Alex Kendall, Ben Miller, Yaseen Bhatti
Thesis Statements Problem The contemporary urban environment is characterised by rapid change. Unless resilient systems are developed, factors such as the Climate Emergency will bring widespread disruption and unrest.
Theory Building on the ideas of Researching For Design we are looking at Resilience theory and Generative Design Theory derive answers which combine understanding of mathematics, engineering and the natural environment to create solutions.
Solution Computational design tools will allow us to generate architectural interventions for tackling issues centred around rapid change in our urban environment.
Contents Context 4 Brief Analysis
19
The brief is to explore design methods through the creation of a pavilion for the serpentine gallery.
Site Analysis 25 The Serpentine Gallery sits at the heart of Hyde park in London, and is therefore a perfect location to explode new design ideals.
Methodology
31
Exploring the “how� behind the design process what is needed to fulfil a requirement of contextual knowledge, and how can looking into how we design affect the route taken.
Applied Method 49 Focusing on the Genetic Algorithms used to design and looking at how this can both expand and limit the creativity of the designer.
Detailing
100
How does a building designed in an digital design space support itself once real world forces begin to effect it?
Appendices and Bibliography
109
Problem
Problem
Design System #1
Design System #2 Design System #3
Solution
Solution
Research Into Design
Project Area of Research
Research into: History Aesthetic Perceptual Theoretical Perspectives Social Economic Political Ethical Cultural etc.
Project Area of Research
Project Area of Research
Reference Materials
Research Through Design
Research into design is the most common form of research, used in PhD thesis and dissertations. Focusing on researching existing relevant elements and has many useful methods and approaches to follow.
Portfolio Design Approach
Although difficult to define as Research the building of ideas from previous iterations can be considered an element of design. The simplest form of this creation of a pot that is superior to a previously created pot.
Research For Design Focusing less on the Solution as an isolated element instead as an accumulation and representation of the method used to reach this particular outcome. Therefore there is a shift in focus from the outcome itself and more looking and representing the process as a whole.
Pavilions And Problems
Current Model
Traditional Pavilion Testbed Taught Knowledge
Taught Knowledge
K n ow l e d g e
K n ow l e d g e
No t h i ng L e arn e d
Known existing Typologies
Untested Architectural Concept
Pavilion Testbed
What is a pavilion The Contemporary Pavilion Traditionally the pavilion was designed to be used as a testbed for new ideas and experimentation, but the idea of a pavilion has become diluted. Currently, the pavilion is more about art than architecture. Pavilions are no longer being used to test ideas and feedback into the profession, instead area repeating what has already been shown.
Predictable Pavilion
Proven idea
Failed Test
A pavilion should be “not predicated on the difference between art and the world, but rather facing their profound imbrication� Sylvia Lavin
3.00 2.75 2.50 2.25 2.00 1.50 1.25 1.00
365 Majority
0.75 0.50 -0.25 -0.00
UK Climate Debates
-0.25 -0.50 1960
1970
1980
1990
2000
2010
2020
2030
2040
2050
2060
The Climate Crisis Global Temperature Rise The Climate Crisis poses hardly imaginable existential repercussions for our planet and species if sufficient measures are not taken to resolve our exploitative and destructive relationship with our natural environment. Meanwhile Far-Right Nationalism is being elected across the world from Brazil to India, from the UK to the US. These leaders are defying any counter climate change action and protecting the big business that frequently finances them and who are also causing the vast majority of the damage.
Designing Resilient Architecture
Engaging People with the Problem
Rising global temperatures are already causing extreme weather events and disruption throughout the globe including:
Developing Resilient architecture has no impact if there is no will to use the findings.
Rising Seas Rising Temperatures Increased severe weather Widespread Desease Crop damage and food shortages
Ί
Measures must be taken to cope with this, as well as to control our relationship with resources to create more resilient systems. Resilience Theory may contain answers to these issues, and this is what we want to explore.
Potential
1950
Îą
r
Recent events, such as the UK General Election, have demonstrated that there is a lack of prioritisation from the public on this issue. With others taking the forefront, the stance elected is that of silence. However Climate Action, such as protests and strikes have made substantial headway in bringing this to peoples attention. A pavilion is a public space and an opportunity to explore topical issues. It is also an emotive experiential environment.
K
Ί Connectedness
Through this Climate Action can be taken to engage visitors with the issues that drive our design.
Design Systems
Algorithmic Rule Set
Traditional
Structure
Known material Perameters
Inorganic Speciation Process and Form ‘Explicit and open-ended negotiation between multiple architectural intentions and the complex milieux within which they will operate’ ‘Architecture, seen in this way, is neither a neutral substrate, mapping external influence without resistance, nor is it an inert exercise in the explicit production of shapes without reference to their environment. Instead it is an active participant with agency across multiple domains. Algorithmic techniques enable non-hierarchical, non-linear and explicit negotiations between an enlarged set of architectural intentions.’
Material
Program
Known existing Typologies a f
b
e
c
Valid Outputs
d
Predictable Outcomes
Final Outputs
NATURAL SELECTION Natural processes develop unique organisms which are each adapted to the functional requirements of the environment they occupy. This is attained through a generational process of Selection, Breeding and Mutation. Genotype Phenotype Environment Genotype Phenotype Genotype Phenotype Environment Environment
Generative Design Learning From Nature The natural environment offers a vast body of information on how problems arrising have been resolved. we can study how design occurs in nature, and the process that natural evolution takes in arriving at its final design solutions.
X Y Z
Exploitation // Exploration One of the most successful elements of natural design is its unique ability to negotiate the trade-off between exploitation and exploration.
MIN
X Y Z
MIN
MAX MAXMIN X Y Z
MAX
In Nature
GENOTYPE
PHENOTYPE
ENVIRONMENT
The encoded information or DNA of an organism.
The physical expression of an organism.
The context the forms the organism.
Unknown systems must be explored to gain understanding. However when a system is partially known, deciphering whether to optimise knowledge or seek more can be hard to determine.
Exploitation Selection
Breeding
Mutation
Using existing information to find the optimum.
Exploration Selection
Breeding
Random development knowledge.
In Design
Mutation
of
new
Genotype Phenotype Environment Genotype PHENOTYPE Phenotype Environment GENOTYPE ENVIRONMENT Genotype Phenotype Environment MIN MAX MIN MAX X MIN Y MAX X X Y Z Y Z Z
The parameters of a design.
The physical representation of these parameters.
The
context of design.
the
Neural Networks Neural Networks are compositions of a huge number of simple calculations which have the ability to create highly complex non-linear functions. They are structures of input parameters. These have been successful at modelling many real-world systems.
Rule of thumb
Unexpected yet high performing
Hidden Layers
Input Layer
Expressionistic
Output Layer
Population No
Pursuing “Fitness” Processes for finding favourable solutions Fitness defines the favourability of a system in achieving its defined purpose. The principals of the Selection Breeding and Mutation process can be applied to systems in order to find favourable solutions.
Fit
Yes
Generate random options within the Design Space
Selection
Crossover
Output
Breeding Approaches Mutation
Features such as the process of breeding are all also parametric within the system. A designer must decide on the level of inclusion versus elitism in order to avoid missing out on novel outputs or pursue extreme optimums within the Design Space.
Breeding Chance
Breeding Chance
Fitness
Generate mutations based on favourable qualities.
Breeding Chance
“Fitness” Complex systems tend to have a high number of fitness criteria. Often parameters therefore have non-linear or complex relationships with Fitness values. Sometimes these parameters collaborate, conflict or do not interact at all. Ultimately the overall best fit is decided by the priorities of the designer.
Parameter X
Compramise
Fitness
Breed favourable pairs.
Fitness
Choose options with favourable qualities.
Fitness
As designers we are striving for novel design solutions. However, we are also bound by general rules of thumb in order to achieve a guaranteed performance. Our aspiration should be to achieve a hybrid solution.
Fitness
Novelty Vs Performance
Parameter Y
Input Parameters
Design Optimisation
Available data sets are catagorised into 3 types
Methods of Data Harvesting
identified
and
A
Continuous values MIN MAX MAXIMISE
TARGET
Objective Functions
Goals are determined for the system, as to what ideal value is wanted for each data set.
While some simple optimization problems have direct solutions, most require the problem to be solved incrementally using ‘optimization algorithms’.
Discrete values B C
Constraint functions
==
<=
Extents of the data are determined to proscribe the limits to a feasible design space.
Genetic Algorithm START
Roulette selection of parents
Generate inital population
Crossover to produce children
Calculate fitness of individuals
Satisfy stop criterion
END
Deterministic Methods Input Layer
Hidden Layer
This process achieves a solution through the direct application of a series of defined steps.
Output Layer
Mutation of children
Direct Analysis
Gradient Descent
Exhaustive search
Calculate fitness of children
Heuristics (Rule of thumb)
Application: Finite Element Analysis A vertice based analysis of force distribution through a solid. These can be used to assess the functionality of structural systems andoptimise material distribution.
Calculate generation by “Elitism”
Genetic algorythms use a top-down external system that tests a given model to try to learn how it works from the outside in, to itterate and ‘evolve’ solutions.
Direct Analysis
Stochastic Methods Gradient Descent
Exhaustive search
Heuristics (Rule of thumb)
Monte Carlo (Random testing)
Stochastic Gradient Descent
Metaheuristics
Monte Carlo (Random testing)
Stochastic Gradient Descent
Metaheuristics
This process works by introduce some level of randomness while ‘searching’ for a solution.
Application: Fluid Dynamics An agent based system that simulates dynamics of nodes affected by geometry fields and other factors to see how they interact. These can be used for crowd flow or water flow simulations.
Permutation Sequence MINIMISE >=
Flexibility / Applicability
No Definitive Solutions
The process is efficiently able to handle a vast array of problems and applications. It can be rapidly adapted and reappropriated and produce objective bodies of solutions to problems.
The process is not designed to generate a single ‘Optimum’, rather to sift through and assess a vast body potential solutions based on the data provided. The designer is the ultimate creator and interpretor. The intent is to ensure the designer is more informed and guaranteed of being correct.
Forgiving & Maleable
Processing Limitations
The system will accept and calculate any scenario regardless of its depth, completeness, accuracy or validity. It can also be rapidly itterated and improved based on intuitive feedback.
When defining the Design Space a balance must be attained between fully describing the possibilities of a system and developing a system with too many inputs, making it too ‘heavy’ for a processor and taking long to complete.
Dynamic & Collaborative Feedback
Generally Quantitative Analysis of a genotype is completed on a numerical basis, quantitively. Although there are ways of interpretting qualitative elements, and this, once completed, allows for more time to assess Qualititive features, the system is never a full picture of the problem.
Systems can rapidly be written, adapted and improved. It also provides an intuitive feedback loop with the designer to be itterated. Itterations also rarely eliminate existing work, only evolving.
Computational Design
Pros
Cons
Variance
Bias
VS
Whats Tested
CONTEXT
ACUTE SHOCK
Continuous stress CONTINUOUS POSITIVE STRESS
SHOCK TO THE SYSTEM
NEW STATE OF EQUILIBRIUM
PAVILION
RESILIENCE CONTINUOUS NEGATIVE STRESS
OLD STATE OF EQUILIBRIUM
Systems PROCESS
Adaptation Scales
Resilience Theory Resilience Theory explores how systems limit, cope with and facilitate change. Biology and ecologies can provide answers on how to achieve resilient systems. ‘In nature, the biota and the physical environment interact such that not only does the environment shape the biota but the biota transform the environment.’ These systems adapt to change at all scales in different, non-linear ways. They are characterised by cyclical processes of Exploitation, Conservation, Release and Reorganisation.
Ecologies Potential
Systems of change
α
K
r
Ω Connectedness
CONTINUOUS VARIANT STRESS
System fails when external instances exceed the parameters of resilience
Calculated Constraints
Undefined Boundary
Catagorised
Diversity
Equilibrium Point
Maximum Resilient Range
New Factor Imposed
Temporary Instances Time
Equilibrium Line Begins To Deform
Maximised Efficiency
Equilibrium Point
Resilience Theory Engineered RESILIENCE In the creation of a resistant system, there are 2 approaches the first is engineered resilience, this focuses on understanding and preserving a current state. For instance building to withstand weather conditions within a specific expected range. This method maintains the efficiency of a function while under load and always aims to return a system to the initial point of equilibrium. The quality of this Engineered Resilience can be measured by 2 quantitative measures; a systemâ&#x20AC;&#x2122;s resistance to disturbance and, speed of return to the equilibrium.
Continuity Productivity Efficiency
Cons: Requires Control Vulnerability
Equlibrium Point
Redundency
Point of Equilibrium Begins To Shift New Equilibrium Point
Diversity Opportunity Persistance Cons: Unpredictability No Redundency
Equlibrium Point
Resilience Theory Ecological RESILIENCE An Ecological Resilient system is not constrained within an expected range of conditions but is instead an interconnection of adaptable systems. Ecology recognises the natural development and variation of a state with multiple equilibrium points and as part of a wider system. This maintains the existence of a system but not necessarily the function of the system itself. Ecological Resistance can be measured in terms of the magnitude of disturbance that can be absorbed before the system changes structure.
SYSTEM ACTIVE DESPITE DRIVER FAILURE
SYSTEM FLOWS DRIVER BREAKS RESULTING IN A SYSTEM FAILURE
SYSTEM DRIVERS
Resilience Theory
NEW SYSTEM STATE
FUNCTIONAL SYSTEM ACTORS
Defining system properties
DEFUNCT SYSTEM ACTORS
‘Human forsight and innovation can reverse trends [of exploitation and destruction and develop paths that sustain natural diversity and create opportunity.’
SELF HEALING
SELF ORGANISING
MULTI FUNCTIONAL
MULTI APPLICABLE
2019 Junya Ishigami
2018 Frida Escobedo
2017 Francis Kéré
2016 BJARKE INGELS
2015 Selgascano
2014 Smiljan Radic
2013 Sou Fujimoto
2012 Herzog & de Meuron Ai Weiwei
2011 Peter Zumthor
2010 Jean Nouvel
2009 Kazuyo Sejima
2008 Frank Gehry
2007 Kjetil Thorsen
2005 Alvaro Siza Eduardo Souto de Moura
2003 Oscar Niemeyer
2001 Daniel Libeskind
2000 Zaha Hadid
Past Serpentines An Overview Starting in 2000 the Serpentine gallery has hosted a pavilion each year designed by one of the leading global architects.
Pavilion 2011 Peter Zumpthor The timber structure includes a courtyard with garden designed by collaborator Piet Oudolf. The black panelled structure acts as a stage for the garden of flowers and light. The transition into the central garden, leads to a place abstracted from the world of noise, traffic and the smells of London
Pavilion 2015 SelgasCano Coloured ETFE material stretched over a more organic, less refined steel structure in two layers to create a variety of opacities and colours.
Pavilion 2016 Bjarke Ingels The ‘unzipped wall’ is a deconstructed plane modulated in a sine curve at ground level. It’s fabricated via a series of modular boxes made of enhanced GFRP, with open ends on their .
Pavilion 2017 Diébédo Francis Kéré A roof canopy hat generates a rain waterfall in its central courtyard. The harvested rainfall will irrigate plants. The structure is fabricated traditionally with motifs and colour of cultural siginficance to Kéré.
Existing Knowledge: existing material knowlage
Architects Statement What is the intent of the pavilion? Every year the designer of the serpentine pavilion as a change to explain their hopes for their pavilion, explaining the reasoning, context and meaning behind what they aimed to create.
The Climate Crisis is the current largest existential threat to humanity. Increasingly unpredictable weather events are already having severe effect as a direct result. In the UK, whole months worth of rain are falling in the space of days. We want to use our pavilion proposal as a testbed to explore how architecture can intervene to create systems resilient to these dramatic changes. In selecting this approach for developing our pavilion, we will return it to its original function; testing an idea to push the field of architecture forward. This will steer it away from the repetitive, commodified and superficial designs that currently saturate the typology.
Untested Architectural Concept: can a building be optimised using genetic algorithms to advance resilient building techniques?
This idea will also inform how we create the public space within our pavilion. Through an exploration of methods for developing emotive atmospheres, we create a spatial realisation that communicate to visitors the theme that have driven our design. Pavilion Testbed
Proven idea
Failed Test
Decentralised Systems The pavilion is designed as a fragment of a wider system. As such, failure would have limited impact on the wider issue.
Resilience Applied Steps Taken to Ensure a Resilient Design We made decisions throughout the project with a priority towards ensuring Resilience informed our decision making.
Redundency The pavilion is developed for extreme scenarios, meaning it has redundency instilled in its fabrication. The varying uses of the pavilion and the ability to recycle rainwater ensure this is still a valuable design decision.
Structurally Optimised
Replaceable Elements
Using Karamba we have engineered the structure to ensure it is definitely capable of coping with the most extreme water loading it could receive as a result of extreme weather.
The pavilion is constructed of smaller, easily refabricatable components that can be replaced if damaged.
MultiFunctionality
Ease of Fabrication
The pavilion serves a range of purposes, including a sheltered cafe, a piece of rainwater harvesting infrastructure, a piece of sculpture, a think-piece.
In line with our UN SDGs we have designed the structure to be easily fabricatable, but still requiring laymen work in order to avoid eliminating jobs as the fabrication industry transitions into being more automated.
Site Location
Regentâ&#x20AC;&#x2122;s Park Paddington Station
Hyde Park Buckingham Palace Natural History Museum Big Ben & London Eye
N
Tate Britain Gallery Victoria Station
Site Analysis: Macro Location & Nodes
The site for our pavilion lies in the city of ondon, more specifically yde ar . The diagram on this page illustrates ey nodes around the site on a macro level. This gives us an idea of population density and circulation routes indication for the par as a whole. As shown, the site resides right in the heart of the city and so the ondon water crisis will certainly affect these areas.
Key :
rimary Nodes
econdary Nodes
Location: London
Site Analysis: Meso Key Nodes & Circulation
The site is the serpentine pavilion, indicated in the site plan below. The diagram illustrates the par s access points and circulation to and from these points, ey nodes and the site. The circulation allows us to assess the fre uency and density of pedestrian flow through the par towards the site. This will eventually influence out form to develop a footprint that is more considered towards access etc.
N
Park Access Points
Local Underground Links
Key Nodes in Park
Circulation line Key :
Circulation in ar
Circulation to ar
Serpentine Pavilion Site
N
Sunlight Hours in June
Sunlight Hours in August
Sunlight Hours in October
une is the first month the serpentine is open, and is the brightest months of the year.
August is the midpoint for the serpentine pavilions opening time of the year.
ctober is the last month the serpentine is open. It is the dar est month, yet most of the site remains well lit.
Serpentine Gallery Highest density of pedestrian flow (based on the meso analysis)
Sunlight Hours Key : 0H
1H
2H
3H
15H
Park Access Points
N
Site Analysis: Micro
Access & Environmental Analysis The serpentine pavilion site sits within the bounds of the serpentine gallery site. The circulation is based on the access points and the meso analysis to find which access points would receive a higher proportion of visitors flowing through. It is simple, but can be manipulated with a complex pavilion intervention. The solar analysis also feeds directly in to form, allowing us to then consider our intervention with all information re uired to consider.
Serpentine Site Sun Path The sun path illustrated to the right represents the sun positions at all times of the day between une and ctober. Coupled with the shadow analysis this allows us to consider spacial ualities induced by our form.
Isolated Events 2010
2011
2012
8 Events in 1 year
2013
2014
2015
2017
2018
2019
2020
Average Daily Rainfall (mm)
Rainfall (mm)
130 120 110 100 90 80 70 60 50 40 30 20 10 0
2016
Time /years
2018
70 60 50 40 30 20
December
November
October
September
August
July
June
May
April
March
0
February
10 January
A wonderful serenity has taken possession of my entire soul, like these sweet mornings of spring which I enjoy with my whole heart. I am alone, and feel the charm of existence in this spot, which was created for the bliss of souls like mine. I am so happy, my dear friend, so absorbed in the exquisite sense of mere tranquil existence, that I neglect my talents. I should be incapable of drawing a single stroke at the present moment; and yet I feel that I never was a greater artist than now. When, while the lovely
Temperature (oC) 20 18 16 14 12 10 8 6 4 2 0
2020
Average Daily Rainfall (mm)
An Increased Risk of Rain
Rainfall Average (mm)
Rainfall (mm)
Severe Weather Events
Average Climate Data
130 120 110 100 90 80 70 60 50 40 30 20 10 0
2019
City Water Management
Drainage & Flooding
How a city manages its water can have a dramatic effect on the way the city functions efficiently. Surface or Ground Water Pavilion
City
London’s Water Crisis Applying Resilience theory As a coastal city on an island nation its assumed that London has water in abundance. However, in fact, due to unresilient infrastructure and drainage, the city faces crisis on multiple fronts.
Industrial Wastewater Treatment
Water Recycling
Domestic Water Treatment
Industrial Water Treatment Industrial Trade
Urban foundation construction decreases the permeability of the earth for run off of water.
Waste Water Treatment Plant
Resource recovery Biosolids and Biogas
Centralised vs De-centralised
Increase in population and loss of green space
The Victorian system of water control in London, built for 2.5 million people, is stretched to failure. As a centralised system, this affects far more people than otherwise. In contrast when a de-centralised fails minimal areas are affected.
Population
Possible Responses
‘Loss of green space, growing population and ageing water pipes are putting the capital at an increased risk of flooding and drought.’ - Leonie Cooper
Green Space
Water systems should be more decentralised to avoid major disruption. Rainfall and flood water needs restrategising to offset existing issues within the city. As conditions escalate, efforts must be made to predict and pre empt major disruption.
Water wasted
Water available
Centralised
De-centralised
Time
Decentralised Systems The pavilion is designed as a fragment of a wider system. As such, failure would have limited impact on the wider issue.
Resilience Applied Steps Taken to Ensure a Resilient Design We made decisions throughout the project with a priority towards ensuring Resilience informed our decision making.
Redundency The pavilion is developed for extreme scenarios, meaning it has redundency instilled in its fabrication. The varying uses of the pavilion and the ability to recycle rainwater ensure this is still a valuable design decision.
Structurally Optimised
Replaceable Elements
Using Karamba we have engineered the structure to ensure it is definitely capable of coping with the most extreme water loading it could receive as a result of extreme weather.
The pavilion is constructed of smaller, easily refabricatable components that can be replaced if damaged.
MultiFunctionality
Ease of Fabrication
The pavilion serves a range of purposes, including a sheltered cafe, a piece of rainwater harvesting infrastructure, a piece of sculpture, a think-piece.
In line with our UN SDGs we have designed the structure to be easily fabricatable, but still requiring laymen work in order to avoid eliminating jobs as the fabrication industry transitions into being more automated.
Atmospheres
Planet Fossil Fuels Protest Environment Destruction Extreme Consumption Chaos Dire Ice Caps Energy Sustainable Emergency Green Flood
Wildlife
Earth Climate Change Production Ecosystems Nature Development Resilience Urbanisation Extreme Emissions Drought Catastrophe Renewable ForestďŹ re Rising Heatwave Destruction CO2 Ecology Hurricane
Climate Change Evoking Spatial Quality Beyond itâ&#x20AC;&#x2122;s potential as a piece of resilient infrastructure the pavilion is fundamentally a Public Space. We wanted to explore how the idea of Climate Change and the notions of apprehension, innormity and dramatic change that that evokes in people in the way we orchestrated the Atmosphere of our design.
Global
Ozone Apprehension Weather Capitalism Heatwave Extinction Devastation
Material Tectonics Hardness Texture
interior / Exterior Tension Connectivity with wider world Perception of transition between inside and outside Detachment from environment or element
Finish
Atmospheric Design
OPENNESS
AND MOVEMENT
Defining a Spatial Intent Whilst elements of our design are highly function orientated, the spatial quality of our pavilion is also a key contributor to our design.
PATH FINDING
Anxiety
Following reading Silvia Lavinâ&#x20AC;&#x2122;s text on Pavilions we agreed that investigating the interplay of Art and Architecture would yield a more relevant proposal. We used Peter Zumpthorâ&#x20AC;&#x2122;s text on atmospheres to explore how he develops architectural environments.
Calmness
CONTEXTUAL CONNECTION
Case Study: Atmosphere Antoni Gaudi - Sagrada Familia We profiled some renound architectural environments for how they orchestrate their atmospheric conditions. The Sagrada Familia was a prime example of the level of scale and relationship with the natural environment that we wanted.
Case Study: Atmosphere daniel Liebskind - The Jewish Museum We profiled some renound architectural environments for how they orchestrate their atmospheric conditions. The Jewish Museum was another key example. We admired the way the space conveyed the level of enormity and distress of the events it is associated with. The control of space, the use of jutting intersecting geometry and the selection of coarse textures, we assessed as key to this.
Anxiety
Calmness
Case Study: Atmospheres Stanley Kubrick - The Shining Our case studies extended beyond the architectural field, analysing how films evoke tension and intensity. The Shining is a good example, its set design creates confusion and loss of sense of place within the pavilion. Thereâ&#x20AC;&#x2122;s also a tension between the cold harsh outdoors and the hotel.
? Vast reverberating spaces
? Contrasting internal to external exposure
Long repetitive hallways with ini a a ďŹ n in
Perilous context
MOCA PAVILION Tom Wiscombe
Sharp, jutting and intersecting geometry
Changes of material and texture
Variations in route width
Pleat rises to figure Pleat
Advanced water-based polymer with Kevlar textile reinforcement
Interface
2D // 3D Tension
Changes in level
Reduced sense of relative scale and repetition
Playing with interior exterior tension
Presecriptive circulation, modulating from high shelter to low
Focus on contrast from high volume and views spaces to low
Atmospheres Applied Pavilion Scale Strategies With our research from our atmospheres exploration complete and our form optimised to create emergent conditions, strategies were employed to further orchestrate the experience of our pavilion in the design of the details and landscaping.
Distribute pools to give space back to site and generate a cooled environment within the pavilion
Water Mitigation Strategies
Volume of rain
Traditional Design
Resilient Design
Flooding
No flooding
Planned Flooding Slow Dispersion As a cities are large areas covered in primarily hard none porous surfaces it isnâ&#x20AC;&#x2122;t uncommon for heavy rain to cause short term localised flooding. Any method that can slow the rate water is released into the cities system will have a dramatic effect on the chances of flooding within the city.
ROTTERDAM: AN EXAMPLE In the city of Rotterdam a lot of land has been reclaimed from the sea and for this reason most of the city is susceptible to flooding with sudden heavy rain, it is therefore important to slow the rate of water reaching the sewer system. One of the methods of storage is through sunken park areas that are used normally 90% of the time but when required can store vast amounts of water helping to prevent flash flooding.
Form The form of the architectural intervention can be parametrised to control its effect at points through. Density
Tree Distance
Variance
Opening
Volume
Shelter
Building Distance
Supports
Porosity / Openings
Entrance
Light
Wind
Trees
Rain
Existing Paths
Levels
Boundary
Points of Interest
Capacity
Space Volume
No. of Spaces
Entrance size
Route Length
No. of Routes
Shelter
Free Space
Context The attributes of the site context can be measured and manipulated as parameters to control the nature of the engagement of the user.
Agents The behaviour of agents can be influenced. Capacity can be varied and natural movement and stopping can be developed.
Input Parameters Isolating the input variables for parametric massing iterations When designing using a algorithm as a base the inputs that will make up part of this algorithm need to be considered carefully.
Project Specific The approach to our project, based on a theoretical, problem centric intention, provides its own unique parameters to control.
Water Capacity
Spans
Support Distance
Area
Tectonics
TARGET
Objectives & Constraints Primary Goals For the Design Defining goals relevant to our core design drivers and Methods for dealing with them I should be incapable of drawing a single stroke at the present moment; and yet I feel that I never was a greater artist than now. When, while the lovely
TARGET
Objective Functions
==
Constraint functions
==
MAXIMISE
MINIMISE <=
==
MAXIMISE
MAXIMISE
MINIMISE <=
MINIMISE <=
>=
>=
>=
OBJECTIVES & CONSTRAINTS
RESILIENCE
Water Collection Water Runoff Rate Water Distribution Total Footprint Structural Performance
ATMOSPHERE
Variance in Enclosure Variance in Internal Height Reduced wayfinding Vast expanses Reverberation Coarse textures Structural Intensity
GENERATIVE DESIGN
COMPUTATIONAL EXPERIMENTATION Water Simulation Structural Simulation
TRADITIONAL DESIGN
Catenary Structure
Catenary Surface Deciding the Base Form A Catenary Surface is an example of Extensive Curvature which has seen extensive use in Pavilion design. It relies on the Parabola, a shape found throughout nature and in structural systems.
Parabolas
Existance in Nature Structural Performance
Working With Gravity
A Catenary is based on a system of parabolas. This efficiently transfers load along the surface and to the ground.
A parabola is a naturally occuring phenomena. The force of gravity causes projectiles to follow this shape as they move.
Long Spanning
Water Run-Off Water falling on the structure will naturally run off the edges of the surface. The form will also support the water load.
Natural Suspensions
Water Collection
This shape features in natural structures such as spiders webs of silk, networks of radial thread cross connected by secondary threads which the spider can hang from. Gravity gives them this shape
Multiple sequetous Catenary forms will generate pools at their internal anchor points.
Minimised Weight
Robust Shells
The form of the structure is naturally materially efficient, distributing material along load paths.
The perfomativity of the form means that they have evolved iin nature. For example this tortoise shell exoskeleton robustly protects the tortoiseâ&#x20AC;&#x2122;s organs.
Proportion Collected
The pavilion is a public space which, especially on the rainy days in which the pavilion exhibits its potential, will require a large amount of shelter space.
Kg
Working With Water
More extensive structures with more internal anchors, well distributed, will collect a high proportion of the rain fall.
Boundary Anchoring
Full Edge
Catenary Surface
PROS
Converting Our Footprint to a Mesh Can we develop something to complete a structural Can we achieve complex Can we explore different grid systems.
functionally simulation. footprints
Kangaroo takes load, node and surface data from a script in order to output a structurally rational surface.
Parameters
Load Force
Edge Length
Mesh Density
Anchors
Limited Anchor Points PROS
Enclosure Form Flexibility
Views out Flexible Access
CONS
CONS
No Openings Irrational Mesh
High Exposure Form Limitation
Nature of Mesh Polygons
Process Explore ways meshes.
of
generating
Anchors
Mesh Experimentation Explore square and triangle based mesh constructions.
0
Generate boundaries from straight edged and curved edges shapes. Randomise anchor locations.
Goals
3
Can we achieve complex footprints Can we explore different grid systems. What density of mesh division is required.
1
Can we develop something to functionally produce a catenary surface.
Conclusion 7
Any polygonal geometry can be used by subdividing larger boundaries into polygons that do not self intersect.
10 13
Complex Meshes High Polygon and Line Based Anchors Experimentation was completed with increasingly complex base polygons, connecting sequences of varied scales, proportions and edge counts.
Process Apply mesh logic to larger footprints and more complex shapes. Generate anchors based on whole lines and curves.
Goals To achieve random anchor distribution across multiple polygons. To combine square and triangle based meshes. To work out how and to what extent lines can be used for anchors.
Conclusion Any polygonal geometry can be used by subdividing larger boundaries into polygons that do not self intersect. Line based anchors are created using adjacent anchor points from a mesh surface. This limits the nature of anchorage. However limited line based anchor processes can be completed.
Complex Footprints
Line base anchors were also explored, as apposed to point based ones. These rely on a sequence of adjacent points from the mesh.
Line Based Anchors
Generative Design Process
Generative Design Process Optimising Based on Project Criteria A catenary surface can be parametrically generated and analysed to achieve project specific properties using a Generative Design solver. We generated a script in Grasshopper that incorporates our selected most important features and parameters of the design.
PARAMETERS
FITNESS CRITERIA Phenotype
Number of Edge Points
View Area Variance
Random Distribution Variant
Canopy Height Variance
Number of Internal Points
Maximum Height
Random Distribution Variant
Maximum Water Retention
Find max height
OPTIMISE As Close to Chosen Max as Possible
12.
8.
4. Divide footprint into grid
2.
1.
DeďŹ ne Building Footprint
Convert into uninverting polygons
9. (A - MEAN)2 + (B - MEAN)2 + Etc Number of Data Points
Select Random Edge Points
Cut Mesh Horizontally at Eye Height: Adults & Children
Select Random Inside Points Input Chosen Points as Anchors DeďŹ ne Canopy Extension
4. Sample a random number of edge and internal mesh points
OPTIMISE
Maximise Variance
Measure water runoff distribution 5. From the original footprint, divide into an equally spaced grid of points
6. Generate the catenary surface from the mesh and random anchor points
7. Calculate the proportion of rainwater retained on the surface
Calculate Variance
7.
Subdivide into panels
3. Subdivide the polygons into a triangulated mesh of panels
Run Views Analysis for each grid point
11.
Structural Simulation
Convert polygons to mesh
2. Subdivide this into uninverting polygons of similar area and proportion
Calculate Variance
5.
3.
1. Define a polygon footprint based on requirements for the site
10.
Measure height of mesh at each point
6.
8. Measure the height of the canopy at each grid point
9. Run a views analysis at each eye height, using the sections and context geometry
Calculate Proportion That Stays on Surface
10. Calculate the variance of canopy height and views at each grid point
11. Run an optimisation to balance variance of height, views out and rain retained
12. Optimise the catenary surface height for the most high performing genome
Avoidance of Leaf Fall 51%
61%
Positioning the footprint away from the base of trees, especially down wind, will avoid leaves gathering on the canopy.
44%
Site Usage %
Defining Footprint [1]
53%
41%
52%
Site access points. Positioning the polygon to create definite openings relative to key points for accessing the site.
Generating a Basis for Optimisation The base Polygon from which the pavilion catenary surface was generated was based on 6 criteria. Whilst some of these criteria are quantitative, many are qualitative. As such the generative design process is only partially relevant, but still informed our decision process.
In order to facilitate itâ&#x20AC;&#x2122;s rainwater harvesting function to the maximum potential the size of the footprint should be maximised.
The potential views variance. 42%
55%
46%
Fewer definite anchor points as a result of corners in the polygon will allow larger openings between them and therefore more potential variation in views out.
Variation in levels of shelter Shelter is another variable of variance defined by the distance you are from the edge of the canopy at any one point. The variation of this should be maximised.
53%
42%
58%
Optimum Site Locations The site was analysed and regions of optimum light and views were found. These areas have the highest potential variance so should be included in the pavilion.
Grid and Mesh [2-4] divide the Footprint into a Grid of points and a mesh
a minimum amount and in order to provide sufficient atmosphere analysis
We divided the footprint into a grid of points. These would be used for our analysis and also as locations to ditribute anchors to. 4m was shown to spread the anchors by
We also divided the footprint into a mesh surface. This was subdivided 12 times per triangle. This lead to a satisfactorily detailed canopy without overloading the processing time of the script.
2 - Convert to triangles
3 - Mesh Subdivision x12
4 - Grid Division 4m
Divide the polygon footprint into a 4m grid of points.
Select Internal Points [5]
Example 1
Example 2
Example 3
Example 4
Example 5
Example 6
Parameter 1 The first parameter selects the number and location of internal anchor points of the form. This is selected from the previously defined regular grid of points, to avoid them being within 4m of one another. These internal anchor points will serve two functions: •
Determine the location of pools of rainwater to collect on the surface.
•
Alter and vary the volumetric quality of the canopy at different points.
# of Anchor Points 1 3 5 7 9 11 13 Random Seed 1 2 3 4 5 6 7 8 9 10
We elected tohave a high range of internal anchor points, from 1 to 13, which would create a high variety of internal spaces. NOTE: A ‘Seed’ is a specific variation of the randomisation.
Select random points from this grid and then find the closest mesh point to those points.
These selected points inform where the catenary comes down within the boundary, creating more enclosed spaces and channeling water to the inside space.
These anchor points are definite, in order for the catenary to generate properly.
Select Edge Points [5] Parameter 2 The second parameter selects the number and location of edge anchor points of the form. All points are turned to a short line of 3 points to increase the area the water can runoff down.
Randomly Select 3 Consecutive Points of the remaining edge anchor points Example 1
Example 2
Example 3
Example 4
Example 5
Example 6
The existing corners of the footprint polygon already definitely have anchors in order for the form to work. The random edge points are selected from the remaining evenly divided points along each edge line. These are also a sequence of 3 consecutive points. These anchor points will have two functions •
To create more enclosure and obstructed views
•
To provide extra routes for the rainwater to dissipate and runoff down
# of Anchor Lines 0
1
2
3
Random Seed 1 2 3 4 5 6 7 8 9 10
We elected to limit the number of edge anchors to between 0 and 3 as beyond this reduced the water run off too much. NOTE: A ‘Seed’ is a specific variation of the randomisation.
These selected points inform where the catenary comes down at its edge, creating more enclosed spaces and channeling water to the ground.
Catenary Construction [6] Construction of the form
Load Force
Having selected our footprint, anchors and a base surface Kangaroo can take these defined elements and construct a rational surface to meet design criteria. Initially this is just a fixed hight but other factors can be imparted into the process.
Edge Length
Mesh Density
Anchors
Water Run Off [7] Fitness Criteria 1 - Maximised This criteria determines how much rainwater would run on to the surface and how much would run off the edge. It simulates droplets, calculating their runoff path and determining their destination.
Step 1 Populate the surface with 500 randomly distributed points.
Step 2 Each point will then calculate in what direction it needs to move in order to most efficiently leave the surface. This is indicative of gravity.
For each point, it calculates which direction it has to move to have the greatest increase in Z vector. It repeats this 100mm at at a time, until all points have a Z value of 0.
A proportion is generated between those staying on the surface and ending up at the internal anchor points, and those ending up at the edge. This value is maximised to increase potential retention of rainwater.
The water droplets either end up at the edge anchors or internal.
Step 3
The points, after 8000 vector movements, will accumulate at the internal or external anchor points.
Step 4 The points, after 8000 vector movements, will accumulate at the internal or external anchor points.
# Particles Retained 500 Total Particles Rainwater Retained
Canopy Height Variance [8] Fitness Criteria 3 - Maximised An Height measurement is run at each predetermined grid point under the canopy. These height values are then put into a Variance calculation to identify how much this changes throughout the pavilion. This variance is maximised.
For each grid point it measures a height up to the canopy surface
NOTE: View variance and canopy height are directly proportional to proximity to water bodies on the roof canopy. This mass of water will have an atmospheric affect also.
Views Area Variance [9] Fitness Criteria 2 - Maximised An Isovist is run at each predetermined grid point under the canopy, excluding those used as anchors for that simulation. The area that can be seen out to from this point is calculated.
NOTE: View variance and canopy height are directly proportional to proximity to water bodies on the roof canopy. This mass of water will have an atmospheric affect also.
average eye height for an adult and also a child. These area values are then put into a Variance calculation to identify how much this changes throughout the pavilion. This variance is maximised.
This is completed for a cross section of the canopy at the
Eg 1
Eg 2
The isovists measure how much of the wider site can be seen at any one point. 50m
100m
50m
100m
50m
100m
Measure area at each grid point at both child and adult height.
Eg 3
Serpentine Gallery
Site Trees
Above is an example of the section cut lines of the mesh at each height.
1.8m- Average Adult Eye 1.2m- Average Child Eye
Eg 4
50m
100m
VARIANCE Range
A + B + C + D + E + F + G + Etc.
Standard Deviation
ME Standard Deviation
Number of Data Points
Range
Variance [10] Calculating the diversity of qualities of atmospheres
Variance
(A - MEAN)2 + (B - MEAN)2 + Etc Number of Data Points
From our atmospheres research we have ascertained one of the key features of the space we want to create is maximised contrast and dynamic change throughout. This feature can be applied to most qualities of the space. It can also be quantitively calculated. To do this, all the values of views or height were inputted into the following calculation. VARIANCE
MEAN
High Range Low Mean Deviation
High Range High Mean Deviation
Low Range High Mean Deviation
Design Iterations
Design Priorities
Maximum Water Retention
Qualitative & Subjective Assessment Approach The final solution will be selected based from a pool of high performing genomes, but will be chosen based also on its aesthetic value and how well it fulfils the qualitative elements of the design also.
Limitations & Biases Features Not Addressed By The System With any system there will be a number of qualities not addressed. Compromises will also have to be made between thoroughness and processing time. The following key elements, plus many others which would have less impact, were considered carefully for their implication.
Data Aggregation For a large number of parameters the aggregation of analysis has been reduced to limit the processing requirements of the script and reduce runtime to a more reasonable duration. These were decided to avoid major inaccuracy from the overall system. Examples include: •
View Area directions: Views area was calculated every 12 degrees out from each grid point.
•
Mesh subdivisions: The pavilion mesh is subdivided 12 times per polygon. This had major reductions in failures.
•
Water runoff Points: Water run off was only calculated from 500 points.
View Area Variance
Resilience is the fundamental element of the project, with atmospheres fulfilling an important secondary role. As such, this will be a priority when selecting options.
Irrelevant Views Analysis Views analysis only occurred at points that were not used as anchors. However very rarely some view points still positioned above the canopy so were irrelevant. This was unavoidable other than to increase the general height of the pavilion to reduce the rate it did.
Views Analysed
Still Sometimes Above Canopy
Anchor Automatically Excluded
Canopy Height Variance
Incurred Pool Volume While the system can assess how much water would pool on the roof, it does not calculate the volume the roof can hold at any one location. Points too close to the edge were therefore disregarded. Visual assessment would also assist this, plus our geometric strategy.
Continuous Headway
Partially Randomised
Whilst a proportion of the canopy can be made headroom accessible, the system cannot be coded to ensure it is continuous and doesn’t create dead ends. Outputs will therefore have to be assessed visually to ensure the solution is viable for circulating.
2 of the 4 variables that define the anchors are based on randomisation. This is unavoidable in order to produce a variety of options without being prescriptive, when the number of solutions must be limited. This means the solver may struggle to find high degrees of correlation.
Dead end with no headroom
Proximity of Large open spaces to South
Visual quality of Form
Inferred circulation route interest
High Collection Rate Low Volume Capacity
High Collection Rate High Volume Capacity
Balance
Itterations
Analysing Options
Anchor Points (Internal) 13
Rapidly evaluating Footprint Geometry A constrained analysis of each footprint gave us a first indication of how these each performed based on a pool of 12 itterations each.
Random Seed 10
6 8 4
3
1
Height Variance MAX 9.2
View Area Variance MAX 280
Water Retention MAX 0.62
High Performers
Neural Network
Refined Footprint Selection 1
Neural Network
Refined Footprint Selection 2
From the previous analysis, we could refine our options down to 6. Decision on this was on a much more subjective grounds. However the data allowed us to have conviction that we were choosing an objectively strong option.
Anchor Locations 52% Site Coverage 62% Water Retention
High Performers The footprints with a low number of edge vertices performed very highly in terms of water retention. However they covered only a low portion of the site and were inarticulate in their form.
5.4
Height Variance
199.3 Views Variance 62% Water Retention
Anchor Locations 44% Site Coverage 63% Water Retention
5.9 Height Variance 195.2 Views Variance 63% Water Retention
High Performers
Neural Network
High Performers
Refined Footprint Selection 3
Refined Footprint Selection 4
This one had low site coverage, but avoided enclosing the footpath thorugh the site. It has high retention and very high views variance from its highest performer.
This option filled the site the most but as a result of its high number of anchor points it had a low water retention. It had high height variance potential becuase of its size also.
Anchor Locations 44% Site Coverage 48% Water Retention
7.2 Height Variance 239.9 Views Variance 48% Water Retention
Anchor Locations 61% Site Coverage 34% Water Retention
Neural Network
8.4
Height Variance
221.7 Views Variance 34% Water Retention
High Performers
Neural Network
High Performers
Refined Footprint Selection 5
Refined Footprint Selection 6
This option interfaced with the 3 entry points well. It had high water retention but low site coverage and low views variance.
This option had the second highest site coverage and a high water retention. It also interfaced well with the entry points well, with a high potential view and height variance. We also appreciated the aesthetic of the outputs.
Anchor Locations 41% Site Coverage 57% Water Retention
6.9 Height Variance 200.6 Views Variance 57% Water Retention
Anchor Locations 58% Site Coverage 55% Water Retention
Neural Network
7.0 Height Variance 242.2 Views Variance 55% Water Retention
58%
Selected Footprint Identifying the Base Footprint From these 6 options our final footprint was chosen, with the data in mind and the qualitative features appraised. It found a good balance of limiting the number of vertices whilst still filling a large percentage of the site. It also interfaced well with a number of entry points creating an enticing entrance to draw visitors in. Whilst this is not the most high performing on proportion of water retained, this was only by a small degree, plus, its large footprint will allow it to generally accumulate more water.
2nd Iteration Cycle
2 2
6.5
0
6.0
1
Most Water Retention
Sort by:
400
0.5
0.0
Water Run On/Off
0.1 350
1
15/01/2020
Design Explorer 2
Reset Selection Exclude Selection Zoom to Selection Save Selection to File My Static Link Tutorial (http://www.mpendesign.com/category/tutorial/) Services (https://www.thorntontomasetti.com/services/sustainability/) Info (https://github.com/tt-acm/DesignExplorer/wiki)
Number of anchors (Internal)
15/01/2020
Design Explorer Exploration Second wave of iterations Design Explorer is a system for documenting, visualising and evaluating design options. It is not a solver, instead capturing images and data for each option which can then be uploaded to an interface designed to explore the design space. As can be seen from the range of Anchor points and Seeds that were active in the neural network (Right) arbritrary decisions had to be made by us about limiting the scope of the design space. Design Explorer has no evolution mechanic so is not optimal for evolving solutions. It is, however, very efficient at visualising and ordering options. This system also starkly demonstrated the positive correlation between internal anchor points and performance.
Seed (Internal)
7
Number of anchors (Edge)
10
6
Seed (Edge)
3.0
8.5
2.5
8
Height Variance
5
4
Design Explorer 2
View Area Variance
550
8.0
5 2.0 500 6 7.5 Info (https://github.com/tt-acm/DesignExplorer/wiki) Reset Selection Exclude Selection Zoom to Selection Save Selection to File My Static Link Tutorial (http://www.mpendesign.com/category/tutorial/) Services (https://www.thorntontomasetti.com/services/sustainability/) 4 1.5 3 450 4 7.0 3 1.0 Number of anchors (Internal) Seed (Internal) Number of anchors (Edge) Seed (Edge) Height Variance View Area Variance 2 400 7 10 3.0 5 2 6.5 2 0.5 8.5 550 0 6.0 6 2.5 8 350 4 1 0.0 1 8.0 5 2.0 500 6 7.5 Sort by: Water Run On/Off 4 1.5 3 450 4 7.0 3 1.0 2 400 2 6.5 2 0.5 0 6.0 350 1 0.0 1 Number of anchors (Internal) Seed (Internal) Height Variance View Area Vari… Water Run On/… img Rating scid 1 Sort by: 1
Water Run On/Off
-1 3
9.171116
354.60162
0.186
Numberofanch… 0
0
11.899018
335.279197
0.096
Numberofanch… 0
3
1
6
9.251827
343.174129
0.134
Numberofanch… 0
6
1
10
7.411818
335.521255
0.144
Numberofanch… 0
9
4
-1
12.316014
538.761235
0.506
Numberofanch… 0
1
4
3
11.909349
500.461175
0.524
Numberofanch… 0
4
4
6
13.627861
512.733879
0.444
Numberofanch… 0
7
4
10
15.071996
536.292243
0.502
Numberofanch… 0
10
7
3
15.089412
629.012985
0.666
Numberofanch… 0
5
7
6
12.381195
591.738595
0.602
Numberofanch… 0
8
7
10
16.266078
612.323185
0.562
Numberofanch… 0
11
Setting L M S
Water Run On/Off
0.5 0.4 Setting L M S 0.3 0.2Run On/Off Water 0.5 0.1 0.4 0.3 0.2 0.1
tt-acm.github.io/DesignExplorer/
1/1
Anchor Points Random Seed Number of anchors (Internal) Seed (Internal) Height Variance (Internal) 1
13 1
-1
Anchor Points (External)
View Area Vari… Water Run On/… img
scid
Numberofanch… 0
0
Numberofanch… 0
3
0.134
Numberofanch… 0
6
0.144
Numberofanch… 0
9
538.761235
0.506
Numberofanch… 0
1
500.461175
0.524
Numberofanch… 0
4
15.071996 9.171116 15.089412 11.899018 12.381195 9.251827 16.266078 7.411818
512.733879 View Area Vari… 536.292243 354.60162 629.012985 335.279197 591.738595 343.174129 612.323185 335.521255
0.444 Water Run On/… 0.502 0.186 0.666 0.096 0.602 0.134 0.562 0.144
Numberofanch… img Numberofanch… Numberofanch… Numberofanch… Numberofanch… Numberofanch… Numberofanch… Numberofanch… Numberofanch…
7 scid 10 0 5 3 8 6 11 9
12.316014
538.761235
0.506
Numberofanch… 0
1
9.171116
3
10 11.899018
1
6
1
10
4
354.60162
0.186
335.279197
0.096
9.251827
343.174129
7.411818
335.521255
-1
12.316014
4
3
11.909349
4 Number of anchors (Internal) 4 1 7 1 7 1 7 1
6 Seed (Internal) 10 -1 3 3 6 6 10 10
4
-1
4
3
4
6
4 7
7
Random Seed Rating
613.627861 Height Variance 5
411.909349
10
4
0 Rating 0 0 0 0 0 0 0 0
500.461175
0.524
Numberofanch… 0
4
13.627861
512.733879
0.444
Numberofanch… 0
7
10
15.071996
536.292243
0.502
Numberofanch… 0
10
37
3
15.089412
0.666
Numberofanch… 0
5
6
112.381195
629.012985 591.738595
0.602
Numberofanch… 0
8
7
10
16.266078
612.323185
0.562
Numberofanch… 0
11
tt-acm.github.io/DesignExplorer/
1
10
Height Variance MAX 7.2
View Area Variance MAX 253.4
Water Retention MAX 0.56
High performance correlated with high internal anchor points.
1
1/1
Octopus Solution Solver Experimentation
Pros
The system maps outputs along 3 axes as well as colour gradients allowing the visualisation of numerous fitness criteria. The phenomes are also each represented in 3d within the matrix.
•
Has highly parametric mutation and elitism
•
More prone to crashing randomly
•
Visual exploration of design, relative to one another.
•
•
Allows you to select high performing Genomes and Phenomes to replicate.
Has a number of glitches including obscured data values in the visualisation.
•
Generates results quickly.
Evolution Parameters ce
rian
s Va
View
The mutation and elitism parameters are controllable within the system to vary the outputs visualised in the matrix.
Experiment 1 Each iteration during the experiment phase was run for 20 minutes to give an indication of what it would output. The first was completed with the baseline advised parameters.
Heig
ht V aria
nce
Elitism
n Water Retentio
3d Matrix Visualisation
Water Retention
Octopus is a multi-objective solution visualiser. It generates 3d geometry for a selection of the potential solutions, as well as documenting their fitness values for the objectives. It then visualises them on a 3d matrix.
Cons
Elitism
w
Marked Output visualised in 3d
Mutation Probability
Mutation Rate
Mutation Rate
Crossover Rate
Crossover Rate
Population Size
XXX
0.54
Vie
Mutation Probability
ce
an
ari sV
.52
186
.99
12
2
9.2
Population Size Diversify Parameters
2
He
igh
tV ari
an
200
ce
5.6
6
Data Ranges Water Retention: 61% View Variance: 271.9
0.16
Height Variance: 7.6
Mutation Probability
For the final run the time was not constrained, and the paramters were applied in two phases.
Mutation Rate
The first phase had higher mutation and lower elitism to explore as much of the design space as possible.
Crossover Rate
The second phase had high elitism to evolve higher performing solutions
Population Size Diversify Parameters
200
High Performing Phenome ce
an
ari sV
w
Vie 0.55
Best-Fit Geometry Selected
n Water Retentio
Final Selection
Elitism
Elitism
4
8.2
21
Conclusions
9.4
5
Mutation Probability
He
igh
tV ari
High Performing Genome
an
Mutation Rate
ce
Crossover Rate 7.0
3
Population Size Diversify Parameters
200
The final output was one of the highest performing on two fronts, with the maximum water retention which was a design priority. The system struggled to evolve solutions. If re-done, and if more computer power was available, a more advanced process for determining anchors would have been better.
Low Performer Minimum Fitness Values This option shows the worst of the options Octopus returned to us within its interface.
Data
Height
Views
Rain Retention
The low performer produced the following data. Water Retention: 6.2% View Variance: 144.54 Height Variance: 6.2
Analysis
Properties
It has low anchor points meaning it has limited potential for variance. It also retains almost no water.
The data output was severely low showing the range of the design options available.
Height Variance Views Variance Water Retention
Average Performer Median Fitness Values This option shows the worst of the options Octopus returned to us within its interface.
Data
Height
Views
Rain Retention
The average performer produced the following data. Water Retention: 31.2% View Variance: 175.2 Height Variance: 8.1
Analysis
Properties
This option created good height variance as a result of its low anchor points with a high amount of spacing. However it had low water retention.
The data output shows mediochre values, though still a considerable improvement from the previous. It showed insufficient enclosed spaces with a priority towards vast volumes.
Height Variance Views Variance Water Retention
Optimum Selection Selected and Articulated The optimum output was selected. The form was then regenerated with double the mesh subdivision. It was also reanalysed to much higher tolerances. Adjustments of the anchors based on aesthetic and improving the mesh properties concluded with the following outputs.
Data
Height
Views
Rain Retention
Once adjusted the data outputs changed. This was considered through the adjustment with live feedback to tailor the output to our requirements. Water Retention: 59% View Variance: 280.2 Height Variance: 7.3
Analysis
Properties
The anchors clustered together to create low level spaces in regions. Whilst there are very high volumes there is a high proportion of tightly enclosed space with substantial pool volumes above.
The final output showed substantial improvement from the original best output extracted through Design Explorer in order to select the footprint. This can be seen in the orange bars (Left).
Some anchors were adjusted inwards from the edge slightly to improve water capacity. The output was selected based on higher volume spaces being localised towards to South face with high light potential. The edge anchors were redistrbuted to aid this.
Large volume for cafe space.
The form inferred a long winding and dynamicly changing circulation route through the space, deviating inward and out to the edge of the canopy.
Height Variance Views Variance Water Retention
Landscape
Field Conditions Enriching Emergent Properties of the Pavilion Through the generative design process of inducing high spatial and views variance across the site, the system has generated maximums and minimums within the pavilion, that collaborate with the existing features of the site. Using the canopy geometry and properties, key nodes will be selected as stopping points in the circulation through the space and synthesising the legibility of the design.
Canopy Height MIN
MAX
View Area -ve
+ve
Selected Nodes Selected Anchors
Field Theory
Node Selection
Field theory relies on systems of organisation capable of producing vortexes peaks and protuberances out of individual elements. Gradients and transitions define the properties of spaces.
The nodes are selected to aid in amplifying the emergent properties of the pavilion. They are selected on a range of criteria.
Field Generation Each of these nodes will become critical features of the design, key spaces to pause in. Field lines generated by giving the nodes charges will inform our geometry.
Intensity of activity MIN
MAX
Flow towards Nodes WayďŹ nding and Focuses
Orchestrating Milieu Field Informed Elements and Landscaping The fields generated can inform the distribution and geometry of different materials and features throughout the site.
GSEducationalVersion
Pavilion
Without Solar Strategy
Opening 24th June
Environmental Optimisation
Midway 10th August
Closing 24th November
Base Analysis
The base solar analysis with the whole canopy, demonstrates the extensive dark environment within the pavilion, into which we can bring light.
Manipulating Natural Light
Panel Selection
We also used our fields outputs to manipulate the natural light penetrating into the pavilion. Our method for this was to replace opaque panels with translucent ones. This would visually communicate the channeling strategy of the canopy to inhabitants also.
We ran the Ladybug solar analysis, adjusting our roof panel selection in order to align changes in light and dark with the nodes of our fields.
Pavilion
With Solar Strategy
Opening 24th June
Midway 10th August
Closing 24th November
Environmental Section
Water Studies
Water Study Goals
X
SLOWING THE RATE OF DRAINAGE OFF PAVILION
ite saturation is a ma or issue to mitigate when considering rain induced flash flooding. This can be controlled with the nature of the pavilions s in, which will be considered as a test ‘surface’ for the sa e of this experiment. lood ris s can be reduced by slowing the rate of the rainwater draining off these ‘surfaces,’ and so this experiment aims to test shapes to formulate efficient strategies to slow the draining of water.
WATER FLOWING OFF SURFACE
PERMANENT WATER POOLING
In this experiment we want to avoid permanent water pooling as our primary research driven experiment is framed around the distribution of water, not the collection. ooling is still possible, however a strategy would need to be employed to eventually dissipate this as well. WATER-SATURATED GROUND
LITRES OF RAIN TO FALL
SITE SATURATION-LITRES
AVOID PERMANENT WATER POOLING
LITRES OF WATER TO DRAIN OFF PAVILION PER SECOND (RATE)
Fundamental Goal: slow the rate of drainage off our pavilion (l/s)
Water Study Overview Staged Process Summary
The diagram below illustrates the process of developing a strategy to slow the rate of water flow off a pavilion. The process begins with generating methods to calculate metrics to input and output. This in turn aids us to assess the performance of strategies we formulate to achieve our studies goal.
[1]
. et up raining channelling simulation
[2]
[5]
[6]
. Calculate input data for simulation
. Iterate basic strategies
. valuate best performing strategies
[3]
. Calculate output data for simulation to find performance
[4]
. efine perfromance with metrics
[10]
[7]
[8]
[9]
. use best performing strategies
. Iterate new fused strategy
. efine best performing strategy
. Apply best strategy to pavilion
[11]
. Test final pavilionâ&#x20AC;&#x2122;s performance
Water Study Animation [1] Particle Simulation For Rain
Water Study Methods [1] Conditions
STAGE 2 PARTICLE PROPERTIES CONSIDERED
MIMICKING WATER The properties of the particles are considered to create a grouped behaviour as close to water as allowed by the systems capacity.
PHYSICS ENGINE
STAGE 1 -
FRICTION
TESTING SURFACES
SMALL PARTICLE GENERATION
PARTICLE SYSTEM BREAKDOWN COHESION
TIME FACTORED
e will generate basic test surfaces to allow us to begin to formulate strategies towards our goal by generating metrics to assess.
COLLISION
FUNDAMENTAL FORCES
RADIUS
PARTICLE SIMULATION SET UP To achieve our experiment goals underlines previously, we are using a particle simulation system in order to replicate water flowing onto and off the surface. This will help us achieve our goal metric of rate of drainage off our pavilion.
STAGE 3 TEST SURFACES
STAGE 4 ANALYSE METRICS FOR FITNESS
Start
Stage 2 1m続
Water Study Methods [2] Input Metrics Stage 1
This experiment has a stage process in establishing our methodology to reach our goal. tage is the process in which we convert our particles in the simulation into a nown metric, which in this case is litres. The methods ta en to do this started with filling a m empty box with particles. As m is e uivalent to itres of water we can deduce that the amount of particles that fill the box is e ual to this. This then gives us a formula to convert particles to litres throughout the experiment.
STAGE 1 PARTICLES TO LITRES
1M続 BOX = 842 560 PARTICLES
X 842 560
In order to obtain and manipulate data for strategy generation, particles must be converted into a universal metric to analyse outputs and determine performance levels for each experiment. tage illustartes our methodology in converting the particles into litres.
FILL A 1M続 BOX WITH PARTICLES
=
1M続 BOX = 1000 LITRES
1L FINAL CONVERSION
842.56 Particles
To maintain accuracy and realism within the experiment, a recent metric from the met office is used to represent heavy rain. sing the metric conversion from tage , stage illustrates how we translate heavy rain in litres bac into particles as a metric to simulate as well as considering time in seconds.
Stage 3 1m
m
STAGE 2 -
2019 HEAVIEST RAIN = 32mm PER HOUR
. A TIC PER SECOND
X
60² ÷
0.0088L Per Sec Per Sqm
CONVERT HOURS TO SECONDS FOR EXPERIMENT TIME METRIC HEAVY RAIN RATE
h
m
Water Study Methods [2]
1m
DETERMINING HEAVY RAIN
m
1
1
=
1
A
Input Metrics Stage 2
IN
tage follows on from stage by using the conversion formula to determine the rate at which the experiment particles should be falling. sing weather data from the et ffice we can convert the most recent metric for heavy rain the into a particle format, so the experiment accurately resembles heavy rain. This completes the translation from particles to litres and bac again to produce ‘rain.’
Water Study Methods [3] Output Metrics EXPERIMENT COMPLETE : APPLY STRATEGY TO FORM
INPUT HEAVY RAIN METRIC
REPEAT STAGE 3 TO ITERATE STRATEGIES UNTIL OUTPUT METRIC IS OPTIMISED (MINIMISED)
TIME ELAPSED (SECONDS) - t
STAGE 3 -
00 : 10 : 00
PRODUCING SIMULATION OUTPUT METRIC
STAGE 4 TESTING STRATEGIES
BREED HIGHEST PERFORMING STRATEGIES
PARTICLES BELOW LOWEST POINT (LITRES) - x
RUN 10 MINUTE SIMULATION
To test the result for ‘performance’ an output metric is re uired. tages illustrate the process we underta e to ac uire and assess this metric to produce an optimal water channelling strategy
+
GENERATE BASIC STRATEGIES (SKIP STEP AFTER ITERATION 1)
INPUT PARTICLE TO LITRE METRIC
OUTPUT METRIC : LITRES PER SECOND TO FLOW OFF SURFACE (x ÷ t)
ASSESS HIGHEST PERFORMING STRATEGIES
Identifying Bias - Surface Size
To remove the bias from the experiment, the output is ad usted to consider the ratio between the rate of flow per second and the input surface’s footprint area, this is our revised output metric
Adjusted Output Metric: LITRES PER SECOND PER M² TO FLOW OFF SURFACE (l/s/m²)
÷
1m²
RATIO OF RATE OF FLOW TO SURFACE AREA
1m
MORE FLOW OFF SURFACE (L) LARGER FOOTPRINT SURFACE AREA
Water Study Methods [3]
IT
Eliminating Bias
Although our experiment’s main goal is accomplished following our methodology, it holds a bias against larger test surfaces. arger surfaces re uire a larger area for rain to be distributed, and as such more particles are involved with that particular experiment. This results in a larger final output for larger surfaces and smaller ones for smaller surfaces. This bias detracts from the goal of developing high performance strategies to deal with heavy rain. The diagram to the right illustrates the bias and how we overcame it by creating a ration based on si e and the original metric of rate of flow off the surface in litres per second.
=
A TIC ‘ AININ ’
1m
=
The experiment’s output metric is currently bias to surface footprint si e. The larger the surface the more rain will fall onto it, and so more litres will flow off per second. A strategy must be formulated to maintain consistency to focus the experiment solely on water channelling.
Eliminating Bias - Standardising Size
Final Data Output :
0.93 ml/s/m2
Low Performance
Millilitres to Flow off Surface per Second per m2
Final Data Output :
High Performance
Low Performance
edium igh erformance
LITRES OF RAIN (TOTAL)
2.64 ml/s/m2
Higher Final Data Outputs are Lower Performing
High Performance ow erformance
LITRES OF RAIN (TOTAL)
MILLILITRES TO FLOW OFF SURFACE PER SECOND
MILLILITRES TO FLOW OFF SURFACE PER SECOND
igh
71.7 l
55.1 l
edium alue
edium igh alue
BASIC SLOPE
UMBRELLA 37 ml/s
81 ml/s
edium igh alue
ow alue
TIME ELAPSED (Minutes)
TIME ELAPSED (Minutes)
FOOTPRINT SURFACE AREA (m²)
FOOTPRINT SURFACE AREA (m²)
10 Minutes
10 Minutes
tandardised alue
tandardised alue
40.24m2
edium alue
Water Study Methods [4] Defining High Performance Data
Now the metrics and methodology for conducting the experiment are established, it is important to display the data produced in a uniform template to visually and numerically compare strategies. This example illustrates a medium high performing strategy of a basic slope. The final data output re uires minimising maintaining above to achieve high performance. This is due to our goal of slowing the rate of water flow off generated strategies down.
30.63m2
edium ow alue
Water Study Methods [4] Defining Low Performance Data
This example illustrates a low performing strategy, modelled after an umbrella. This strategy clearly allows water to flow off it at a much faster rate than the slope example to the left. This is why higher final data outputs represent lower performances. The remainder of the data is present to visualise all the inputs of the experiment established in the methodology, and understand how these values culminate to produce the final output.
Breed Best Performing Strategies
2.16 ml/s/m2
0.903 ml/s/m2
0.578 ml/s/m2
0.375 ml/s/m2
Roof & Gutters
Branches
Half Pipe
Valley
ow erformance trategy
ow
edium erformance trategy
edium ow erformance trategy
edium erformance trategy
2.66 ml/s/m2
0.261 ml/s/m2
0.313 ml/s/m2
0.093 ml/s/m2
Cone
Meander
Stepped
Spiral
Water Study Iteration 1 [5] Basic Geometry
As stated in the methodology, testing basic geometry for performance is our first step towards creating a water channelling strategy. The data graphs establish these performance levels. As the piral is the highest performing strategy this will be ey in the breeding process. The stepped and eander strategies also perform well and so will be factored in with less weight. ther strategies are not carried through but may have merit down the line.
Key :
ow erformance trategy
2nd Tier Breeding Candidate trategy
Top Tier reeding Candidate trategy
ow erformance trategy
edium igh erformance trategy
edium igh erformance trategy
igh erformance trategy
13.8m
Path Length
Performance
0.261 ml/s/m ml/s/m2
0.20 ml/s/m2 28.1m Path Length
Increase Path Length
Improved Performance
1.73m
Height & Path Angle
Performance
Ad usted utput
etric
0.186 ml/s/m2 0.87m & 5° Height & Path Angle
Meander Evaluation [6]
Decrease Height & Path Angle
9.78°
High Performance Attributes
Graph Key :
Median Angle riginal Attribute ata
New Iteration Data
etric
Improved Performance
45.3° Performance
The meander strategy was determined to be a high performing strategy in iteration . e must now deconstruct the attributes of this shape to assess what contributes to high performance and what doesn’t. As seen in the diagram the change of these attributes produces a differing output metric. All of these altering attributes produce a better performing strategy when increased or decreased. This will be factored into the breeding process.
Ad usted Attribute
0.17 ml/s/m2 26.1°
Median Angle
Decrease Median Angle
Improved Performance
7.41m
Path Length
Performance
0.313ml/s/m2 0.313ml/s/m
0.08 ml/s/m2 19.4m Path Length
Increase Path Length
Improved Performance
1.83m
Height & Path Angle
Performance
Ad usted utput
etric
0.111 ml/s/m2 1.47m & 15° Height & Path Angle
Stepped Evaluation [6]
Decrease Height & Path Angle
18.2°
High Performance Attributes
Graph Key :
Median Angle riginal Attribute ata
New Iteration Data
etric
Improved Performance
45° Performance
The stepped strategy was determined to be a high performing strategy in iteration . The change of the strategy’s attributes produces a differing output metric. These altering attributes either improve or retain the output metric, in this case, the best performing iterating of the experiment yet is attained. This will be factored into the breeding process.
Ad usted Attribute
0.313 ml/s/m2 45°
Median Angle
Decrease Median Angle
Same Performance
20.0m
Path Length
Performance
0.093 ml/s/m2
0.20 ml/s/m2 23.2m Path Length
Increase Path Length
Decreased Performance
2.29m
Height & Path Angle
Performance
Ad usted utput
etric
0.098 ml/s/m2 3.23m & 8째 Height & Path Length
Spiral Evaluation [6]
Increase Height & Path Angle
5.92 째
High Performance Attributes
Graph Key :
Median Angle riginal Attribute ata
New Iteration Data
etric
Similar Performance
9.25 째 Performance
The spiral strategy was the best performing in iteration . As seen in the diagram the change of these attributes produces a differing output. In this case the increase of these values results in a worst performance, suggesting the spiral assessed in iteration is already near optimal for its typology. oving forward this means the properties of the spiral should be adopted without the literal implementation of the typology.
Ad usted Attribute
0.13 ml/s/m2 23.9째 Median Angle
Increase Median Angle
Decreased Performance
Input Best Attributes
Height
Median Angle
Median Angle
Height
Height
Path Angle
Path Angle
Path Length
Path Length
Input Primary Attributes
Breeding Strategies : i1 [7]
Fused Strategy Output [7]
After generating the first generation iterations of the strategies, assessing their attributes and iterating to improve performance, the fusion process can begin. As the spiral cannot be improved by ad usting attributes, its primary attributes will be fused instead. The stepped strategy produces the best performing output metric, but it limited by its nature. The meander strategy can perform well when ad usted, and has much room to perform better with further ad ustments. As such the meander strategy will be the new primary strategy when fusing.
The resultant strategy from the fusion process resembles a â&#x20AC;&#x2DC;stepped meanderâ&#x20AC;&#x2122;. The attributes listed have been considered in formulating a new high performance strategy. These have been inherited from the meander and spiral strategies, manifesting in the form of a longer path length and a lower median angle. The stepped aesthetic also provides a higher path length to median angle ratio. As the spiral offers only its primary attributes, the path length is inherited. This new strategy becomes the new foundation for the nd iteration.
Fusing Attributes
Iteration 2 : Stepped Meander
Stepped
Input Best Attributes
Spiral : Cant Be Improved
Spiral
Stepped : New Highest Performing
Meander
Meander : Most Room To Improve
Final Strategy To Implement [9]
0.091ml/s/m2
0.093 ml/s/m2
0.092 ml/s/m2
0.069 ml/s/m2
Stepped Meander edium igh erformance trategy
Flattened Stepped Meander
Curved Stepped Meander
edium igh erformance trategy
edium igh erformance trategy
0.079 ml/s/m2
0.069 ml/s/m ml/s/m2
Mazed Stepped Meander
Tight Stepped Meander
Tight-Maze Stepped Meander
ery igh erformance trategy
ery igh erformance trategy
ighest erformance trategy
0.081 ml/s/m2
Water Study Iteration 2 [8] Strategy Development
To iterate the stepped meander, we have sub categorised typologies to assess performance. All typologies output high performance output metrics, so the rating system is ad usted to penalise lower performance more. The tight ma e strategy produces the highest perofrmance by a significant margin. This is our final strategy, so the next stage is to apply it to our pavilion and test to see if the pavilion perofrmance is improved.
Key :
ow erformance trategy
igh erformance trategy
ighest erforming trategy
Channelling Fins ‘Steps’
Tight-Maze Stepped Strategy Taking The longest downwards path with the most deviations.
Strategy To Pavilion Translation
Final Smooth Skin
Rigid Section: Untestable
Smooth Section: Testable
The rigid implementation of the strategy results in several block-like masses which is impossible to manipulate to test on. Therefore it must be merged into 1 complete mass for testing.
To manipulate the pavilion for testing its performance it requires smoothing. However when this is carried out, the steps ‘melt’ into the surface, leaving only the fins.
Applying the Highest performing strategy attributes to the pavilion form. The channel route is dictated by the positions of the anchors and peaks.
Optimised Form The form in which we apply the strategy to, which has been derived and optimised in previous sections.
Pavilion Implementation [10] Applying Strategy to Pavilion
Now iteration is complete and has outputted a highly performing strategy, we must apply it to the pavilion. To adapt the ‘tight ma e stepped meander’ strategy to function and perform highly for the pavilion, we must ta e its attributes and apply the same logic to extract channelling routes for the water. There are points which we want for channel water towards anchors from all points on the pavilions s in.
Final Integrated Pavilion [10] Modelling Constraints inalising the strategy onto the pavilion is a complex process re uiring the use of multiple modelling tools. As illustrated above, the rigid manifestation of the strategy is not testable, and so re uires editing. This edit successfully merges all elements into one, rendering the pavilion testable. owever, by doing this the stairs dissapear, absorbed into the pavilion. This modelling constraint means we have to sacrifice clearly defined steps in order to complete the water channeling studies.
Strategised Pavilion - Post-Water Study
Plain Pavilion - Pre-Water Studies
Final Performance : 0.642 ml/s/m2
Final Performance : 0.774 ml/s/m2
Testing Pavilion [11]
Assessing Experiment Output Now a strategy has been developed and implemented, it is important to test the result, to determine if the study was a success or not. As expected, the strategised pavilion outperforms the standard catenary form significantly. In addition, considering the modelling constrains of the implemented pavilion, as it was not fully integrated, we may speculate that a genuine â&#x20AC;&#x2DC;steppedâ&#x20AC;&#x2122; meander translation could well be the optimum strategy. To conclude, this study explored many options of water channelling methods, to mitigate flash flooding caused from ground saturation. Considering our goals at the start, this study can be considered an overall success.
N
1:200 Plan
Base Structural Grid
Voronoi Grid
Karamba3D Analysis Structural Evaluation Karamba is a design tool used to evaluate and refine the structure of an element, this is particularly useful in none standard implication like the multi-domed structure of the pavilion. This allows the evaluation of where more or reduced structure is needed.
Structural Reduction Result of the karamba Analysis Starting with a standard square structural grid this is then attracted to the areas of high stress in the structure and a voronoi grid is then applied based of the deformed points. This system allows for dense structure where it is needed allowing of reduced structure where it could cause more stress than support.
Anchor Points
Recycled aluminium
Aluminium Is melted
6 m m Aluminium sheets formed
Sheets are cut using laser cutter
Making The Structure Folding structure to form The basis of the pavilions structure comes from cutting flat patterns from recycled aluminium panels. These flat patterns can be folded and joined together the create a composite 3d structure that provided adequate structural support without adding additional weight.
Patterns are cut from the sheets
The top clip allows the external panels to be attached Once the structure in connected to the ring the clip is tightened to hold the structure
Patterns folded to form structure
Water Strategy Implementation
High Rainfall is collected in the pools
Water leaves the pool at a rate that is equal to the average rainfall of the area feeding the pool per second
Pools Slowing the Release of Water Water flows off the surface and is collected in the pools, these pools slow the release of water into the ground water system during heavy rain to be water the covered area would experience under usual weather conditions.
Recycled ABS Plastic
Plastic is ground
Small pellets are formed
Pellets are melted
Forming The Panels Vacuum forming Surface By vacuum forming the panels they can be strong, lightweight and water tight whilst being made of recycled material. The recycled ABS plastic, the same as they use to make Lego, is strong, lightweight, long lasting, and slow to degrade making it a useful material for rood construction.
6mm sheets are produced
A sheet is heated above a form
A thin light weight but strong panel is produced
Foundations Designing for disassembly At the edge of the pavilion there is a high level of stress so a single point footing wouldnâ&#x20AC;&#x2122;t be adequate therefore expanding on the connection method for the structure, elongated clip location are attached to pre-cast concrete footings.
Edge footing have a boot to resist one sided lateral loading
Prefabricated concrete footings
Appendices and Bibliography
Workshops Software Introductions
Biomorpher
Design Explorer
A design visualising tool which evolves solutions. You can select prefered options and create generations of improving options.
An interface for visualising design options ordered in terms of their attributes.
Throughout the semester we completed a number of workshops with an array of software tools. This allowed us to be informed and selective about how we wanted to impliment the objectives of our project.
Galapagos
Octopus
Python
From these we chose the most appropriate options and ran our optimisation scripts through the solvers.
The native, 1 goal evolutionary solver for Grasshopper.
An 8 goal evolutionary solver with 3d matrix visualisation of outputs.
A coding language for implimenting more bespoke design features.
Presentations & Cross Atelier Feedback Responding to new ideas and advice
A number of presentations throughout the year showcased existing projects using generative design processes. This included systems for automating steel frame manufacture and optimising smart motorway systems.
Our cross atelier review was an opportunity to see how our narrative communicated to others. From this we adjusted our focus towards the generative design process. We also ensured our context was more rigorous as this was a focus from our discussion.
Galapagos Solutions Solver Experimentation 1
Pros
Galapagos is the native evolutionary solver for Grasshopper and also one of the oldest and most refined solvers available, named after the system of islands on which Charles Darwin developed his theory of evolution. It is a single objective solver which can efficiently output a specific single optimum based on the designers chosen criteria and settings.
Fitness Criteria
Cons
•
Native to Grasshopper
•
Single Objective Solver
•
Very Robust, efficient and with limited bugs.
•
Difficult to comparatively visualise multiple solutions
•
Highly controllable breeding and mutation parameters
•
•
Well visualised selection and iteration process.
Sometimes struggled to process solutions - has limited features for reducing extent and complexity of process.
Evolutionary Solver FITNESS CRITERIA
The fitness criteria for the script had to encompass the 3 we had opted for for our design (Reference stage 11 of the script), but through 1 value. The obvious solution to this would be to multiply the 3 values together.
2 - Canopy Height Variance
These values were divided by a maximum potential variable to eliminate bias.
3 - Maximum Water Retention
1 - View Area Variance
Max Stagnation
50 Population
50 Initial Boost
50 Maintain
Fitness
Maximum Height
5% Inbreeding
Annealing Solver
Limitations
1
This did not allow us to reference back and see what seperate parameters were high performing. It was hard to cross compare solutions on qualititive measures. As such we deemed Galapagos to not be the most applicable tool for our needs.
F
2
3
NET FITNESS VALUE
F
Temperature
100%
Cooling
0.95
Drift Rate
25%
Our simulation needed to Maximise the value. We used the above settings for population mutation stagnation and inbreeding. We ran it for 72 itterations, ie, the duration of a night.
? ? ? F
Solution
1
2
3
4
5
6
7
8
Itterations
75%
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
The system managed to increase fitness by a relatively high proportion.
System struggled to evolve into a more elite pool of solutions
GENOTYPE Number of Anchor Lines 0
1 3 5 Random Seed
1 2 3 4 5 6 7 8 9 10
Number of Anchor Points 1 3 5 7 9 11 13 Random Seed 1 2 3 4 5 6 7 8 9 10
PHENOTYPE
Elitism
Elitism
Mutation Probability
Mutation Probability
Mutation Rate
Mutation Rate
Crossover Rate
Crossover Rate
n Water Retentio
Population Size Diversify Parameters
200
Population Size Diversify Parameters
ce
an
ri Va ws
0.52
Vie 7
.5 11
2
9.5
2
He
igh
tV ari
an
ce
Experiment 2 More Elitism, Less Mutation For this run, the Elitism parameter was increased to a high level. This found a high performing option quite quickly and managed to improve on this a few times, but overall, based on the other experiments, the values were quite low.
Experiment 3 More Mutation, Less Elitism For this run, the mutation parameters were increased to a high level and the â&#x20AC;&#x2DC;Diversity Parameterâ&#x20AC;&#x2122; function was switched on. This meant the system would explore the highest variety of options but may not evolve from the best of them.
200
Bibliography Allan, S. (2008). From Objects to Fields. Carpo, M. (2012). The digital turn in architecture 1990-2012. Erioli, A. (2015). Intensive Aesthetics / Intensive Surfaces. Hollings, C. S. (1996). Engineering Resilience versus Ecological Resilience. Iain Maxwell, D. P. (2010). Inorganic Speciation. Kendall, A. (2019). Optimised Material Fabrication in the Digital Age. Lavin, S. (2012). Vanishing Point: Sylvia Lavin on the Contemporary Pavilion. Nagy, D. (2017). The Problem of Learning. Reiser, J. (2005). Atlas of Novel Techtonics. Zumpthor, P. (2006). Atmospheres: Architectural Environments - Surrounding Objects. Baan, I., 2015. dezeen - Serpentine Pavilion 2015 by SelgasCano photographed by Iwan Baan. [Online] Available at: https://www.dezeen.com/2015/06/22/serpentine-pavilion-2015-iwan-baan-london-selgascano/ [Accessed 02 10 2019]. Crow, H. +., 2011. dezeen - Serpentine Gallery Pavilion 2011 by Peter Zumthor photographed by Hufton + Crow. [Online] Available at: https://www.dezeen.com/2011/07/06/serpentine-gallery-pavilion-2011-by-peter-zumthor-photographed-by-hufton-crow/ [Accessed 02 10 2019]. Shepherd, J., 2016. Independant - Serpentine Gallery Pavilion 2016: Bjarke Ingelsâ&#x20AC;&#x2122; summer house unveiled. [Online] Available at: https://www.independent.co.uk/arts-entertainment/art/news/serpentine-gallery-pavilion-2016-bjarke-ingels-summer-house-unveiled-a7069446.html [Accessed 02 10 2019]. Stephenson, J., 2017. Area - Serpentine Pavilion 2017. [Online] Available at: https://www.area-arch.it/en/serpentine-pavilion-2017/ [Accessed 02 10 2019]. https://tomwiscombe.com/MOCA-PAVILION https://www.mathsisfun.com/data/standard-deviation.html https://www.theguardian.com/uk-news/2019/jun/12/burst-thames-water-pipe-cuts-off-up-to-100000-london-homes https://www.bbc.co.uk/news/uk-england-london-48529484 https://www.london.gov.uk/sites/default/files/running_out_or_flooded_out_-_londons_water_crisis_by_leonie_cooper_am.pdf https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/weather/learn-about/uk-past-events/interesting/2019/2019_008_july_rainfall.pdf?fbclid=IwAR3CdkCWqpypBre1H4e3qSrT7spETNqQkRMZBuC_ YHKlJW0BvJx_Ubtjr-o https://www.archdaily.com/91273/ad-classics-jewish-museum-berlin-daniel-libeskind/5afa4a49f197cc297f000003-ad-classics-jewish-museum-berlin-daniel-libeskind-photo https://libeskind.com/work/jewish-museum-berlin/ https://www.archdaily.com/91273/ad-classics-jewish-museum-berlin-daniel-libeskind https://www.google.co.uk/imgres?imgurl=https%3A%2F%2Fi.pinimg.com%2Foriginals%2F11%2F7e%2F37%2F117e370b906b5f8f1dcf17cf228411ab.jpg&imgrefurl=https%3A%2F%2Fwww.pinterest.com%2Fpin%2F49349607 1652338554%2F&docid=AvP-9SoICWLebM&tbnid=1-3qnex6JvH2-M%3A&vet=10ahUKEwit8uXjsp7mAhXbi1wKHeaQCGAQMwiGASghMCE..i&w=796&h=1200&hl=en&bih=938&biw=1840&q=jewish%20museum%20berlin%20 inside&ved=0ahUKEwit8uXjsp7mAhXbi1wKHeaQCGAQMwiGASghMCE&iact=mrc&uact=8 https://www.serpentinegalleries.org/explore/pavilion