09-03-31 go kawakita eesb thesis 2008

8/6/2019 09-03-31 Go Kawakita EESB Thesis 2008

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Environmental Optimisation Methods in Sustainable Design Process:In Combination with Evolution-Based Digital Technologyby

GoKawakita

SubmittedtotheDepartmentofArchitecture

inpartialfulfilmentoftherequirementforthedegreeof

MasterofScienceinEnergyEfficientandSustainableBuilding

atOxfordBrookesUniversity

April2008

Abstract

Thisdissertationdiscussesemergent,sustainabledesignmethodscombinedwithevolutionarycomputationandenvironmentalsimulationtools.Digitaltechnologiesarefrequentlyappliedto

engineeringfieldsresultinginimprovedsolutions.Ontheotherhand,veryfewarchitectscan

benefitfromsuchtechnicaladvancesinthefieldofsustainablearchitecture.Themainaimofthis

thesis is to research and developnovel designmethodscorresponding to the production of

environmentallyoptimisedarchitecturebyusingdigitaltechnologies.

Thecompletedprojectsexaminedinthefirstpartofthedissertationareanalysedtoinvestigate

possible future developments of digital tools applied to sustainable design. Moreover, the

analysesare expanded to create an experimental design tool that generates and optimises

windowsonselectedwallsundergivenenvironmentalcriterions.ThesystemusesaGenetic

Algorithm as an optimisation method, which is integrated into ECOTECT to provide

environmental simulations and enable the output of more optimal solutions. Finally, some

solutions to the practical problems of implementing this particular design methodology are

discussed.

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Acknowledgments

Iwouldliketoshowappreciationtoallmyadvisors.BenDoherty,whohasbeenmysupervisor

duringthiswork,hashelpedmetolearnalotincludingtechnicalaspects.MaryHancock,the

coursechairofMSc inEnergyEfficientandSustainableBuilding,gavemeanopportunityto

achievemyspecialtopic.

Ialsowouldliketothankeveryonewhohassupportedmeinthiswork.Especially,Iamvery

gratefultomyfamily;myfather,mymotherandmysister.

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Contents

1.Introduction 1

1.1BackgroundArchitectureandDigitalTechnology............................1

1.2MotivationandMainAim................................................2

1.3ThesisOutline........................................................3

2.CaseStudy4 4

2.1ComputerScienceEvolutionaryalgorithmsanddesign......................4

2.1.13DVirtualCreaturesKarlSims...................................4

2.1.2Genr8........................................................7

2.2ArchitectureGenerativedesignandcomputation............................9

2.2.1EvolutionaryArchitecture-JohnFrazer.............................9

2.2.2TheGenerativeSystem-LuisaCaldas.............................12

2.2.3LightHouse-GianniBotsford....................................15

2.2.4InductionDesign-MakotoWatanabe..............................18

2.3SummaryandAnalysis-EmergentTechnologiesandSustainableDesign.......21

3.ResponsiveFaadeDesignSystem(RFDS) 24

3.1DescriptionofRFDS..................................................24

3.2GeneticAlgorithmOptimisationMethod.................................25

3.2.1OverviewofGAs..............................................25

3.2.2BiologicalTerminology..........................................25

3.2.3ImplementationsofaSimpleGeneticAlgorithm......................26

3.2.4AdvantagesandDisadvantages..................................28

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3.3TestingSimpleModel.................................................29

3.3.1Non-EvolutionaryComputationalModel............................29

3.3.2Implementation................................................30

3.3.3ResultsandSystemDevelopments................................33

3.4ResponsiveFaadeDesignSystem......................................35

3.4.1SystemImplementation.........................................35

3.4.2CodingMethodGenotypeandPhenotype.........................38

3.4.3FitnessFunction...............................................39

3.5TestingtheResponsiveFaadeDesignSystem............................40

3.5.1OptimisationProcessesunderSimpleConditions....................40

3.5.2ResponsesofUser-DefinedPriorityLevels.........................49

3.5.3BenchmarksoftheRFDS.......................................53

3.5.4ExperimentalAdaptationtoPracticalDesignProcess.................56

4.FutureDevelopmentsandPossibilitiesoftheRFDS 61

4.1OtherSearchMethods.................................................61

4.2AdditionalEnvironmentalParametersandMulti-ObjectiveSituations...........61

4.3InteractiveOperationsAestheticDesignandEnvironmentalOptimisation......62

4.4AccessibilitytotheSystem.............................................63

4.5ComplexGeometryandGenerativeDesignTool...........................64

5.Conclusion 65

Bibliography 67

AppendixA 70

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List of Figures

2-1 3Dvirtualcreaturesevolvedforcompetition.............................................52-2 3Dvirtualcreaturesevolvedforswimming..............................................52-3 3Dvirtualcreaturesevolvedforwalking................................................52-4 3Dvirtualcreaturesevolvedforjumping................................................52-5 DiagramofconceptualmodificationofEvolvingVirtualCreatures............................72-6 ExamplesofgrowthmodelinGENR8withexternalforces..................................82-7 Diagramofbasicconceptandanexampleofoutput

derivedfrombuildingareaoptimisationprogramme......................................102-8 ObjectsgeneratedbytheBuildingEnvelopeDesignSystem...............................112-9 OptimisationsolutionsforthecaseofOporto...........................................122-10 AnnualenergyconsumptionforOportosimulation.......................................132-11 Paretofronttowardannualenergyconsumptionandgreenhousegasemissions...............142-12 TheLightHouseandsiteconditions..................................................152-13 Voxelsunlightanalysis..............................................................162-14 Environmentaldataineachheightandseason..........................................162-15 TheWebFrame;physicalconditionsforformfindings....................................182-16 Formfindingsandhumanpreferences.................................................192-17 Theflowofhumanevaluationanddevelopmentofcomputation............................20

2-18 Keiriki1-Formgenerationandstructuraloptimisation....................................203-1 SystemflowchartoftheRFDS......................................................24

3-2 SimpleGAflowchart..............................................................26

3-3 SimpletestmodeloftheRFDSgeneratingcircularwindows...............................29

3-4 Programflowchartofthetestmodel...................................................30

3-5 Differencesinthenumberofnodes...................................................313-6 Generativeprocessofthetestmodelshowingdaylightlevelsatthereferencepoint............32

3-7 Naturaldaylightlevelsimulationbyanalysisgrid........................................333-8 AnimageoftheRFDSgeneratingpixel-likefaade......................................353-9 RFDSsystemflowchart............................................................363-10 Animageofarrangementofreferencepoints...........................................373-11 Compositionofachromosomeandcodingmethod......................................383-12 ThefitnessfunctionoftheRFDSandfunction-basedweightingfactors......................393-13 Simpleexplanationsofatestimplementation...........................................40

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3-14 Ageneratedsolutionandlightinganalysis..............................................42

3-15 Lightinganalysiswithfourreferencepoints.............................................43

3-16 Naturallightinglevelateachreferencepoint............................................44

3-17 Fitnessscoresofthefirstsimulatedmodel.............................................44

3-18 Fitnessscoresofthesecondsimulatedmodel..........................................45

3-19 Lightinganalysisattwoheightlevels..................................................46

3-20 Naturallightinganalysiswitheightreferencepoints......................................47

3-21 Naturallightinglevelateachpoint....................................................48

3-22 Arrangementofreferencepointsandtargetdaylightlevels................................49

3-23 Fitnessscoresofthesimulationwithweightingfactors....................................50

3-24 Naturallightinglevelofeightreferencepoints...........................................51

3-25 Differencesfromeachtargetlevel.....................................................52

3-26 Initialfitnessscoresandcalculationtimeindifferentpopulationsizes........................54

3-27 Calculationtimeandchromosomelengthindifferentwindowsizes..........................54

3-28 Generatedwindowsindifferentwindowsizes...........................................55

3-29 Arrangementofroomsandreferencepoints............................................56

3-30 Generatedoptimumwindowarrangement..............................................57

3-31 Lightinganalysisofthesimulationmodel..............................................58

3-32 Lightinganalysisshowinglightinglevels...............................................59

3-33 Fitnessscoresofthesimulationmodel................................................60

3-34 Naturallightinglevelsofsixreferencepoints............................................60

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Chapter 1: Introduction

1.1 Background Architecture and Digital TechnologySincecomputerswereinvented,manyaspectsoflifehavebeenchanged.Onemightbeableto

recogniseeasilysuchvariationsbyonlysweepingonesroom.Theremightbeacomputeritself,

andmanypiecesofelectricequipmentcontrolledbycomputers.Evennon-digitalitemssuchas

books,clothes,etc,arealsorelated tocomputersdirectlyorindirectlythroughmanufactureor

distributionetc.Moreover,itmightbenoticedthatbyexpandingoursights,almosteverythingin

relation toour lives isbased ondigital technologies. Inother words, our livesmightnot be

feasibleanylongerwithoutcomputers.

As faras thefieldofarchitectureis concerned,the situation issimilarto other fieldsaswell.

Architecturewasregardedastheresultofworkingbyhand.However,thesituationisnolonger

asitwasinthepast.Drawingsandevenmodelmakingisproducedandcontrolledbycomputers.

Furthermore,notonlysimpletaskssuchasthese,butalsodesignitselfhascometobelongto

thecategory of theabilityofcomputers.Designprocessesderived from piecesofarchitects

sketchesarereplacedbycomputersinthelatestdigitaldesign.VariousarchitectssuchasZaha

Hadid,Ali Rahim,GregLynn,Nox,etc incorporatecomputers into theirdesignasmore than

mere drawing tools. Such trends of digital design are more conspicuous amongst younger

architects. Achim Menges, Alisa Andrasek, Aranda/Lasch, and so on are representative ofyoungergenerationinemergenttechnologyandarchitecture.Intheirdesigns,computersareno

longerapartoftheirdesign toolsordesignprocesses.Suchdigitaltechnologyistheirdesign

itself.

Althoughcomputersarewellutilisedinarchitecturaldesignatpresent,theapplicationofdigital

technologies is more advanced in the engineering field, especially structural simulation and

design.Structuralmembersareoptimisedbydigitalsimulationintermsofconstructioncosts,life

cyclecosts,energyconsumptionandevengreenhousegasemissions.Structuraloptimisationof

trussesisoneofthemosttypicalapplications.Inaddition,optimisationofthesizeandcontrolof

HVACsystemsisalsooneexampleinanotherengineeringfield.

Ontheotherhand,therelationshipbetweendigitaldesignandsustainabledesigninarchitecture

isfarbehindcomparedwithotherfields.Currentlyifweusedigitaltechnologiesforsustainable

design,conventionalmethodswouldgenerallyproducebettersolutions.Inotherwords,therole

ofcomputersisonlytosimulateobjectspreviouslydesignedbyarchitects.Firstlyarchitectureis

designed, and it is simulated to gather environmental data. Afterward, the architecture is

changedorfixedpartially,andisenvironmentallyanalysedagain.Suchtasksareiterateduntil

the satisfactory completion of the design, or the budget runs out. Generally, sustainable

architecture goes through such design processes at present. Other non-architectural fields

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frequentlyuseoptimisationtoproduceimproveddesignoutcomes;however,thisisnotthecase

insustainablearchitectural design.Thehigh performanceofmoderncomputing cannowbe

usedadvantageouslyforsustainabledesignaswellasotherarchitecturalfields.

Thus, digital technologies are well combined with architecture in various fields except for

environmental design. Moreover, such connections between architecture and digital

technologies might become more intimate in future compared with present circumstances.

Therefore,ascomputersareeffectivelyutilised intheotherarchitecturalfields,environmental

designmustincorporatedigitaltechnologiesinordertoremaincurrent.

1.2 Motivation and Main Aim

Theenergyconsumptioninthebuildingsectoriscurrentlymorethan45%oftheUKtotalenergy

use. Therefore, energy savings in the building sector are exceedingly important in termsof

overallenergyconsumption.Currently,therearevariouswaysofenergyconservationincluding

insulation, thermal mass, controlled ventilations, renewable energy sources, and so on.

Researchintosuchelementshasresultedinbetterbuildingperformancesintermsofenergy

efficiency.Meanwhile,theseissuesarerelatedtootheraspectssuchasembodiedenergy,use

ofnatural sources,etc.According to this, itmight be alsonecessary toresearch theway to

optimise theutilisations of the natural environment itself, including passive heating, passive

cooling,andsoon.Inthisthesis,anemergentmethodforsustainablearchitecture,whichrelatedtodigitaltechnologiesandpassivedesignmethods,isinvestigatedandadvanced.

Therelationshipbetweensustainabledesignanddigital technologiesis notwell advancedat

present,asreferredtoin theprevioussection.However,accordingtothehighperformanceof

computation in other architectural fields, the combination between digital technologies and

sustainabledesignmightbeanewwayofsavingenergy.Additionally,morethan70%to80%of

thetotalenergyinbuildingsisconsumedbyspaceheatingandlightinginbothresidentialand

commercial buildings. Becauseof that, it could bevery efficient and hold great potential for

energysavingstooptimisethepassiveenvironmentintermsofheatgains,heatlosses,natural

light,andsoon.Forinstance,unnecessarywindowsincreaseheatlosses.Moreover,theglare

derivedfrombadcontrolofthenaturallightencouragesoccupantstouseartificiallights.Itisthe

mostimportanttooptimisewindowarrangementsinordertobalancethepenetrationofnatural

lightwithheatlossesthroughwindows.

Thus,themainaimofthisthesisistoinvestigateanddevelopthewaysanddesignprocessesfor

effectiveuseofthenaturalenvironmentby usingemergentdigital technologies.Furthermore,

theresultsof researchareexpectedtobeextendedandappliedtonewenvironmentaldesign

systems. Therefore, the production of simple environmental optimisation tools is partial

objectivesofthisthesisaswell.

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1.3 Thesis Outline

This thesis describes the conceptual new method of sustainable design in terms of

environmentaloptimisationsbasedonemergentdigitaltechnologies.Themainaimofchapter2

istoresearchdigitaltechniquesattheforefrontofdesign,andcriticallyconsideringtheirpractical

applications.Accordingtotheinvestigationofpresentsituationsofdigitaldesign,possiblefuture

developmentsarealsodiscussedinthischapter.

Based on the results of research in chapter 2, some experimental models in terms of

environmentaloptimisationsareimplementedinchapter3.ECOTECTcontrolledbyLuawhichis

simple programming language is applied to the models. The results derived from the

implementationsareanalysedandutilisedforfuturedevelopmentsofadvanceddesignsystems.

Inchapter4,furtherstagesandpossibilitiesbasedonthemodelsorsystemsaredemonstrated.

Finally, the overall aspects of sustainable design and digital technologies are concluded in

chapter5.

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Chapter 2: Case Study

Inthischapter,someexamplesofdigitaldesignsaredescribedandanalysedintermsoftheir

presentimplementationandfuturepossibledevelopments,especiallyfromthepointofviewof

sustainablearchitectureanditsdesignprocesses.Technicalaspectsofemergenttechnologies

are mainly discussed in the first section, and, on the other hand, practicalities of such

technologicaldevelopmentsaretheobjectiveofthenextsection,focusingonarchitectureand

computation.Finally,thepotentialofdigitaldesignandsustainabilityisdiscussedconsideringall

aspectsanalysedinprevioussections.

Theuseofcomputers issignificantand fundamental inthepresentarchitecturalfield.Almost

every aspect such as drawings, simulations, presentations and so on, is computerized.

Especially, activities such as structural analyses might not be feasible without digital

technologies.Meanwhile,computationsarenotaseffectivelyappliedtosustainablearchitecture,

comparedwith other fields.For instance, the forefront digital design, complexenvironmental

simulations, etc. Considering the successful application of digital technologies to other

architectural fields and advantagesderived from them, this thesis seeks toprove that such

technologiesmustbe beneficial tosustainabilityaswell.Therefore, it isnecessary forfurther

developments in sustainable design to research and investigate the advantages and

disadvantagesoftheseemergenttechnologies.

2.1 Computer Science Evolutionary algorithms and design2.1.1 3D Virtual Creatures Karl SimsKarlSimsisoneofthemostwell-knowncomputerartistsusingevolutionarycomputationintheir

work.Heshowsustheattractionofevolutionaryalgorithmsandtheirpossibilities throughhis

computeranimations.Oneofhisworks,EvolvingVi ualCreatures (1994) isanexpositionof

resultsofhisresearchabout'virtualcreatures'producedandevolvedundercertainsituations.In

this work, 3D virtual creatures are expected to become more complex and behave more

interestingly thanwhen theyarefirstcreated,as anoutcomeof optimisation,althoughvirtual

creatures are basically dependent on their fitness in thesamewayasother evolutionary art

works.Creaturesareevaluatedwithafitnessfunctionandhigheradaptationsareabletosurvive

underagivensimulatedenvironmentalconditionssuchasgravity,groundfriction,andsoon.

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However, the work is different from others in the aspect that control systems which are

user-definedareintroducedto decidetheformsofvirtualcreatures,aswellas theevolution.

Creatureshavesomenodeslabelledashead,body,limbandsoon,butnotspecificfunctional

components in their biological concepts. Each node and its connectionaffect the results of

morphologies and creature behaviour. In addition, creature behaviour is controlled by three

effects which are sensors, neurons and effectors. Such control systems of phenotype

morphologiesarethereasonwhyinthiswork,virtualcreaturesaremorecomplicatedandtheir

behavioursaremoreinterestingthanothersproducedinordinaryevolutionaryways.

Figure2-1:3Dvirtualcreaturesevolvedforcompetition.

Source:KarlSims(1994)

Figure2-2:3Dvirtualcreaturesevolvedforswimming.


Figure2-4:3Dvirtualcreaturesevolvedforjumping.


Figure2-3:3Dvirtualcreaturesevolvedforwalking.


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Fitnessfunctionsareoneofthemostintricateandstimulatingelementsingeneticalgorithms.A

fitnessfunctionisasinglevaluederivedfromafunctionwhichexplainstheaimofexperiment,

ntheotherhand,theoptimumsolutionundercertainenvironmentisnottheoptimumsolution

nderdifferentconditions.Accordingtothis,itmightbeeasilyunderstoodthatspeciesdevelop

s demonstrated above, 3D virtual creatures are successfully produced by Karl Sims.

eanwhile, his works could be modified in terms of architecture and its environmental

and it is the key factor which encourages reaching the conclusion of the experiment. For

example, in the worksofKarlSims demonstrated in figure 2-1 to2-4,walkingorswimming

speed,themaximumheightofthejumporthedistancetothecubeaftermovementtowarditina

fixedtime isusedforcalculationofthefitnessscores.Thesevariousconditionsforevaluation

resultindifferentoutputsevenifthegivenenvironmentforexperimentsisentirelythesame.The

relationshipbetweentheinfluenceofthefitnessfunctionandagivenenvironmentisoneofthe

pointsforconsiderationwhenevaluatingthefitness,andviceversa.

O

u

ineachenvironment inadifferentway.EvolvingVirtualCreatures clearlydemonstratessuchevolutions of creatures and their possibilities in different conditions including gravity, water

resistance, andsoon. In fact, the physical featuresofvirtual creaturesaredifferent in each

conditionorpurpose;swimming,walkingandjumping.Forinstance,paddlingandtailwagging

creatures are produced by the swimming fitness function and there are shuffling creatures

produced by the walking fitness function. Each creature evolves strategies to exist in its

particularcircumstances.

A

Moptimisationsasfollows.Creaturesareinterpretedasvirtualspace,andcreaturemorphologyor

behaviour is regardedasphysicalspaceor itsuse.Figure2-5showsaconceptualmodelof

architecturalreinterpretationofKarlSimsworksbytheauthor.Inthefirststageofanexperiment,

genotypevirtualspaceiscreated,whichhasarchitecturallycategorizednodessuchaswindows,

shading devices,andso on.Thevirtualspace formsphenotypespace,which isphysical3D

spacewith some restrictions. Afterwards, generated 3Dspacebehaves and changesunder

control systems. That is to say, this virtual space behaviour involves occupant activities

determinedby 'neurons', which isone of operators used in Sims works,with the values of

sensorssuchaslightingsensor,temperaturesensors,orweathersensor,forexample,opening

orclosingwindows,usingshadingdevices,etc.However,therearenaturallyphysicallimitations

inthesamewayastheSimsexperiments,forexample,whetherwindowscanbeopenedornot,

orwhethershadingdevicescanbefullyclosedornot.Thus,simulatedoccupancyactivitiesare

restrictedbyimposedphysicallimits.Meanwhile,afitnessfunctioninthismodifiedcasemightbe

energy consumption orCO2 emissions within a givenperiod. Through theseprocesses and

genetic evolutions, optimised virtual spaces are created. This is one of possible ways to

reinterprettheproject,EvolvingVirtualCreatures,intoarchitecture.

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VirtualSpace(Genotype)

SpaceMorphology

MainSpace

Window

ShadingDevice

Wall

SpaceVolume

WindowSize

ShadingDeviceSize

WallDepth

OccupantActivities Sensors

Energyconsumptions

Neurons

Effectors

VirtualSpace(Phenotype)

Environmentalfactors

Weather

Sunpath

Season

U-value

G-value

Shadingfactor

Airtightness

Evaluation(FitnessScores)

Evolution

LightingSensor

TemperatureSensor

WeatherSensor

Physicallimitation

Materiality

CO2Emissions

Window-Openornot

Shadings-Activeornot

Figure2-5:DiagramofconceptualmodificationofEvolvingVirtualCreatures.

2.1.2 GENR8GENR8isasurfacemodellingtoolusingabiologicalgrowthmechanismwhichisbasedonmap

L-Systems.ThemapL-SystemsareoneoftheL-Systemsspecifiedforsurfacegeneration.The

main aim of GENR8 is interactive design, so various parameters are available in order to

generate and evolve surfaces. Basically, in the system, parameters can be regarded as

environmentalfactors,andsurfacesaregeneratedwiththesesurroundinginfluences.Figure2-6

showsexamplesofsurfacesproducedbyGENR8withexternalinfluences.Intheleftcase,a

surfaceisattractedbygravityanddisturbedbyasphereinthesamewayasintherealworld.

Meanwhile,intherightcase,cylindersmagnetizeasurfaceinrelationtothedistancebetweena

surfaceandattractors.

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Figure2-6:ExamplesofgrowthmodelinGENR8withexternalforces.

Source:OReillyandHemberg(2006)

Users are not able to control the whole system of form finding in other traditional surface

generationtools,becausetheiralgorithmstocreatesurfacesaredeterminedbeforehandandthe

algorithms aredesigned by system developers.Manipulation is only possible through initial

setup, and thegenerativeprocessitself isnotmodifiable. That is tosay,althoughuserscan

decideinitialinputas theylike,thewaystocreateobjectsarefixed.Ontheotherhand,unlike

suchconventional design tools,GENR8 encouragesusers to access every stage of surface

growthandevolutionarycomputationbecauseinthesystem,componentsrelatedtoevolutionary

algorithmsarealsopartofthesystemsparameters.

Meanwhile,inaestheticdesign,oneofthemostdifficultelementsaboutevolutionaryalgorithms

isafitnessfunction.Mathematicalfunctionsareveryusefulinthecaseofoptimisationfor'hard'

quantifiableparameters;however,it isdifficult toexplainhumansensibilitiesin termsofexact

mathematical functions.Therefore, InteractiveEvolutionaryComputation(IEC) isused forthe

fitnessfunction inGENR8.Thisis thealgorithmaimedatpredictinguserpreferences.Fitness

functions are parametric factors in GENR8 aswell as the other elements explained above.

According to this, aesthetic sensations of users are reflected in the generated forms, even

thoughtheevolutionaryalgorithmsarecarriedoutinsidethesystem.

Thus,Genr8isdevelopedconsideringinteractionsbetweenusersandthesystem.Theyarethe

most considerable aspect of the system due to the fact that even ifone isnot an expert in

programming, generative design is made easily available. Generally speaking, designers or

architectspreferintuitivemanipulations,sothatcomplicatedandintricateprocessesarelikelyto

beavoided.Moreover,itmightbetruethatitiscommonlynotnecessaryfordesignerstolearn

suchcomplexcomputer sciences.Emergentdesigntoolsareattractiveandmanypeopleare

likelytousethem;however,thedeepmechanismsofsuchtechnologiestendnottobetheusers

focus.Therefore,intermsofdevelopmentofdigitaldesigntools,accessibilityofanapplicationto

itsusersmustbeoneofthemostfundamentalandnotableaspects.

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Asfarasthesurfacegenerationprocessitselfisconcerned,GENR8isaninnovativedesigntool

asdemonstratedabove.Nevertheless,noenvironmentalparameterssuchas lighting factors,

winddynamics, sunlightandso on,arecurrentlyavailable.Although it isbecauseGENR8is

createdasnotanoptimisationtool,butanaestheticsurfacemodellingtool,thetoolwouldbe

significantlymoreusefulforresponsivedesignifitwasabletobeequippedwithenvironmental

parameters.

Thereareseveralpossibilitiesforfurtherdevelopmentofthesystem.Firstly,referencepointsto

measurenaturallight,internaltemperature,andsoon,couldbeoneoftheadditionalparameters.

Thepointswouldhavepredeterminedvaluesastargets,andsurfaceswouldbegeneratedand

modifiedunderthoseconditions.Forinstance,ifareferencepointaboutnaturallightissetupin

thescene,asurfacewouldbederivedfromthecalculatedannualaverageluminancebynatural

light.Ifasurfacecouldgrowwithoutcoveringorunveilingareferencepointtoomuch,itcouldbeapplied topassivesolardesign.Thesecondpossibility isthewinddynamicsparameter.What

this parameter would do is to simulate and calculate air flow through a generated object.

Accordingtothisparameter,idealformswouldbecreatedandoptimalairflowmadeavailable.

Suchtechnologiesmightbeusefulforarchitectureinthetropics.Thus,ifit isnotnecessaryto

considerthetechnicalproblemsofsoftwaredevelopment,possibleimprovementsareinfinite.

Needless tosay,therearevariousproblemsspecial toenvironmentaldesignsoftware,which

cause difficulties of system developments. From the computer scientific points of view,

complexityofenvironmentalsimulationmightbethemostproblematictopic.Duetothefactthatenvironmentalsimulationstakea greatdeal of time, thesystemsarenot interactiveatall. In

otherwords, changes ofobjects in the scene donot respond to the result ofenvironmental

calculations simultaneously. In the same way as rendering in 3D visualization software,

calculations are necessary every time the scenes are changed. These facts might be

troublesomeobstructionsfortheearlystageofdesigntasksorresponsivedesign.Userscannot

work comfortably, for example by sketching their ideas. Moreover, developments of new

softwaresolvingsuchproblemstakeenormouscostandtimetoproduce;therefore,itcouldbe

statedthatitistoodifficultforindividualstocopewiththesecomplexities.

2.2 Architecture Generative design and computation2.2.1 Evolutionary Architecture - John Frazer

JohnFrazerbrokenewground inthefieldofdigitalarchitecture.Hisfocusdiffersfromother

algorithmic architecture which creates the latest attractive objects, which might be the

mainstream in present digital design. As far as evolution is concerned, such present

mainstreamsareprobablymerecomplicatedformfindingexercises.Inshort,theyarecreatedby

uncountableiterationsofworks,whethertheyareintricateorsimpletasks.Ontheotherhand,

Frazersarchitectureisasolutioncorrespondingtocertainconditions.Theyaregeneratedand

evolvedinasimilarwaytothenaturalworld.

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In his projects, environmental conditions, especially solar constraints, are one of the most

importantfactors.TheGroningenExperiment(1995),whichisacomputationalurbandesign,is

one of his notable research projects. In the project, he demonstrates a responsive design

processwhichissimilartothewaysthatsomearchitectsusecomputationalmethodsatpresent.

Althoughnaturallightistheonlyfactorofgenerativedesignatthispoint,itcouldbesaidthathis

ideaoftherelationshipbetweenarchitectureandthenaturalenvironmentisimportantduetoits

innovativeparts.Afterwards,theideaofenvironmentallyresponsivedesigndevelopedwiththe

growthoftechnology.In2003,heputforwardamoreadvancedandcomplexmethodwhichisa

computer programme to generate and arrange architecture, optimised under the maximum

buildingareaandcontrolofsolargains.Inshort,thissystemisbasicallyappliedtourbandesign

accordingtothefactthatbuildingsincertainareaarecomprehensivelyoptimised.Figure2-7is

anexampleoftheoutputofthesystem.

Figure2-7:Diagramofbasicconceptandanexampleofoutputderivedfrombuildingareaoptimisationprogram.Source:Frazer(2003)

Oneofimportantpointsoftheprojectisthewaytointeractivelyarrangegeneratedarchitecture.

Theshadowderivedfromeachbuildingiscalculatedthroughayear,andthosedataareusedfor

optimisationoflayoutscheme.Thesurroundingenvironmentinfluencestheplaceandsizeofa

building and vice versa. Needless to say, the project is basically about optimising building

arrangement,andtherefore,itisnotpossibletoapplytheprojecttothecaseofasinglebuilding

simulation.However,unliketheordinarywaysofpassiveenvironmentalsimulationsinwhichthe

environmentinfluencestheresultinaunidirectionalway,thegenerativeprocessesinhisproject

are interesting and remarkable due to the fact that influencesare bidirectional between the

surroundingsandthegeneratedarchitecture.Suchinteractionmightbeexceedinglyimportant

forsustainabledesign.

However,thecomputerprogramhedevelopedcanhandleonlysimpleobjectssuchasacuboid;

therefore, practicality and accuracy are not well achieved in the present circumstances. In

addition to this, notonly natural light,butalsoother environmental factorsare necessary for

further development, and other elements such as energy consumption, greenhouse gas

emissionsandsooncanbeappliedtosuchobjectivesaswell.

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Frazeralsoshowsusmanyotherinterestingpointsingenerativedesign,aswellasresponsive

design demonstrated above. TheBuildingEnvelopeDesignSystem isa softwaresystem to

generatenovelformsbyevolutionarycomputationasfigure2-8shows.Preliminaryoutlinesand

somesupportiveaxesdeterminedbyuserscreatecomplexformsthroughseveralcomputations.

Intermsoftheflowofthegenerativedesign,thesystemisconsiderablydeveloped,andthere

are many applicable ideas to other generative systems or generative design processes.

Meanwhile,duetothefactthatthemainfocusofthesystemisthemathematicalformgeneration

andevolution,createdshapesarenotcomposedofanyenvironmentalelements.Briefly, they

aresimplyaesthetic,andnotgeneratedwithinfluencesbyanyexternalconditions.

Figure2-8:ObjectsgeneratedbytheBuildingEnvelopeDesignSystem.

Source:Frazer(2003)

As demonstrated above, each aspect such as generative design or responsive design is

gradually, but definitely being developed, although there are still problems to be solved for

practical use. In the environmental design,one of themost important factors isaccuracy or

practicalityderivedfromtheplacingandorientationofbuildings,materials,sizesofeachbuilding

part,etc.Infact,manycomplicatedequationsareusedtosimulateenvironmentalconditionsin

theprojectdemonstratedabovetoarrangearchitectureintermsofmaximumsolargain,evenif

each model is simple in the project. Such complexity might be the reason why technology

development in termsofenvironmentaldesign isnotmainstreamatpresent. Inotherwords,

designers or architects might prefer to create aesthetic objects, rather than solving difficult

environmentalsituationsandmakingcomplicatedequations.

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2.2.2 The Generative System - Luisa CaldasLuisaCaldasisoneofthepioneerswhoappliedevolutionaryalgorithmstoenvironmentaldesign.

TheGenerativeSystem (GS)which isaGA-based simulationsystem invented byher, helps

designersorarchitectstofindsolutionsmainlyintermsofenergyefficiency.Inasimulationusing

GS, thereare two main factors tomeasure fitness scores; annual energy consumption and

satisfactionofdesignconstraints.Annualenergyconsumptioniscalculatedduetobothlighting

andheatingorcoolingenergy,andwithconsiderationofsurrounding influencessuchassolar

gains, heat losses, internal radiation, and soon.On the otherhand,design constraints are

slightlymorecomplicated.AccordingtoCaldas,GS isnotonlyanevaluationtool,butalsoa

designtool;therefore,theresultsofsimulationsneedtoreflecttheopinionofdesigners.Inother

words,designconstraintsarepartofthefitnessfunctiontoavoidunexpectedresultsandcontrol

thegenerativeprocesstoacertainextent.

Figure2-9:OptimisationsolutionsforthecaseofOporto.

Source:Caldas(2001)

GS isappliedto existing architecture tocomparepresentenergyperformance and simulated

solutions. The selected building for the experiment is the school of architecture in Oporto

designedbyAlvaroSiza.Figure2-9demonstratestheresultsprovidedbyGS.Thewindowsize

and the depth of shadingdevices are optimised.The red lines are user determineddesign

constraintsintermsofSizasdesignconcept.Figure2-10showsannualenergyconsumptionfor

bothexistingbuildingandsolutions.Accordingtothefigure,thereductionofenergyconsumption

isabout10%forthebestsolution.Moreover,Caldas(2001)claimedthatthereductionmight

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becomemore,whichcouldbeconfirmedbyamoreaccurateenergycalculationinGS.Thus,the

Generative System encourages finding environmentally better design solutions within

architecturaldesignintentions.Thisuser-dominantoptimisationforGAisremarkable,anditcan

bedevelopedandappliedtoadvancedgenerativedesigntoolsforfurtherstages.

0best-shading best average existent worst

Others

Ventfans

Spacecool

Spaceheat

Lights

oporto-shading

oporto-best

oporto-average

oporto-existent

oporto-worst

87.58

89.99

96.22

96.45

110.55

20

40

60

80

100

120

Figure2-10:AnnualenergyconsumptionforOportosimulation.

Source:Caldas(2001)

However,itisalsotruethatGScurrentlyoptimisesjustwindowsizeandthedepthofshading

devices.Additionally, the fitness issimplyameasure ofenergyconsumption. It ispractically

necessarytoconsiderotherfactorsincludingconstructioncosts,embodiedenergy,lifecyclecost,

andsoon.Thelowestenergyconsumptionisnotalwaysthebestsolutionforotherelements,

andvice versa.For instance, the latest products includingglazingmight beexpensive even

thoughtheirenvironmentalperformanceis better than theoldersolutions.Suchasituation is

knownasParetoefficiency,whichistheconditionthatifsomeinagroupareimproved,others

become worse. As far as such multi-objective optimisation problems are concerned,

multi-objectivegeneticalgorithms (MOGA)areappliedtoGS forthesecondstage.This isa

substantialapproachintermsofpragmaticsolutions.Theblacksquaresinfigure2-11explain

trade-offs for twoobjectives, the reduction of greenhousegasemissions and annualenergy

consumptions,sothatusersareabletochoosemoreidealandpracticalsolutionsthanthosefor

singlefunctions.Furthermore,intheexperimentsofGSforParetooptima,othercombinationsof

objectives,forexampleconstructioncost,energybalance,etc,arealsosuccessfullysimulated.

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0

1000

22 22 24 25 26 27 28 29 30

MWh

gen1

gen200

2000

3000

4000

5000

6000

7000

Figure2-11:Paretofronttowardannualenergyconsumptionandgreenhousegasemissions.

Source:Caldas(2001)

Thus,becauseofitsenvironmentaloptimisations,theGenerativeSystemisaninterestingand

usefultoolevenifitisunderdevelopment.Ontheotherhand,itisafactthatsomeproblemsstill

remaintobesolved.Firstly,atthisstage,GSisnotintegratedintoanyCADsoftware,anditis

notdevelopedasaplug-intool.SolutionsderivedfromGSarenotvisualizeduntilevaluations

and simulations are completed. This means that intuitive manipulations, which designers or

architectsmightpreferin thedesignprocess,arenotavailable.Therefore,GSdoesnot reach

thelevelofagenerativedesigntool,andisstillasimulationsupporttool.Secondly,formfinding

or form generation is not supported in the tool unlike Genr8. Although shape generation is

experimentallydevelopedasthethirdstageofGSatpresent,feasibilityisextremelylimitedand

itispossibletomodifyonlythegivenrectangularbox.

Nevertheless, these limitationsunder3Dvisualizationandproductivitymightnotbethemost

problematicin termsoftheadvancesofothermorphologytools.Integrationoftechnologiesin

differentresearchstudiesisnecessaryandpossibleforfurtherimprovement.Themostserious

problemisdifferencesbetweenvirtualandrealspaceinthesoftware.Buildingfabrics,purpose,

and other physical situations are fundamental for environmental building simulations. For

example, it is impossible to calculate heat losseswithoutU-values, and solar gains without

g-values or glazing types. Moreover, it is not possible to compare building performance to

benchmarkswithoutoccupancy.Ontheotherhand,virtualspacesinsoftware,ofcourseexcept

forenvironmentalsimulationtools,donothaveanyphysicalexistences.Aboxisnotabuilding

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oraroom.Averticalobjectisnotaconcretewallorawoodencolumn.Suchbasicfactorsfor

environmentalsimulationsareobstaclestointuitivemanipulations,andarethemostproblematic

andcomplicatedelementsforfurthersoftwaredevelopment.

Asexplainedabove,GSisaninterestingsystem,althoughithasvariousproblemstobesolved,

including design ability, reality in a virtual space, etc. Especially, approaches toward

multi-objective optimisation problems are notable in terms of comprehensive solutions.

Therefore,itcouldbeclaimedthattheconceptsoftheGenerativeSystemareusefulfornotonly

developmentofothertools,butalsoenvironmentaldesignitself.

2.2.3 Light House - Gianni Botsford

TheLightHouseinNottingHill,LondondesignedbyGianniBotsfordisoneofasmallnumberof

real computational design projects. Due to the restricted site situations, the project is

conspicuousbyitsuseofsophisticatedenvironmentalanalysisandoptimisation.Asfigure2-12

demonstrates, the site is surrounded onall sidesby three to five storey houses and trees;

therefore,theonlywaytogainsunlightisthroughtheroof,whichBotsfordcallstherooffaade.

One ofbasic requirements for the project is toprovide natural light to thewhole area in the

house;notonlythetopfloor,butalsothebottomfloors.

Figure2-12:TheLightHouseandsiteconditions.

Source:GianniBotsford(2006)

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Figure2-13:Voxelsunlightanalysis.


Figure2-14:Environmentaldataineachheightandseason.


Inthesolutiontosuchlimitedconditions,therearetwomainimportantkeywords;SolarLogic

and Building Envelope Optimisation. At the first stage of the project, extremely detailed

environmentaldatawascollectedbyusingtheSolarLogicwhichBotsforddevelopedinorderto

visualizeandanalysethebehaviourofnaturallight.Duetothesystem,environmentalconditions

aredemonstratedasa three-dimensionalgridbyvoxelswhichare1mcubes.Figure2-13and

2-14areexamplesofvoxeldataanalysis.ThewayofexplainingnaturallightbytheSolarLogic

cangreatlyencourageuserstoanalyseexistingconditionsofthesiteatanypointandanytime

throughayear.Forexample,itispossibletorecognizethree-dimensionallythebrightestorthe

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darkestpartofthesite,orthecomprehensivechangesofnaturallight.Itisjustlikethewaythat

engineers collect anddissectmicroscopic information for further developments. There is no

entirelysubjectivedesignprocessbecauseofpreconceptions.Scientificenvironmentaldatais

gatheredat thefirst stageof thedesignprocesses.Thismightbeoneof themostimportant

factors for environmental design, although various buildings are controlled by designers

preoccupationsfromthefirststageofprojects.Moreover, itmightavoidarchitectstostrayinto

time-wastingonunnecessarysimulationscausedbywrongintuitions.

Althoughthesystemexplainedaboveisexceedinglyinterestingandsubstantialfordataanalysis,

theyarecurrently visualisationsofenvironmentaldata, notarchitecture itself yet. In termsof

architecture, thecollected data is used forspatiality andmateriality withclientspreferences

including room arrangement, room size and soon.Meanwhile,as explainedabove, the roof

faadeistheonlypossiblewaytoobtainnaturallightinthesite;therefore,buildingmaterialsfortheroofareoptimisedbyusingthesoftwaredevelopedbyBotsfordandArup.Thesoftwareuses

ageneticalgorithmand canbe applied tomulti-objectiveoptimisationproblems.This topic is

alsodiscussedintheprevioussection,andmightbeimportantwithregardtopracticalsolutions.

InthecaseoftheLightHouse,Paretofrontbetweendaylightfactoranddirectsunvisibilityis

evaluatedwithglazingtypessuchasopaque,filmedorclear.Dueto themanyobjectivesand

thereforemultiple fitness functions, reconciling thesebecomesdifficult. Is themostimportant

factoreconomical,environmental,or functionaloptimisation?Therecouldbeasmany fitness

functionsasthenumberofproblemstobesolved.

Considerably,intheLightHouseproject,computationaldesignandtechnologiesareactualized

unlikemanyotherprojectswhichremaininaconceptualstate.Ontheotherhand,suchareal

project demonstrates some remarkable aspects for reality and computational optimisation.

Generallyspeaking,clientstrulycontroleachproject.Solutionsderived fromcomputersmight

not be the best solutions or even satisfactory solutions for them. This is probably more

conspicuous in the caseofprivate projects thanpublic ones.For instance, even ifa limited

glazingareaonthenorth faadeisabettersolution intermsofenergyefficiency,onemight

requireaglassbox.Logicalsolutionscouldsometimesbeagainstpurepreferences.

Tosumup,itmightmeanthatduetothefactthatarchitecturealwaysexistswithhumanbeings,

it isalsonecessary for computation tohandle humanity. This isdeeply related to two huge

topics:multi-objectiveoptimisationproblemsandinteractiveevolutionarycomputation(IEC).IEC

is the algorithm which handles human preferences and evaluations. It might be true that

evaluationof scientificmatters isnot truly complicated in regards tomathematical functions.

Meanwhile, numerical expressionsof human preferences arequite difficult.What is good?

Whatdoesgoodmean?Computersareverybadatsuchsubjectiveelements.Moreover,even

ifnumericalexpressionsofhumanpreferenceswerepossible,itisnotequivalenttotheabilityto

createpreferablesolutions.Onecanselectandgradefavouritecolours,shapes,materials,and

soon;however,acombinationofthemdoesnotbecomeonespreferenceinahighprobability.

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2.2.4 Induction Design - Makoto WatanabeInductionDesign isa series ofprojects whichMakotoWatanabe launched in 1990andhas

researched up to the present. Everyproject in the InductionDesign series isdeveloped by

computerprogramstofindsolutionswhichadapttogivenconditions.Heclaimsthatcomputers

andarchitecturecreatedby themarenottheresultofhighstandardgraphictechnologies,but

solutionbasedtechnology.Inotherwords,simplificationofworks,forexampledrawingplans,or

randomgenerationof infinite patternsas formcontrols isnot the essenceofcomputationof

architecture.Theenormousamountofthoughtisthetruesubstanceofdigitaldesign.Therefore,

developmentofdesignprocesseswhichgeneratesolutionsundercertainstatements,orsuch

processesfortheirownsakearethemostimportantelementintheInductionDesignseries.

Someofhisdigitaldesignsareactualized,althoughcountlessotherprojectsby otherpeoplefocusingon thecomputationofarchitecture,whicharesimilar tohisprojects, result invirtual

schemesincomputerscreens.TheWebFrameisthefirstrealworkofthesetodemonstrateto

usvariousaspectsofcomputationinrelationtothecombinationoftherealandthevirtual.Inthe

project,themostimportantfactoristhefitnessfunctionfortheformgenerativeprocess.Basically,

suchvalueevaluationsmightbecategorizedintotwomaingroupsintheWebFrameproject:

physicalconditionsandaestheticsensations.Physicalconditionsmightberegardedassimilar

elementswhicharecommonlysimulatedinotherprojects,andformgenerationsareexecuted

undersuchphysicalconstraintsasspatiality,materiality,legalmatters,andsoon.

Figure2-15:TheWebFrame,physicalconditionsforformfindings.

Source:MakotoWatanabe(2000)

On the other hand, the method of handling aesthetic sensations is extremely remarkable.

Designersorarchitectspreferencesarereflectedinfitnessmeasurementsinthesamewayas

physical conditions. However, human sensations are always problematic in computer

programming dueto the fact thatnumerical formulations ofhumansensations areextremely

difficult.Whatisthebestforsomebody?Whatisaesthetics?IntheWebFrameproject,such

problems are resolved by assigning scores towards solutions derived from computers.

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Generatedformsbycomputersareevaluatedbythedesigners,andthefitnessscoresareused

inthenextsimulationforbettersolutions.After iterationoftheseoperations,computersmight

becomeabletosatisfyhumanpreferences.

Suchconceptofcomputationandhumanityisadvancedinanotherproject,theProgramofFlow.

Figure2-16and2-17explainthedesignflowandhumanevaluation.Theimportantdevelopment

inthisprojectisthewaytogradehumanpreferences.IntheWebFrame,designersorarchitects

givescorestocomputersafterformgeneration;meanwhile,inthecaseoftheProgramofFlow,it

ispossibletodecidedesignintentionsinadvancebygivinghandsketchesandtheirgrades.In

short,designconstraintisslightlypossible.Theabilitytoactivelypredictuserpreferencesallows

the system to 'design' new forms whichalready fit the conditions that the system has been

'trained'with.AlthoughusepreferencesarealsoconsideredinGenr8,thewayitisexecutedinthisprojectismoreflexibleandintuitiveintermsofusinghandsketchesfordesigngrades,which

manydesignersmightpreferratherthanparametricalconstraints.

Figure2-16:Formfindingsandhumanpreferences.


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Figure2-17:Theflowofhumanevaluationanddevelopmentofcomputation.


AnotherimportantaspectintheInductionDesignseriesisoptimisation.Althoughthisconceptis

notachievedatthispointintheWebFrame,inprojectsKeiriki1&2,theprogramforstructurally

optimisedshapegenerationhasbeendevelopedandadvancedsince2005.Briefly,thesoftware

creates objects with initial parameters and simple models, and the generated forms are

structurallyoptimisedafterwardasfigure2-18demonstrates.Oneofthemostremarkablefactors

intheKeirikiprojectsismaterialityandeconomicefficiency.Intheprogram,structuralmembers

are automatically selectedby computers in termsof load, and simultaneously optimised for

economicefficiency.Ornaments,whicharenotrelatedtostructure,andexcessivematerialsare

reducedandremoved.Inshort,the totalvolumeofmembersisapproximatelyminimized,and

this fact is important not only structurally, but also environmentally according to energyconsumption,includingembodiedenergy.Additionally,Keirikihasadvantagesintheaspectof

intuitive manipulations. Arbitrary modification of generated shapes is also possible, and the

softwarefindsoptimisedsolutionsthatcorrespondtosuchalternatives.

Figure2-18:Keiriki1-Formgenerationandstructuraloptimisation..


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Thus,theInductionDesignserieshasachievedandrealizedvariousimportantandremarkable

elementsindigitaldesign.Ontheotherhand,problemstobesolvedalsoremain.Firstly,asfar

ascreativityisconcerned,algorithmsusedforformgenerationintheInductionDesignseriesare

generative, but not evolutionary, even if produced shapes are solutions to given conditions.

Variousoptimised spider-web-like objects arecreated inevery softwareexecution; however,

other shapes are by no means generated by the software. Needless to say, the extent of

arbitrarinessindesignisanothermassiveacademictopic.Inadditiontosuchproblemsrelated

tocreativity,multi-objectivesituationsareanotherelement.Thistopicisalsodiscussedinthe

previoussection,anditmeansthatoptimisationundermulti-functionalconditionshasnotbeen

solved in the present circumstances, and is a substantial problem currently. Finally,

manageabilityasatoolisproblematic.ThesoftwarecreatedbyWatanaberequiresprofessional

knowledgeofstructure,anditiscompletelydifferentfromformalknowledgetooperatesoftware

includingCAD,3Dvisualizationsoftware,andsoon.Inotherwords,inrelationtomulti-objective

optimisation problems, deeply comprehensive professional knowledge would be required ifgenerative optimisation software, similar to Keiriki, which covers every condition including

structure,construction,environment,andsoon,wereinvented.Thereforethereare,ofcourse,

toomanyprerequisitesanditwouldnotbesuitableforallusers.

2.3 Summary and Analysis - Emergent Technologies and Sustainable Design

Manyaspectsofemergenttechnologyanddigitaldesignhavebeendiscussedintheprevious

sections with completed projects. Since computers became fundamental elements, digital

technologiesseemtohavebeengraduallydeveloped.Somesystemsderivedfromcomputers

canimitateevenbiologicalmechanisms.TheEvolvingVirtualCreatures(1994),byKarlSimsis

oneof themostremarkableexamples,demonstratingsuchdigitalinnovationsasexplainedin

theprevioussection.Itmightalsobeclaimedthatcomputersarenolongersimplyextensionsof

our handwork, but even an augmentation to our brains. As far as digital design in the

architecturalfieldisconcerned,generativedesigntoolssuchasGenr8orsoftwareinventedby

JohnFrazerdemonstratedesignabilitiesofcomputersascreativetools.

Although there are many remarkable aspects of digital technologies, the most significant

advantageofcomputationmightbetheabilitytoprocessahugeamountofinformationinashort

time.Thisisanexceedinglysimpletask,butasignificantlyimportantelement.Thehumanbrain

mightnotbeabletocalculateahugeamountofcomplicatedfactors,forinstance,environmental

simulations as Botsford has done in the Light House project. Application to solutions of

optimisation problems is also one of examples of such an advantage derived from digital

technologies.Infact,theprojectinOportobyLuisaCaldasprovesthatcomputerscancreate

betterresultsthanhumanbeingsundercertainconditions.Intermsofsustainability,especially

energyconsumption,thefactisremarkableforfuturedevelopment.

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However,itisalsotruethatsuchaccurateiterationsofenormouscalculationsbecomeobstacles.

Generallyspeaking,computersiteratetheirtasksuntiltheyfinishthem,oncecommandshave

beenput in,evenif thetasksarenotfinite,andmoreover,theycalculatealltasksinthesame

way.Ontheotherhand,humanbeingscanjudgethepossibilitiesoffinishingcalculations,or

choosethewaytocalculate.Suchjudgementsaredevelopedbytheirexperiencesandintuitions.

Computers do not have such abilities, and the only way to achieve them is well-designed

algorithmscreatedbydesigners.Thus,digitaltechnologiesarenotfeasibleforeveryoneifthey

arenotgoodatcomputing.Complexitiesandaccessibilitiestoemergenttechnologiesmightbe

one of the most serious disadvantages of digital design. In other words, such forefronttechnologiesarestillfordesignerswithspecifictechnologyskills.

As explained above, there are some advantagesand disadvantages in digital design itself.

Meanwhile,intermsofthefurtherdevelopmentofcomputationinsustainablearchitectureanditsdesignprocesses,severalproblemsalsoneedtobesolved.Firstly,multi-objectivesituationsare

one of the most problematic aspects. The perfectly optimised solution which satisfies every

objective cannot exist in environmental design. As demonstrated in the previous section, if

architectureisoptimisedintermsofacertaincondition,otherpurposesmightnotbeachieved

verywell.Thisis deeplyrelated tosustainable designaccordingto thefact thatpracticalities

suchasspatiality,materiality,cost,performance,andsoonaretheelementswhichdetermine

theenvironmentalperformanceofarchitecture.Everyaspectisactuallylinkedtoeachother,and

itisimpossibletopursueonlyoneelement.IntheprojectsofLuisaCaldasorGianniBotsford,

theysearchforsolutionsofmulti-objectiveoptimisationproblemsbyusingthelatestcomputer

technologies.However, theyarenotcompletely solved,andstill oneof themostcomplicatedtopics.

Userpreferenceisthesecondmatterintermsofnotonlyenvironmentaloptimisation,butalso

designaspectsofarchitecture.Itmightbeeasytoachievetargetsonlynumerically.Forexample,

thepurposetogainmaximumsunlightissimplysolvedwiththelargestwindoworglassbox.The

bestsolutionforthelowestheatlossesistoavoidarranginganywindowsonthewall.However,

thesesolutionsare,needlesstosay,notacceptablefordesignersorarchitects.Environmental

solutionsproducedbycomputersareonlyusefulwhentheyaresatisfiedwiththesesolutions.

Moreover,projectsaremorecomplicateddueto theexistenceofclients.Therefore,optimised

solutionsarenecessarytobegeneratedinaccordancewithuserpreferences.Intermsofsuch

conditions, interesting technology called the Interactive Evolutionary Computation (IEC) is

appliedtoGenr8andtheprojectsofMakotoWatanabe.Thesystemhandlesuserpreferences,

andreflectsthemintooutputsderivedfromcomputers.Ontheotherhand,LuisaCaldascontrols

designconstraintssuccessfullybyusingadifferenttechnologyinherproject inOporto.These

technologies are emergent, and still under development; therefore, further research is

fundamentallynecessary.

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Finally, the way to explain a real space in a digital spacemight be regarded as themost

problematicfactorinthedevelopmentsofemergenttechnologiesandsustainabledesign.Ina

virtualspaceof3Dvisualisationsoftware,drawnobjectsdonothaveanymeanings,andthey

arethebriefoutputofbinarydataonascreen.Ontheotherhand,usersregardsuchobjectsas

buildings, rooms, parts ofarchitecture,etc. This gap of recognition between computers and

humanbeingsisoneofthefactorsthatobstructcombinationsofdigitalandsustainabledesign.

As also explained above in this section, accuracies and practicalities including spatiality,

materiality and so onare exceedingly significant factors for environmental simulations. It is

impossibletosimulateenvironmentalperformanceinanycasewithouttheseelements.Infact,a

greatdealofdatamustbedeterminedforcalculationsinenvironmentalsimulationsoftwarelike

ECOTECT,TAS,etctoproducemeaningfulresults.

Suchtasksinenvironmentaltoolsmightbeacceptablein termsof theirmainaim.Meanwhile,theymight notbesuitable fordesign tools, becausedesignersorarchitects generally prefer

intuitive design processes. Therefore, specifications of material, zones, etc, seem to be

inconvenientasearlystagesofdesignprocesses.Thisisoneofthemostimportantreasonswhy

environmental simulationshave notbeen connectedwith generative design.TheGenerative

SystemcreatedbyLuisaCaldasisoneofthetoolstryingtodealwithsuchcomplexity;however,

successfulresultshavenotbeenachievedyet.

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Chapter 3: Responsive Faade Design System (RFDS)

In this chapter, the Responsive Faade Design System (RFDS), which optimises window

arrangementconsideringnaturaldaylightlevel,isdemonstrated.Thesystemisacombinationof

evolutionarycomputationandenvironmentalsimulation.Oneofthetechnologiesofevolutionary

computation, the genetic algorithm, is applied to the RFDS in order to investigate optimum

solutionsundercertainconditions.Inthefirstpartofthischapter,geneticalgorithmsarebriefly

explained includingbasic concepts, terms, implementations, and so on. Secondly, a simple

environmentaldesignmodelwhichisnotbasedonevolutionarycomputationistestedintermsof

furtherdevelopmentsandpotentialofthesystem.Thefinalpartofthischapteriscomposedof

theimplementationoftheRFDSandsomeresultsofexperimentalsimulationsdemonstratingitsadvantagesanddisadvantages.

3.1 Description of RFDSTheRFDSistheoptimisedwindowgenerationtool

consideringnatural daylight level at user defined

referencepoints.Inthepresentcircumstances,the

system worksonly in ECOTECTwhich iswidelyusedenvironmentalsimulationpackage.Byusing

the RFDS, it is no longer necessary to iterate

traditional top-down search methods. In

conventional ways, designers are required to

repeatdesignandenvironmentalsimulationsuntil

they obtain their target, which might not be an

efficient method. On the other hand, the RFDS

encouragesuserstofindsomeoptimumsolutions

once initial parameters have been inputted.

Various parameters are available for the

optimisation processes, so that it is possible to

generatewindowsonanywallsandinanysizes.

Thedetailed implementationanddesignabilityof

theRFDSisdemonstratedinthefollowingsections

ofthischapter.

GeneticAlgorithmOptimisationmethod

EcotectEnvironmentalSimulation

FinalResult

Fitness

No

Yes

Figure3-1istheflowchartoftheRFDSdescribing

two main components; a genetic algorithm (GA)

andECOTECT.Inthepresentsystem,Lua,which

is a simple programming language, is utilised to

Figure3-1:SystemflowchartoftheRFDS

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controlthewholeoperationintermsoffacilitatingthesystemdevelopmentandaccessibilityto

ECOTECT.Ageneticalgorithmisoneofsearchmethods,andiscurrentlyappliedtovarious

kinds of optimisation problems. At the first stage of the design process, some parameters

definedbyusers are passed into theGAgenerating an initial statement for theoptimisation

process.Afterward,theGAgoesthroughsomecomputationalsteps,andtheresultsachieved

by these steps are environmentally evaluated in ECOTECT. If the evaluations satisfy user

targets, executions are finalised and optimised solutions are outputted for user evaluation.

Meanwhile,thesameoperationsareiteratediftheevaluationsarenotadequateforthetargets.

TheRFDSattemptstogenerateenvironmentaloptimalsolutions.

3.2 The Genetic Algorithm Optimisation Method3.2.1 Overview of GAsGenetic algorithms (GAs) are search methods which draw heavily on the metaphor of the

mechanismsofbiologicalevolution.Inbrief,itisthesimulationofvirtualcreaturesinacomputer

derivingthecreatureinagroupbestadaptedtotheenvironment.Duetotheirexceedinglyhigh

performance in optimisation problems, the algorithms are utilised in various fields including

economics,socialsystems,schedulingsystems,andsoon.Thelistofapplicationsmightnotbe

byanymeansexhausted.

Originally,GAswereinventedanddevelopedbyJohnHolland,hiscolleagues,andhisstudents

inthe1960s.Incontrasttothepresentwidespreadapplicationstoproblemsolutions,theinitial

research of GAs was to simply study adaptation methods of natural selection and natural

genetics.Ontheotherhand, it isgenerallystated thatcreaturesareevolvedadjustingto the

surrounding environmentby iterating crossover, mutation, andselection. In other words, the

higherthefitnessofindividualstowardtheirenvironmentis,thehighertheprobabilitythatthey

willsurviveandproduceoffspringis.Suchanadaptationmechanismisaproblemsolutionitself,

and the reason orbasic concept for the variousapplications in different fields, although the

originalpurposeofGAsbyJohnHollandisdifferentfromthepresentutilisations.

3.2.2 Biological Terminology

Thefollowing is some of thebiological terminologywhich isused inGA implementations. In

termsofGAimplementations,itisnecessarytounderstandtechnicaltermsatthispoint.

Individual: Anautonomouspiececharacterisedbyachromosome. In this case, one possiblesolutiontothedesignproblem

Population: AgroupofindividualsPopulation Size:ThenumberofindividualsinapopulationGene:AfunctionalblockofDNA

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Allele: ApossiblevalueofageneChromosome:StringsofDNA.Inthiscase,alistofparametersLocus:TheplaceofageneinachromosomeGenotype:Geneticexplanationofachromosome(e.g.binarystrings)Phenotype: Thephysicalmanifestationofthegenotype

3.2.3 Implementations of a Simple Genetic Algorithm

As far asthesimplestGAs are concerned, there

are just three types of operators; Selection,

Crossover,andMutation.Figure3-2explains thesimplestGAsystem flow.After someparameters

areinitialised,thethreeGAoperatorsareiterated

untiltheresultsderivedfromthealgorithmssatisfy

terminal criteria definedbyusers.Thus,GAsare

executedbytwoverysimpleelementswhicharea

loopofGAoperatorsandterminalcriterion.Each

step of the algorithms is explained in detail as

follows:

Start

End

Yes

No

Initialisation

Evaluation

Selection

Crossover

Mutation

Evaluation

TerminalCriterion

InitialisationIn this step, some parameters including

population size, number of generations,

chromosomelength,andsoonareinputted.

Afterwards, the initial input randomly

generates genotype individuals of the first

generation. Especially, population size is

significant in terms of the operations that,

generally speaking, the longer the

chromosome length is, the bigger the

population size is. Additionally, the bigger

population size requires longer calculation

time until convergence. However, small

population sizes may result in premature

convergence.

Figure3-2:SimpleGAflowchart

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EvaluationFitnessscoresarecalculatedforfurtherselectionoffitterchromosomes.Oneofthemost

important aspects in this step is the Fitness Function which calculates the fitnessmeasurementofeachindividual.Thisoperation isdeeplyrelated totheefficiencyofthe

wholeGAflow;therefore,itneedstobedeterminedcarefully.

SelectionThefitter chromosomesin thepopulationare basically selected for reproduction.As in

biologicalevolution,thefitterchromosomesaremorelikelytobeselectedandreproduced

ineachgeneration.Meanwhile,lowerfitnesschromosomesarealsopossiblyselected,butwith a lower probability. This probabilistic selection dependson the selectionmethod.

Thereareseveraltypesofselectionsuchaseliteselection,rouletteselection,tournament

selection,etc.Eachselection typehasadvantagesanddisadvantages.Forinstance, in

eliteselection,thefitterchromosomesarecertainlyselectedinorder;however,premature

convergenceishighlypossible.IntheRFDS,acombinationofeliteselectionandroulette

selectionisused.

CrossoverCrossoverroughlymimicsthegeneticoperationofbiologicalrecombinationbetweentwo

chromosomes.Thefitterchromosomesarechosenbytheselectionoperator;however,it

isnoteffectiveenoughtoevolvethepopulation.Thecrossoveroperatorencouragesmore

variationbyexchanginggenesbetweentwochromosomes.

MutationThemutationoperatorrandomlyflipsorchangesgenesinachromosomebetweenalleles,

generallywithaverylowprobability.Chromosomesgeneratedbythecrossoveroperator

are basically copies of the parent chromosomes; therefore, premature convergence

possibly occurs. Chromosomes that have been mutated help to avoid premature

convergence.Generallyspeaking,themutationrateshouldbe1/L,whereListhelengthof

chromosome.Moreover,ifthemutationrateistoobig,thealgorithmbecomessimilartoa

randomsearch.

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Terminal CriterionInthisstep, the conditionsrequiredto terminate theGAis evaluated.If the process is

regardedasbeingcompleted,thefittestindividualinthegenerationisoutputtedasoneof

thepossibleoptimumsolutions.Theconditionsofconvergenceareasfollows:

- ifthefittestscoreinthepopulationsatisfiesthecertaintargetstargene- if the average fitnessscore in the populationsatisfies the certain target populationimprovement

- iftheincreaseordecreaseoffitnessscoresinthepopulationbecomesbelowacertainvalue-convergence

- ifthenumberofgenerationsbecomesoverthedefinedvaluefiniteiteration

3.2.4 Advantages and DisadvantagesGAscanbecharacterisedbysomefeaturessuchascodedparameters,globalheuristicsearch,

fitness-basedselection,andsoon.Geneticalgorithmsareaverygeneralsearchmethod,and

are not specific to any particular application. GAs are very effective at avoiding becoming

trappedinlocaloptima.Ontheotherhand,therearealsosomedisadvantagesaswell.GAsare

fitness-based, and it is necessary to calculate fitness scores for each individual every

generation;therefore,processingloadsareveryhigh.Inordertoproducegoodsolutions,bigger

population sizes are generally necessary, and the bigger population size causes higherprocessingloads.Meanwhile,themostseriousdisadvantage is thecomplexityandvariety of

initialparameters.Satisfactorysolutionsrequireaccurateinputofinitialsetupparameterssuch

aspopulationsize,mutationrateandsoon;however,theyarechangeabledependingonthe

objectives.Therefore,itisverycomplicatedandunpredictableforuserstodetermineaccurate

setupparameters.

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3.3 Testing Simple Model3.3.1 Non-Evolutionary Computational ModelIn this section, a simple test model of RFDS is demonstrated. The RFDS is the

evolutionary-baseddesignsystemfindingoptimumsolutionsunderuser-definedenvironmental

conditions. However, in this simple test, evolutionary computation and optimum simulation

methodsarenotyetoperated.Themainpurposeofthissimpletestistoobserveandanalysea

generativeandresponsiveprocessofthesystem.Therefore,someaspectsofthetestmodel

suchasparameters,outputs,andsoonareslightlydifferentfromthedevelopedRFDSinfurther

stages.Meanwhile,themainaimofthethesis,thatistoinvestigateanddevelopthewaysand

designprocesses foreffective naturalenvironment byusingemergentdigital technologies, is

consistent,andisreflectedinthismodelaswell.Figure3-3demonstratesanexampleoutput

generatedbythetestmodeloftheRFDSgeneratingcircularwindows.

Figure3-3:SimpletestmodeloftheRFDSgeneratingcircularwindows.

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3.3.2 Implementation

Thetestmodelissimplyimplementedas

figure3-4 explains.At thefirst operation,

someparametersareinputtedtoinitialise

the system, for example, the shape of

window, the number ofwindows, and so

on. After initialisation, new windows are

generated,andthesizeofeachwindowis

also increased until the environmental

targetvalueissatisfied.Theevaluationof

convergence is manipulated after every

operation in terms of accuracy towardtarget values. The following is detailed

informationofeachparameterforsystem

initialisation.

Start

End

Yes

No

Initialisation

GenerateWindow

TerminalCriterion

TerminalCriterion

No

Target Daylight Level [lux]Inthesystem,theterminalcriterionis

naturaldaylight level [lux]. Users are

allowed toset up the target valueofdaylight level at a reference point in

the model. It ispossible for users to

placethereferencepointanywherein

the model. In the case of this test

model, only one reference point is

allowedtobearrangedinthescene.

Radius of circular windowsNewwindowsaregeneratedinterms

of radius determined by users. The

size of generated windows is

increasedconstantlyin relation tothe

initial radius; therefore, the value of

the initial radius is one of the most

important elements affecting the final

output.

Figure3-4:Programflowchartofthetestmodel

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Maximum number of windowsItispossibletodeterminethemaximumnumberofwindowswhichwillbegeneratedinthe

model.Oncethenumberofwindowsinthesceneisbeyondthisvalue,newwindowsarenot

generated,andonlywindowsizeisincreasedinsteadinordertosatisfythetargetdaylight

value.

Number of nodes in a windowThis isa slightlytechnicaltermdemonstrating thenumberofpointscomposingawindow.

Basically, the testmodelgenerates approximately circular windowsas figure3-3shows;

however, the shape of windows actually depends on the number of nodes. Figure 3-5

explainsthe typesofwindowshapesinthedifferencesof thenumberofnodes.Themore

nodesthereare,thesmootherthewindowshapeis.Meanwhile,thelargenumberofnodescausesheavyprocessingloads,anditisnecessaryforuserstoconsiderthewholesystem

flowatthesametime.

Figure3-5:Differencesinthenumberofnodes

Thus, the simple test model is composed of only four parameters. In addition to this, the

evaluationofconvergenceisexceedinglysimple,comparedtootherenvironmentalsimulation

tools.Therefore, itistruethataccuratesimulationsandcomplexdesignarenotallowedinthe

system.Figure3-6isanexampledemonstratingthegenerativeprocessofthetestmodelwith

thevalueoftargetdaylightvalue.

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Figure3-6:Generativeprocessofthetestmodelshowingdaylightlevelsatthereferencepoint

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3.3.3 Results and System Developments

Figure3-7:Naturaldaylightlevelsimulationbyanalysisgrid.

Figure3-7isthedaylightanalysisoftheexamplegeneratedbythesimpletestmodel.Inthis

case,thereferencepointisplacedinthecentreoftheroomataheightof600mmfromthefloor.

The target daylight level is700 lux.As the figure shows, the system successfully generated

severalcircularwindowsuntiltheterminalcriterionissatisfied.Ontheotherhand,inthismodel,

only one reference point is available, and it is not accurate enough to control the internal

environment.Infact,theexampleresultdemonstratesthatalthoughthedaylightlevelaroundthe

referencepointisnearly700lux,theotherareasarenotunderspecificcontrol.Especially,the

lux levels of half of the part in the room are above the target level. They might have an

exceedinglyhighdaylightlevel,andmightnotbedesiredintermsofglare.

Meanwhile,dueto thefactthatthetestmodeliscreatedsimplytoexperimentandinvestigate

thegenerativeand responsiveprocessesofthesystemrelatedtonaturaldaylightlevel,other

environmentalandpracticalparametersarenotoperated.Intermsofthefurtherdevelopmentof

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thesystem,otherparametersarenecessary tosimulateandcontrolmorecomplexsituations.

For example, temperature could be applied to one of the other terminal criteria. Moreover,

energyconsumptionandgreenhousegasemissionsmightbenecessarytobeimplementedin

futuremodels. It isalso important tonote that such target criteria requiremorecomplicated

logical system because practical values including the physical properties of materials are

fundamentalforenvironmentalsimulations.

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3.4 Responsive Faade Design System

Figure3-8:AnimageoftheRFDSgeneratingpixel-likefaade.

3.4.1 System ImplementationAsmentionedintheprevioussectionofthischapter,theRFDSattempttogenerateoptimum

windowarrangementsunderuser-definedenvironmentalconditionsbyimplementingagenetic

algorithmwithinECOTECT.Figure3-9explainsthedetailedimplementationof thesystem.At

thefirstpoint,usersarerequiredtoinputtwoimportantparametersforoptimisationprocesses,

which are target daylight levels at each reference point, and the priority of each target.

Afterwards,initialparametersforGAoperationsneedtobegivenbyusers.TheGAoftheRFDS

isarelativelyconventionalGA,andthealgorithmisasexplainedintheprevioussection.Once

thesystemobtainsall of required initial parameters from users,possible solutionsunder the

givenconditionsaregenerated,andultimatelyproducedasa3Dmodelinthesceneatthefinal

stage.IntheRFDS,theterminalcriterionisthenumberofgenerations.Thatistosay,oncethe

programmehasperformedagivennumberofgenerations,programmeexecutionsarecancelled

tooutputsolutions.Inadditiontothefinal3Dmodel,theRFDScanoutputthefittestsolutionina

generation. Numerical results are also outputted as an Excel file format. The detailed

explanationsofeachelementrelatedtothesystemareasfollows:

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GenerateInitialGroup

Crossover

Mutation

Evaluation

Selection

OutputtheResult

Excel

No

GAmainloop

End

TerminalCriterion

Start

Yes

Wmf

Figure3-9:RFDSsystemflowchart.

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a) Reference PointUsers are allowed to place reference

points for measurement anywhere in a

scene. There are no limitations to the

numberofreferencepoints;however,itis

important for users to consider that the

morethereferencepointsareplaced,the

more difficult it is for the system to find

optimumsolutions.

b) Target Daylight Level [lux]The fitness scores are calculated in

termsoftargetdaylightlevelsdeterminedbyusers.IntheRFDS,targetlevelsare

notlisted,andarecompletelyuser-definedvalues.Daylightlevelisdemonstratedinluxdueto

thefactthat,atpresent,variousbenchmarksarepublishedrelatedtolightinglevels.Therefore,

usersareabletoeasilydecidetargetlevelsateachreferencepoint.

Figure3-10:Animageofarrangementofreferencepoints

c) Priority Level of TargetPrioritylevelisoneoftheelementsinfluencingfitnessmeasurementsaswellastargetdaylight

levels.Therearethreelevelsfromlowprioritytohighpriority,andusersarerequiredtogive

suchlevelstoeachreferencepoint.Accordingtothis,thesystemgeneratespossiblesolutionsgivingmoreimportantreferencepointshigherpriority.

d) Window Size and IntervalInthisimplementationoftheRFDS,windowsarearrangedinlinesasfigure3-8demonstrates,

sothatoutputisapixel-likefaade.Usersareabletochangewindowsizeandintervalbetween

windows.

e) GA Operators and Initial InputsItisnecessaryforuserstoinputsomeparameterstoinitialisetheGSwithintheRFDS.Similarto

otherGAs, population size, number ofgenerations, numberof elites, andmutation rate are

requiredasinitialinputs.Asdemonstratedintheprevioussection,therearethreetypesofGA

operators;selection,crossover,andmutation.IntheGAoftheRFDS,eliteselectionandroulette

selection are combined.Uniform crossover isutilised in the RFDS,and occurs toall parent

chromosomes.Uniformcrossoverisoneofthecrossoverstogeneratechildchromosomesby

randomlymixinggenesofparentchromosomes.Meanwhile,mutationisoperatedaccordingtoa

givenmutationrate.

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f) Output formats of optimum solutions and simulated dataTheRFDSoutputssolutionsas3Dmodelsinascene;therefore,itispossibletoexportthemto

CADand3Dvisualizationsoftware.Additionally,thefittestsolutionsineachgenerationarealso

outputtedasawmffilewhichisavectorformatimage.Numericaldataincludingfitnessscoresin

eachgeneration, simulateddaylight levelsateach referencepointandsoonareexported to

Excel.Thesedata encourageusers toanalysenotonlyenvironmentalperformance,but also

systemperformanceforfuturedevelopments.

3.4.2 Coding Method Genotype and PhenotypeOne ofthemost importantelements inGAs isthecodingmethod.In theRFDS,codingand

decoding isoperatedas figure3-11demonstrates.Before runningGA,the systemgenerates

virtualgridsonwallsusingthewindowsizeandintervaldefinedbyusers.Allelesusedinthe

systemarebinary;0and1.Therefore,achromosomemightbe01001010101101,andtheset

ofbinary codesof achromosome isalsoa set ofbinarycodes ofeachfaade. Ifeachgrid

explainedaboveisregardedasgenes,itispossibletoarrangewindowsonwalls.Inthepresent

system,1meansawindowand0meansnotawindow.Duetosuchdefinitionofphenotype

andgenotype,windowsarearrangedoneachfaadeasthefigureshows.

It is alsonecessary toconsider the length of chromosomes before the system proceeds. A

chromosome is simply a set of binary codes, and the coding method is also very simple;

therefore, the smallerwindow size and interval require larger number of binary codes for a

chromosomeresultinginanincreaseofcalculationloads.

Figure3-11:Compositionofachromosomeandcodingmethod.

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3.4.3 Fitness Function

AnexperimentalmethodisappliedtothefitnessfunctionoftheRFDSintermsofdesignabilities

ofthesysteminrelationtouserpreferences.Afitnessscoreissimplyatotalvalueofthefitness

measurements ofeach referencepoint.Thesystemisanoptimisation tool considering target

daylight levels; therefore, in thecase of theRFDS, thechromosomeofminimumdifferences

fromtargetlevelsisthefittestinapopulation.Inotherwords,inthissystem,fitnessfunctions

attempttoapproach0.Althoughthetotalofeachdifferencefromtargetlevelsateachreference

point is clearly a possible fitness function, the concept ofweighting factor is utilised in the

presentmodel.

Asfigure3-12demonstrates,weightingfactorsarecalculatedbasedonprioritiesofeachtarget

daylightlevel.Thehigherthepriorityis,thebiggertheincreaserateofweightingfactoris.Thefitnessfunctionofthesystemisthesumoftheweighteddifferences,whicharethecalculatedas

thedifferencesmultipliedbyweightingfactor.Itmeansthatthehigherthepriorityis,thebigger

thefitnessscoreislikelytobe.Accordingtothis,thesystemisexpectedtofindsolutionsgiving

prioritiesthroughGAoperations.

Figure3-12:ThefitnessfunctionoftheRFDSandfunction-basedweightingfactors.

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3.5 Testing the Responsive Faade Design System3.5.1 Optimisation Processes under Simple Conditions

Some experimental simulationswere completed byusing theRFDS to evaluate thepresent

performance of the system, and investigate the advantages and disadvantages for future

developments.Theimplementationsofthefirsttestmodelincludingtheroomandwindowsize

areasfigure3-13shows.Inthismodel,fourreferencepointswithoutpriorityareplaced,and

eachtargetdaylightlevelisasfollows:

Referencepoint1:200[lux]priorityLow




TheGAparameters are the populationsize= 20, number ofgenerations= 400, number of

individualsforeliteselection=2,andthemutationrate=0.03.Additionally,thechromosome

lengthundertheinitialisedwindowsizeis800accordingtothesystem.Allprioritieshavebeen

settolowtoensurethattheweightingdoesnotbiasresults.

Elevation

h=700

6,000

6,000

3,000

200

200

100

3,0001,500 1,500

Plan Perspective

Windowsize

Figure3-13:Simpleexplanationsofatestimplementation.

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Figure 3-14 and 3-15 demonstrate the generated optimum solution and the natural lighting

analysis.Ifreferencepointsarecategorisedintotwogroupswhicharethedarker(points1and

4)andthebrighter(points2and3)intermsofthetargetlightinglevels,thefiguresshowthatthe

RFDSsuccessfullyapproximatedanoptimumsolutionundergivenconditions.Inotherwords,

theleftareaoftheanalysisgridinfigure3-15isdarkerthanitsrightarea.Aslightinglevelsat

points2and3areabout640luxand830lux,anditcanbestatedthattargetlightinglevelsare

sufficientlyachieved. As figure3-16 also shows a relativelysuccessful achievement, lighting

levelsatpoints2and3approachtheeachtargetasthesimulationproceeds.

Lightinglevelsatreferencepoints1and4arenotsatisfactoryvalues.Thetargetlevelofpoint1

is200luxandthatofpoint4is400lux,whileactuallightinglevelsare,duetotheresult,about

380luxand570lux.Although figure3-17shows thatoverall fitnessscoresaredecreasedin

eachgeneration,lightinglevelsatpoints1and4arestableafterthe150thgeneration.Therearetwopossiblereasonsforsuchsystemidleness.Firstly,windowsarearrangedonallfourwallsof

thesimulatedmodel;therefore,it isdifficulttoachievelowlightinglevels.Secondly,reference

pointsareplacedcloselyobstructingdifferencesoflightinglevels.

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Lux

Point-1Target:200

379.064

Point-2Target:600Actual :642.727

Point-4Target:400Actual: 566.082

Point-3Target:800Actual: 828.176

Testedresultunder4referencepoints

Lightinglevelanalysisgrid

Figure3-14:Ageneratedsolutionandlightinganalysis.

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ReferencePoint-1

379.064[lux]

ReferencePoint-2

642.727[lux]

ReferencePoint-3

828.176

ReferencePoint-4

566.082

Lux

Figure3-15:Lightinganalysiswithfourreferencepoints.

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Generation

Figure3-16:Naturallightinglevelateachreferencepoint.

Generation

Figure3-17:Fitnessscoresofthefirstsimulatedmodel.

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Thefirstexperimentshowssomeinterestingaspectsofthesystem.Morecomplicatedconditions

areappliedtothesamemodelusedforthefirstsimulationtoinvestigatefurtherbehavioursof

the system. In the first simulation, four reference points are placed in the model, while the

numberof referencepoints is increased toeight in thesecondsimulation,and each point is

arrangedasfigure3-19shows.Thefollowingiseachtargetlevelatreferencepoints:

Referencepoint1:200[lux]priorityLow Referencepoint5:400[lux]priorityLow




Inthesecondsimulation,referencepointscanbecategorisedintotwogroupsinthesamewayasthefirstsimulationmodel.Points1,4,5,and8arethedarkergroupandpoints2,3,6,and7

arethebrightergroup.Accordingtothis,itisexpectedthatthehalfzoneoftheroom,wherethe

brightergroupisplaced,wouldgainmorenaturaldaylightthanthatofthedarkergroup.Asfaras

valuesofeachpointareconcerned,thedarkergroupshouldachievelowerlightinglevelsthan

thebrightergroup.However,figure3-20showsspatiallynocleardifferencesoflightinglevels

contraryto thefirstsimulation.Althoughfitnessscoresareconstantlyimprovedas figure3-18

demonstrates,daylightlevelsateachreferencepointdonotsuccessfullyapproacheachtarget

levelaccordingtofigure3-21.

Itisbecausereferencepointsareplacedtoodense

09-03-31 go kawakita eesb thesis 2008

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