7-V6|+|V6-V5|+|V5-V1|+|V1-V4| Area1 Area2 Area3

Contrast of Material Porosity

Number of Points Hit on the Mesh=P

Y=|P8-P3|+|P3-P2|+|P2-P7|+|P7-P6|+|P6-P5|+|P5-P1|+|P1-P4| 2

3

Contrast of Spatial Porosity

9

Z= To Be Evaluated at Step N

1

...

Arean

P1

Value B Surface Area = Area1+Area2+Area3+...+Arean

Value of Contrast=X+Y+Z

P2

P3

Total Openess =(1695-P1)+(1695-P2)+...+(1695-P9)

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Square

WHAT ARE THE MEASURES FOR EVALUATION AND HOW DO THEY RELATE TO THE PRIORITIES OF THE DESIGN SYSTEM?

// IV : 039 DESIGN EVALUATION : DEFINING MEASURES

P9

each space (radius, height, and material porosity) is determined, and then iterations are generated.

m2

F

Q

m1

F

...

1001| spatial arrangement 1001

p

Evolutionary Design

F

Generic Parameter

strong attachment to success measures are selected.

p Q

Q

m1

Phenome

m13

1| spatial arrangement 1 2| spatial arrangement 2 3| spatial arrangement 3

2The geometry with

2

m

First Selection

m10

3D Objects

x12

Number of Surface=1 Access Points≥2 Geometry-Program Match

Q

m9

Biomorpher

m4 Q

Genome

m3

11

F

m 8

Q

that attached to the success measures have its mutation, which are prepared for conducting next selection.

Q

F

3Each design spaces

m5

p

m6

p

m7

4With each subsequent selection, the design space attach more to success measures(objective funcions).

810

Level of Contrast

720

Value A

0.48

450

m5 m7 m8

0.9

m5 m6 m7

0.2

1.2

Third Selection

1.5

Forth Selection

m5 m7 m9

Second Selection

1st Selection to 17th Selection

750

17300

Surface Area

19600

Value B

0

2980

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1The genetic data of

18706

Openess

Value C 3064

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Geometry Optimisation—Biomorpher

BIOMORPHER - GENETIC ALGORITHM - EVOLUTIONARY FEEDBACK TO COMPUTER BASED ON THE SELECTIONS OF THE DESIGNER.

// IV : 040 OPTIMISATION : EXPERIMENT ONE

Generic Parameter

...

1001| spatial arrangement 1001

Phenome

success criteria

Genome 3D Objects Genetic Settings

Design Explorer

3D View

Geometry Optimisation—Design Explorer

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1| spatial arrangement 1 2| spatial arrangement 2 3| spatial arrangement 3

DESIGN EXPLORER - DESIGN SPACE SEARCH, EVALUATION AND SELECTION

// IV : 041 OPTIMISATION : EXPERIMENT TWO

Iterations

3D View Generic Parameter

...

1001| spatial arrangement 1001

Phenome Genome 3D Objects

Octopus

Geometry Optimisation—Octopus

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1| spatial arrangement 1 2| spatial arrangement 2 3| spatial arrangement 3

OCTOPUS - DESIGN SPACE SEARCH, EVALUATION AND SELECTION

// IV : 042 OPTIMISATION : EXPERIMENT THREE

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Level of Contrast Between Adjacent Spaces

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100 SOLUTIONS TO OUR DESIGN PROBLEM - NOW WE EVALUATE AND SELECT

Fragmented Geometry

Constraints Functions

Objective Functions/ The Goals of the Optimization 0

// IV : 043 SELECTION : 100 ITERATIONS

Surface Area

564

618

772

632

14960

11772

14176

3333

0.96

1.176

0.98

0.684

566

539

632

755

422

15023

15227

5883

8676

17774

0.96

0.86

0.876

1

1.38

618

569

580

615

638

18706

10712

14771

6467

13202

1.176

1.024

1.216

0.96

0.96

474

639

612

579

671

15025

13602

12009

17196

8990

0.48

0.96

1.176

0.96

1.316

11420 1.176

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625

OUR SELECTION OF 20 ITERATIONS NARROWED DOWN FROM 100 These 20 represent low performing and high performing solutions. This allows us to compare relatively and look at the dynamics between each of the three measures.

// IV : 044 SELECTION : 20 ITERATIONS

Surface Area Openess Contrast

Openess = 14399 Contrast = 1.056

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Surface Area = 894.60m2

WE SELECTED THIS OPTION AS THE HIGHEST PERFORMING SOLUTION FROM A RANGE OF HUNDEREDS OF ITERATIONS Using the algorithmic iso-surface system, we applied the geometric rules of our pre-determined space archetypes to the parameters of the algorithmic form system.

This realises the actual geometric properties of our pre-determined spaces in an algorithmic behavioural geometry.

// IV : 045 SELECTION : 1 HIGH PERFORMING SOLUTION

SIZE POROSITY

SIZE POROSITY OPENESS

SIZE POROSITY OPENESS

SIZE POROSITY OPENESS

SIZE POROSITY OPENESS

SIZE POROSITY OPENESS

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OPENESS

WE DON’T FINISH WITH THE GENERATIVE DESIGN RESULT WE BEGAN BY MANUALLY TWEAKED THE GEOMETRY OUTPUT WHILE RETAINING IT’S HIGH PERFORMING CHARACTERISTICS.

// IV : 046 SELECTION : FINAL GENERATIVE FORM

50 .5 V6 M³ =5 23 .6 M³

V8 =1

23 .6 V0 M³ =1 V3 1 =1 50 2 .

V7 4. 3M³ 8 = V 5 4. M³ =5 9M 23 ³ .6 V1 M³ =3 56 .8 M³

.7 M³

V4 =5

81 V2 =3

0

63 10

10

P6 =

P8 =

S.

S.

12 11 S. P S. 0= P3 38 = 9 S. 17 8 P S. 7 = P5 65 =7 3 5 S. P1 =6 95

3

P4 =

21 S.

P2 =1 S.

Overall Spatial Porosity（Openess)= 14399

CONTRAST Value of Contrast（Volume)=1.056

SURFACE AREA Surface Area=894.60㎡

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OPENESS

WE MEASURED OUR MANUALLY TWEAKED GEOMETRY TO CHECK IT WAS STILL HIGH PERFORMING We used isovist to measure openess, our algorithm to measure contrast, and a standard geometry attribute check to measure surface area.

// IV : 047 SELECTION : PERFORMANCE DATA

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// V : DESIGN RESOLUTION

GEOMETRY REFINEMENT DIGITAL FABRICATION AND ASSEMBLY CONSTRUCTION DRAWING PACKAGE

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// V : 049 SITE PLAN

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Lord Media Centre

by Future Systems

Casa Sperimentale

by Giuseppe Perugini

Canterbury Cathedral Edwin Smith via RIBA Collections

Deperdussin Monocoque Racer Pilot: Maurice Prévost, 1913

Laboratories And Corporate Facility For Pa Technology,

by Richard Rogers Partnership, 1984

ENDOSKELETON (STRUCTURE IS INSIDE THE SKIN) An endoskeleton is a structural system where loads are supported through an internal skeleton. This principle is one found readily in nature e.g. vertebrates.

Some of the benefits of an internal structure are that it is obviously more protected; but it also allows the architecture to grow additively from the structure, rather than be contained.

In this sense it serves as an attachment site (as in nature, skeletons do for muscle).

MONOCOQUE (STRUCTURE IS THE SKIN) A monocoque is a structural system where loads are supported through an external skin. This principal is one found readily in nature e.g. egg shells. A true monocoque carries both tensile and compressive forces within the skin and can be recognised by the absence of a loadcarrying frame. It can be lighter than some frame systems, but can result in a thicker and denser skin compostiion, like sandwich panels.

EXOSKELETON (STRUCTURE IS OUTSIDE THE SKIN) An exoskeleton is a structural system where loads are supported through an external skeleton. This principal is one found readily in nature e.g. insects and crustaceans. One of the key benefits of the hanging structural design is that it can improve the building’s interior flexibility, often resulting in uninterrupted (i.e. column/frame free) interior space.

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Amsterdam Centraal Station

WE LOOKED AT THREE PRINCIPAL STRUCTURAL STRATEGIES, FOCUSING ON THEIR RELATIONSHIP TO THE ARCHITECTURE SKIN Examining these 3 principle structural strategies is key to our form finding investigation, and pose some complex questions. We are not only looking for the most suitable structure in terms of weight to strength ratio, we are aslo looking for suitability to the program and concepts of the pavilion, as well as looking for opportunities in any of these structural strategies where we can innovate with generative design.

// V : 050

STRUCTURAL STRATEGIES : RESEARCH

// V : 051 PAVILION STRATEGY OVERVIEW

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WEAVERBIRD MESH SUB-DIVISION LEVEL 1

WEAVERBIRD MESH SUB-DIVISION LEVEL 3

MESH MACHINE MESH REFINE

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WE REFINED THE GEOMETRY INTO CONTROLLED TRIANGULATED PANELS THAT ARE SUITABLE FOR FABRICATION & ASSEMBLY

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INITIAL GEOMETRY MESH OUTPUT

This process demonstrates our control over the geometry; we manipulated the mesh with the goal of creating geometry that can feasibly be fabricated and assembled quickly and efficiently. This digital fabrication process invloved us balancing the uniform size and distribution of panels with the accuracy and smoothness of the surface.

// V : 052

MESH REFINEMENT : FOR PANEL CONSTRUCTION

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STARTING GEOMETRY

SMOOTHING AND REFINEMENT

PANEL CREATION

OUTPUT FROM GENERATIVE DESIGN PRCESS

MESH SUBDIVISION

UNIFORM TRIANGULATION

// V : 053

MESH REFINEMENT : FOR PANEL CONSTRUCTION

// V : 054 POROSITY

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H

CAVE: 100

ENTRANCE G: 250

A

CAMPFIRE: 150 F

GALLERY: 200

B

WATERING HOLE: 300 C

SQUARE: 250

E D

ENTRANCE E: 250

MOUNTAIN TOP: 100

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G

WE MANUALLY APPLIED MATERIAL POROSITY TO OUR TRIANGULATED PANELS BASED ON CORRESPONDING ARCHETYPE RULE Based on the porosity parameter attached to each space archetype, we have applied varying levels of material porosity to each room.

We controlled the spread and radius of openings as well as the placement.

// V : 055

PANEL SHELL : CONTROLLED POROSITY APPLICATION

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Porous Viewports

Openess

Exit

“Courtyard”

Paths

Entrance

// V : 056 CIRCULATION

// V : 057 PANEL STRIPES VISUALISATION : OBJECT ID

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A11

A10

A07

A09

A12

A11

A08

A10

A13 A11

A14

A10

A15

A09

A08

H

G

A

A16 A28 A29 F

A30 A31

A

E

K

D13 D14

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D D15 D11 D16

// V : 058

D12

PANEL SHELL : EXPLODED ASSEMBLY SYSTEM

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A15

// V : 059 A13

A14 A12

A17

A07 A16

PANEL SHELL : ASSEMBLY DETAILS

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INITIAL FABRICATION LAYOUT:

ITERATIONS:

FINAL FABRICATION LAYOUT:

PANEL AMOUNT: 510 LARGEST PANEL COMBINATION: 27 SMALLEST PANEL COMBINATION: 1 AVERAGE PANEL PER STRIPE: 7.984081

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PANEL AMOUNT: 716 LARGEST PANEL COMBINATION: 22 SMALLEST PANEL COMBINATION: 1 AVERAGE PANEL PER STRIPE: 7.105307

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PANEL SHELL : STRIPING OPTIMISATION FOR ASSEMBLY

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22 23 Final Fabrication Layout: Panel Amount: 510 Largest Panel Combination: 27 Smallest Panel Combination: 1 Average Panel per Stripe: 7.984081

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PANEL SHELL : DIGITAL FABRICATION COMPONENTS

Interior Structure embedded in Foundation Plate[For Supporting Platform]

Structural Skeleton with suspended joints

Angle plate embedded in Foundation Plate[For erecting panels at the bottomedge]

Exo Skeleton Footing Pad

Reinforced Concrete Plates

Pavilion shell

Excavation Pavement and platforms

Platform structure Exo Skeleton Platform

Suspended Joints [For Panels above ]

Suspended Panels and Panels fixed on the Foundation are the starting points of Assembly

The pavillion structured as an entity as Panel strips supporting their adjacent one. The Exo Skeleton gives more rigidity.

Anchor bolts

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Interior Structure embedded in Foundation Plate

Foundation slab

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CONSTRUCTION SEQUENCE

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Beam connection detail

Panel to foundation connection detail

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Panel to panel connection detail

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GA SECTION & CONSTRUCTION DETAILS

Suspension to panel connection detail

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5m

1m

Cantilever additional support

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// V : 067 SOUTH ELEVATION

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// V : 068 NORTH ELEVATION

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// VI : REFLECTION

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This studio project utilises the serpentine pavilion as a testbed to explore generative and evolutionary design. During our design study process in the studio facilitated us to approach design theories in generative design. During the semester workshops were held periodically to help us research deeper into design processes as well as utilising digital tools, including evolutionary and optimisation grasshopper plugins as well as python scripting. This allowed more flexibility during our design process and different methods lead to slightly different deign solutions. During this studio we established a notion on a computational thinking process and how it could facilitate design. We established the ability to identify design problems and reach to the architectural solution according to the rules we set. With various outcomes produced, we were able to select and adjust our design outcomes. In our research, we were focusing on the spacial sequence and the pattern applied to spaces. Incorporating the generative design process, we were able to get the high performance results, which is to our definition the high contrast of spatial sequence, optimised surface area and applied spatial pattern. The generative tool helped us to arrange and compare to reached the optimum decision. However, the decision made by ourselves does not align totally with the most optimum geometry. The way of balancing between different success measures is a decision that us as architects should make.

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// VII : BIBLIOGRAPHY

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Buchanan, R. (2007). ‘Strategies of Design Research: Productive Science and Rhetorical Inquiry’. In: Design ResearchNow. Ed. by R. Michel.

Board of International Research in Design. Birkhäuser Basel, pp. 55–66.

Cullen, G. (1961). Townscape. London: Architectural Press.

Downton, P. (2003). Design research. RMIT Publishing.

Frayling, C. (1993). ‘Research in Art and Design’. In: Royal College of art Research Papers 1.1, pp. 1–5.

Glendinning, Miles. (2019). SC_1141948.png, Chapel Street and Skene Street, Section 2,Kidd Street, 1983 [image]. University of Edinburgh.

Edinburgh College of Art. Jonas, W. (2007). ‘Design Research and its Meaning to the Methodological Development of the Discipline’. In:Design research now, pp. 187–206.

Mark, R. (1972). The Structural Analysis of Gothic Cathedrals. Scientific American, 227(5), 90-101.

Theodossopoulos, D. (2004). Technology and geometry in the design of gothic vaults in Britain. Proceedings of the 2nd International Congress on Construction History. Cambridge. pp.3079–3095. Construction History Society.

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Allen, S. 1996. Field conditions. Architectural Design, 66, 21-21.

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