建筑结构的生成式智能设计方法研究进展 Generative AI design for building structures

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AutomationinConstruction157(2024)105187

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AutomationinConstruction

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Review

GenerativeAIdesignforbuildingstructures

WenjieLiao

aXinzhengLua,,

*,YifanFei

b,YiGub,YuliHuang

a

aKeyLaboratoryofCivilEngineeringSafetyandDurabilityofMinistryofEducation,TsinghuaUniversity,Beijing100084,China

bBeijingEngineeringResearchCenterofSteelandConcreteCompositeStructures,TsinghuaUniversity,Beijing100084,China

ARTICLEINFO

ABSTRACT

Keywords:

Designingbuildingstructurespresentsvariouschallenges,includinginefficientdesignprocesses,limiteddata

Buildingstructuraldesign

reuse,andtheunderutilizationofpreviousdesignexperience.Generativeartificialintelligence(AI)hasemerged

Datafeaturerepresentation

GenerativeAIalgorithm

Designevaluation

Intelligentoptimization

asapowerfultoolforlearningandcreativelyusingexistingdatatogeneratenewdesignideas.Learningfrompastexperiences,thistechniquecananalyzecomplexstructuraldrawings,combinerequirementtexts,integratemechanicalandempiricalknowledge,andcreatefreshdesigns.Inthispaper,acomprehensivereviewofrecentresearchandapplicationsofgenerativeAIinbuildingstructuraldesignisprovided.Thefocusisonhowdataisrepresented,howintelligentgenerationalgorithmsareconstructed,methodsforevaluatingdesigns,andtheintegrationofgenerationandoptimization.ThisreviewrevealsthesignificantprogressgenerativeAIhasmadeinbuildingstructuraldesign,whilealsohighlightingthekeychallengesandprospects.Thegoalistoprovideareferencethatcanhelpguidethetransitiontowardsmoreintelligentdesignprocesses.

1.Introduction

Thedesignofbuildingstructuresisanuancedtaskthatnecessitatestheblendingofempiricalandmechanicalknowledge.Engineershavebeenconsistentlydiscovering,developing,andimplementingsophisti-catedcomputer-aideddesigntechnologiestostreamlinetheprocessandtargetefficientandreliablestructuraldesigns.

Inthe1980s,intelligentdesignmethodswereproposedbasedonexpertsystemalgorithms[

62

].Inthefollowingyears,aseriesofintel-ligentdesignmethodsbasedonbiologicallyinspiredalgorithmsemergedtogetherwiththeconceptofgenerativedesign[

80

,97

,

102

].Advancementsincomputertechnologydrovethedigitizationandautomationofbuildingstructuraldesignsforwardatanunprecedentedpace.However,expertsystemalgorithmsandbiologicallyinspiredal-gorithmsfindthemselvesgrapplingwithissuesindatalearning,designruleencoding,anddesignefficiency,whichhampertheirwiderapplication.

Inrecentyears,artificialintelligence(AI)technologies,notablydeeplearning,havemaderemarkableprogressinlearningfromexistingdataandgeneratingnewdesigns,whichsetsthemapartfromconventionalexpertsystemsandbiologicallyinspiredalgorithms.GenerativeAIdesign,whichharnessesmachinelearningalgorithmstolearnfromdataandunfoldsnewcontent,hasbeenidentifiedasoneofthetoptentechnologytrendsfor2023[

12

].Intelligentgenerativetechnologies,

suchasDALL-E[

8

]andChatGPTbyOpenAIandAlphaFoldbyDeep-Mind[

41

],havedemonstratedtheversatilityofgenerativeAIinvariousfieldsandhavebecomeacutting-edgeareaofresearch.

Buildingstructuraldesignsrelyprimarilyondrawingsthattranslateintostructuredorimagedata.Engineerscompletethedesignprocessbasedonanarchitecturaldesignwithmultipleconstraintssuchascomplianceandeconomy.Theend-to-enddesignprocessofgenerativeAIisconsistentwiththatofbuildingstructuraldesignbyengineers[

104

],equippedwithpowerfullearningandgeneratingcapabilitiestotackletheintricatepuzzlesofintelligentdesign.Therefore,ithasbecomeanewresearchtopic.

SeveralcomprehensivereviewshavebeenundertakentoexploretherecentadvancementsinintelligenttechnologiesbasedonAIinbuildingstructuraldesign,analysis,construction,andmaintenance(

Table1

).ThesereviewscontributesignificantlytotheunderstandingofvariousAItechnologies,includingmachinelearning,deeplearning,GenerativeAdversarialNetworks(GANs),GenerativePre-trainedTransformer(GPT)models,andGraphNeuralNetworks(GNNs).Bydelvingintothesetopics,thesereviewsshedlightonthepotentialapplicationsandbenefitsoftheseAItechnologiesinbuildingstructures.

Intherealmofdesignandanalysis,severalnotablereviewstudieshavebeenconducted.AldwaikandAdeli

[59

]conductedareviewfocusingontheoptimizationofhigh-risebuildingstructures,utilizingnature-basedoptimizationapproachesliketheneuraldynamicsmodel

*Correspondingauthor.

E-mailaddress:

luxz@

(X.Lu).

/10.1016/j.autcon.2023.105187

Received11June2023;Receivedinrevisedform15October2023;Accepted5November2023

Availableonline11November2023

0926-5805/©2023ElsevierB.V.Allrightsreserved.

2

W.Liaoetal.

Table1

ScopesofexistingreviewstudiesrelatedtoAI-assistantbuildingstructuraldesign.

Dsgn.

Anlys.

C&O&M

DL

(inc.

Gen.

AI)

ML

Optim.

DRL

Aldwaik&

Adeli[

59

]Chietal.

[37

]Amezquita-

Sanchez

etal.[

49

]

LiandAdeli

[

120

]

Afzaletal.

[

58

]

Sunetal.

[35

]Pizarroetal.

[

77

]

Badugeetal.

[

95

]

Wuetal.[

11

]Zakian&

Kaveh[

69

]Yükseletal.

[

65

]

Wangetal.

[

19

]

Omranyetal.

[

34

]

Sakaetal.

[9

]Jiaetal.

[110

]Topuz&

ÇakiciAlp

[

14

]

Koetal.[

42

]Ours

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

Dsgn.=Design,Anlys.=Analysis,C&O&M=Construction&Operations&Maintenance,DL=DeepLearning,Gen.AI=GenerativeAI,ML=MachineLearning,Optim.=Optimization,andDRL=DeepReinforcementLearning.

andgeneticalgorithms.Chietal.[

37

]andOmranyetal.[

34

]exploredtheintegrationofbuildinginformationmodeling(BIM)withsmarttechnologiesinstructuraldesignpractice,suchasstructuraldesign,planning,analysis,andoptimization.Afzaletal.[

58

]providedanoverviewofstructuralcomponents,optimizationstrategies,andtheutilizationofvariouscomputationaltoolsinRCstructuraldesignopti-mization.Pizarroetal.

[77

]reviewedrule-andlearning-basedmethodsforintelligentrecognitionanddesigninarchitecture.Sunetal.

[35

]delvedintotheresearchonmachinelearningapplicationsinbuildingstructuredesignandperformanceevaluation.ZakianandKaveh[

69

]presentedanoverviewofseismicdesignoptimization,encompassingcommonsolutionmethods,optimizationproblemtypes,andoptimiza-tiongoals.Yükseletal.

[65

]analyzedtheresearchstatusofgenetical-gorithms,fuzzylogic,andmachinelearningmethodsinengineeringstructuredesign,coveringareassuchasdesigngeneration,evaluation,optimization,decision-making,andmodeling.Wangetal.[

19

]con-ductedareviewspecificallyontheapplicationofAItechnologyinmaterialandstructuralanalyseswithinthefieldofcivilengineering.TopuzandÇakiciAlp

[14

]evaluatedthecurrentstateofmachinelearningincomputer-aideddesign,engineering,andmanufacturingofarchitecture.Lastly,Koetal.

[42

]introducedadvancementsinauto-matedspatiallayoutplanningcombinedwithAI.

SeveralreviewstudiesalsoexploredAIapplicationsintherealmofconstructionandmaintenance.Amezquita-Sanchezetal.[

49

],andLiandAdeli[

120

]providedaninsightfulreviewandoutlookontheuti-lizationofmachinelearningtechnologiesinstructuralsystemidentifi-cation,healthmonitoring,vibrationcontrol,design,andoptimization.Badugeetal.[

95

]introducedthelatestadvancementsinapplyingAItechnologies,includingmachinelearninganddeeplearning,inbuilding

AutomationinConstruction157(2024)105187

designandvisualizationwithinthecontextofthebuildingindustry4.0.Wuetal.[

11

]presentedacomprehensivestate-of-the-artreviewontheutilizationofGANstotacklechallengingtasksinthebuiltenvironment.Sakaetal.[

9

]assessedthepotentialofGPTmodelsintheconstructionindustry,identifyingopportunitiesfortheirimplementationthroughouttheprojectlifecycle.Jiaetal.

[110

]exploreddiverseapproachesforconstructinggraphdatafromcommonconstructiondatatypesandhighlightedthesignificantpotentialofGNNsfortheconstructionindustry.

However,thereisanoticeablegapintheanalysisanddiscussionsurroundingthedevelopmentandapplicationofgenerativeAIinthestructuraldesignofbuildings.Thisgapresultsinalackofsystematicandtargetedresearchwithinthisfield.Uponreviewingexistingstudies,itbecomesevidentthattheresearchfindingsinthisareaarefragmentedandincomplete.Researchersencounterchallengesinaddressingspecificissues,whiletheentrybarriersfornewresearchersenteringthisfieldareontherise.ThesefactorscollectivelyimpedefurtherprogressandhindertheadvancementofgenerativeAIinbuildingstructuraldesign.Therefore,thisstudyaimstoprovideanoverviewofexistingintelligentstructuraldesignmethodsandfocusesontheachievementsandappli-cationsofgenerativeAIinbuildingstructuraldesign,whichhasun-dergonerapiddevelopmentoverthepastthreeyears.TheanalysisofexistingstudiesrevealsthattheprimaryresearchareasingenerativeAI-basedstructuraldesignencompassdatarepresentation,generational-gorithms,evaluationmethods,andtheintegrationofAIgenerationwithdesignoptimization.Byconductinginvestigationsinthesepertinentdomains,theadvancementofAI-basedbuildingstructuraldesigntech-nologycanbeeffectivelyfacilitated.Accordingly,thisstudywillpre-dominantlycenteraroundshowcasingtheaccomplishmentsofexistingresearchwithinthesespecificareas.

Section2

providesamacro-introductiontocurrentAImethodsandtheirapplicationsinbuildingstructuraldesign.

Sections3–5

reviewthedatafeaturerepresentation,generativealgorithmconstruction,andevaluationmethodsingenerativeAI.

Section6

introducesmethodscouplingintelligentgenerationandoptimization,andSection7presentstypicalengineeringapplicationcasesofintelligentbuildingstructuredesign.

Section8

providesrelevantconclusionsandprospectsforthefuturedevelopmentofthisresearchfield.

Consequently,thisstudyhighlightsthepotentialofgenerativeAIinbuildingstructuraldesignandtheneedforfurtherresearchinthisfield.ThedevelopmentandapplicationofgenerativeAIinstructuraldesigncansignificantlyenhancetheefficiencyandaccuracyofthedesignprocess,leadingtomoresustainableandsaferbuildingstructures.

2.Introductiontoartificialintelligence(AI)methodsandapplicationsinbuildingstructuraldesign

2.1.AImethods

ResearchonAImethodshasbeenconductedforaconsiderableperiod.Inthisstudy,thetimelinewaspartitionedusing2012asareferencepoint,whichmarksthe“deeplearningera”owingtothehighlyeffectiveimageclassificationalgorithmAlexNet[

4

].AccordingtoYükseletal.[

65

],asillustratedin

Fig.1

,mostAImethodsbefore2012werecategorizedasclassicalAImethods,whereasthoseafter2012werereferredtoasmodernAImethods.Thisreviewfocusesprimarilyondeep-learningalgorithmsdevelopedafter2012.

(1)ClassicalAImethods

Representativemethodsincludeknowledge-basedsystemsandbio-logicallyinspiredalgorithms.Knowledge-basedsystemsincludeexpertsystems[

23

],fuzzylogic[

48

],andgenerativedesigngrammars[

1

].Biologicallyinspiredalgorithmsincludegeneticalgorithms[

31

],parti-cleswarmoptimization[

84

],andcellularautomata[

25

].

3

100%

0%

W.Liaoetal.

AutomationinConstruction157(2024)105187

Artificialintelligence(Al)

classicalAl

earnngM)

DeepeargD

Artifcialneura

convolutional

neura

networks

(CNN)

cellularautomata(CA)

Differentialevolution

algorithm(DEA)

particleswarm

ptimization(pso)FuzzYlogic

Recurrent

neura

networks

(RNN/LSTM)

BiologicaIIY-inspired

algorithms

Graph

neura

networks

(GNN)

knowledge-based

systems

Geneticalgorithm

network(ANN)

Expertsystem

ModernA

Machinel

GA

Fig.1.Artificialintelligence(AI)methodswidelyadoptedinbuildingstructuraldesign.

(2)ModernAImethods

advancementshaveprovidedengineerswithenhancedinitialdesignsolutions,elevatingbothdesignefficiencyandquality.Theinsightsfrom

Since2012,theAIfieldhasundergoneacomprehensivedevelop-mentdrivenbydeeplearning,whichcanhandlebigdata,extracthigh-dimensionalfeatures,andsignificantlyimprovelearningfromdata[

111

].Typicaldeeplearningmethodsincludedeepconvolutionalneuralnetworks(CNNs)[

4

,

111

],deepgraphneuralnetworks(GNNs)[

99

],anddeeprecurrentneuralnetwork/longshort-termmemory(RNN/LSTM)[

40

,

90

].Inaddition,advanceddeeplearningalgorithmshavebeendevelopedusingthesemethods,suchasthevariationalautoencoder(VAE)

[24

],generativeadversarialnetwork(GAN)[

38

],transformer[

10

],anddiffusionmodels

[28

].Inrecentyears,generativeAI[

10

,

28

,

38

,

96

]hasbecomearepresentativetechnologicaladvancementindeeplearningresearch.TheemergenceofgenerativeAIordeepgenerativemethodshasopenednewpossibilitiesforintelligentdesign. Presently,modernAImethodshavefoundextensiveapplicationsinvariousengineeringfields,includingarchitecture,mechanicalengi-neering,andaerospaceengineering

[65

].Theyhaveproventobeeffectiveingeneratingarchitecturallayouts,renderingarchitecturalimages[

27

,39

,

56

,57

,63

,

64

,79

,89

,

101,107

],generatingwheelstruc-tures[

92

],andaerodynamicshapesofaircraft[

22

].These

theserelatedstudiesserveascrucialreferencesforthedevelopmentofgenerativeAI-basedintelligentdesigninbuildingstructures.

2.2.ApplicationofAIinbuildingstructuraldesign

Thebuildingstructuraldesignprocesscanbedividedintothreeprimarystages:conceptualdesign(schemedesign),detaileddesign(preliminaryoroptimizationdesign),andconstructiondrawingdesign.Amongthesestages,theconceptualdesignstagesignificantlyimpactsthefinaldesignoutcomeandreliesheavilyondesignexperienceandknowledge(asshownin

Fig.2

).Therefore,intheconceptualdesignstage,AIplaysasignificantroleinthestructuraldesignofbuildings.

Thecoretaskofaconceptualbuildingstructuraldesignisdesigngeneration(alsoknownassynthesis).AccordingtoMaher[

62

],buildingstructuraldesignsynthesismethodsmainlyrefertothegenerationofcorrespondingdesignsguidedbyspecificknowledgefragments,suchasheuristicrulesanddescriptiveframeworks.Inaddition,withthedevelopmentandapplicationofgenerativeAItechnologiesthatcanlearndatafeaturesandgeneratenewdesigns,currentstructuraldesign

Highinfluence

Lowinfluence

Lowexpenditure

Highexpenditure

Result

conceptual

Design

precttime

construction

drawing

design

Fig.2.Influenceofthebuildingstructuraldesignphaseonprojectcost[

15

].

pg(pxg)(x)

GeGneaetreaedaataSaSmapmlepdleaata

pdatpad(atxa)(x)

4

W.Liaoetal.

synthesismethodsmainlyincludeheuristicsearch-based,descriptivegrowth-based,andgenerativeAIlearning-baseddesigns(

Table2

).

Heuristicsearch-baseddesignsprimarilyusebiologicallyinspiredcomputationtechniques,suchasgeneticalgorithms,particleswarmoptimization,andcellularautomata[

2

,

5-7

,

33

,

43-45

,

54

,

61

,80

,

88

,

91

,

93

,

112

,

119

].Descriptivegrowth-baseddesignsprimarilyusegenerativedesigngrammar[

1

,

16

,

26

,53

,

87

,

108

].Theseintelligentdesignmethodshavebeenwidelyappliedinmultipleareasofbuildingstructuraldesign,suchasmulti-schemesearch,comparativeselection,andmaterialoptimization,andhavepromotedtheprogressofdigitali-zationandautomationinstructuraldesign.However,theseclassicalmethodsfacedifficultiesregardingdatalearninganddesignefficiency.Incontrast,generativelearningisanintelligentdesignmethoddomi-natedbygenerativeAI,whichhaspowerfuldesigndatalearningandefficientnewdesigngenerationcapabilitiesandhasbeencontinuouslydevelopingandadvancinginrecentyears.

ThemainarchitectureofthegenerativeAIalgorithmsisshownin

Fig.3

,whichincludesadataset,aneuralnetworkmodel,andalossfunctionmodule.ThedevelopmentandapplicationofgenerativeAIinbuildingstructuraldesignwillmainlyfocusonthesethreeparts,andadetailedanalysisandsummaryarepresentedin

Sections3-5

.

Tofurtherunderstandthecurrentstateofresearchonintelligentstructuraldesign,weconductedasearchoftheWebofScienceCoreCollectionforrelevantpapers.First,asearchwasconductedonAI-basedstructuraldesignmethods,yielding188searchresults.Subsequently,searcheswereconductedonclassicalAI-basedandmodernAI-basedstructuraldesignmethods,resultingin130and74searchresults,respectively.Thesearchformulaearelistedin

Table3

.

Fig.4

(a)showsthechangeinthenumberofresearchpapersonAI-basedbuildingstructuraldesigns,indicatingacontinuousincreaseinintelligentdesignresearchovertheyears,withasignificantincreasesince2020.BycomparingtheclassicalandmodernAImethods,asshownin

Fig.4

(b),itcanbeobservedthatclassicalAIwasthemain-streamresearchmethodforintelligentdesignbefore2012.Since2012,modernAIhasrapidlydeveloped,andtherelatedresearchpapershaveincreasedthreefold;however,somedifferencesstillexistcomparedtoclassicalAImethods.Since2020,generativeAItechnologyhassignifi-cantlyimproveditscapabilitiesandhasbeendevelopingin-depthinthefieldofstructuraldesign,withresearchpapersonparwithclassicalAI

AutomationinConstruction157(2024)105187

methodsandaccountingfor48%ofthetotalresearch,showingatrendofsurpassingclassicalAImethods.

Insummary,byanalyzingtherecenttrendsinintelligentbuildingstructuraldesign,AI-basedmethodsusingdeeplearninghavegraduallybecometheprimaryresearchfocus.Thesemethodsarereferredtobydifferentnames,includingintelligentgenerativedesign,deepgenerativedesign,intelligentdesign,anddeep-learning-baseddesign.However,thecoretechnologyofthesemethodsprimarilyinvolveslearningfromexistingdesigndata,empiricalknowledge,andphysicalprinciplestomastertheabilitytogeneratenewdesignsintelligently.AsthisisconsistentwiththeessenceofgenerativeAI,thisstudycollectivelyreferstothesemethodsasgenerativeAI-basedintelligentdesignsforbuildingstructures.

3.Datafeaturerepresentationanddatasetconstruction

Therepresentationofthedatafeaturesiscrucialinbuildingstruc-turalintelligentdesign.Thisrepresentationisrelatedtothegeometricandmechanicalcharacteristicsofthestructuraldesignandissubjecttothelimitationsofdeep-learningalgorithms.Inbuildingstructuraldesign,datafeaturesmainlyincludetopological,pattern,andsizefea-tures.Topologicalfeaturesrefertothelayoutofstructuralcomponentsinspaceandtheconnectionrelationshipbetweencomponentstoeffectivelyresistoveralllateralandverticalloads.Patternfeaturesrefertothelocalgeometricconfigurationofcomponentsthatmustbeinspecialpatterns(suchasL-shaped,T-shaped,andC-shaped)toresisttheloadofthelocalcomponent.Sizefeaturesrefertothecross-sectionalsizesofstructuralcomponents.

GenerativeAIalgorithmsutilizeconvolutionalneuralnetworks(CNNs)toprocessdataastensors(including1-dimensionalvectors,3-dimensionalimages,andn-dimensionalmatrices).Incontrast,graphneuralnetworks(GNNs)aresuitableforprocessingdataingraphs(nodeandedgerepresentations,with1-dimensionalvectorfeaturesembeddedinnodesandedges).Tensordatacanbetterexpresspatternsandsizefeaturesinthedesign,whereasgraphsaremoreconducivetoexpressingtopologicalfeatures.

Therefore,generativeAIalgorithmscaneffectivelyextractfeaturesandlearnbyappropriatelyrepresentingthedatafeaturesinbuildingstructuraldesign.Thissectionreviewsthedatafeaturerepresentation

Table2

Comparisonofthreemainstreamintelligentdesignmodes.

Heuristicsearch-basedDescriptivegrowth-basedGenerativeAIlearning-based

Concept

description

Concept

illustration

Utilizingtheprovidedinitialsolutionwithinpredeterminedboundaryconstraints,the

iterativeprocessaimstosystematicallysearchfortheoptimalresult.

Theprocessinvolvesestablishinggenerationrulesandconstraints,commencingfromtheinitial

design,andsubsequentlyiterativelyoptimizingitbasedonthedescriptivegenerationrules.

Byleveragingexistingdata,AIcomprehendsthe

mappingpatternsbetweenbuildingandstructure.

TrainedAIgeneratesacorrespondingstructuraldesignforanewbuildingdesigninasinglecomprehensive

step.

Representative

methods

Geneticalgorithms

Generativedesigngrammar

GAN

Initialstructuraldesign

Required

Partialrequired

Notrequired

Manually

definedrules

Required

Required

Partialrequired

Iterationdesign

Required

Required

Notrequired

Learning

Notrequired

Notrequired

Required

Performance

High

Relativelyhigh

Medium

Efficiency

Relativelylow

Medium

High

5

Input

output

physical

constraint

Dataloss

W.Liaoetal.

AutomationinConstruction157(2024)105187

Datasets

Neuralnetwork

Evaluation

Groundtruthdata

embeddedphysicallaws

andempiricalrules

Discriminator

Rea

Fake

Generativeneuralnetwork

Fig.3.AlgorithmframeworkofgenerativeAI.

Table3

SearchobjectsandformulasusedintheWebofSciencecorecollectionandtheresultingnumber(accessedonApril20,2023).

Searchobjects

Searchformulas

Numberof

studies

ClassicalAI-based

buildingstructuraldesign

(TS=(“building”OR“architecture*”)

ANDTS=(“structur*design”)ANDTS=

(“intelligen*”OR“automat*”OR

“artificialintelligence”OR“design

intelligence”OR“generative”OR

“optimiz*”OR“explorat*”)ANDTS= (“expertsystem*”OR“fuzzylogic”OR“geneticalgorithm”OR“generative

grammar”OR“evolution”OR“particleswarmoptimization”OR“cellular

automata”))

(TS=(“building”OR“architecture*”)

ANDTS=(“structur*design”)ANDTS=(“intelligen*”OR“automat*”OR

“artificialintelligence”OR“design

130

ModernAI-based

buildingstructuraldesign

intelligence”OR“generative”OR

“optimiz*”OR“explorat*”)ANDTS=

(“machinelearning”OR“deeplearning”

OR“neuralnetwork”OR“generativeadversarialnetwork”OR“variational

74

AI-basedbuildingstructuraldesign

autoencoder”OR“transformer”OR“diffusionmodel”))

Thesumofthesetwoformulas

188

methodsanddatasetconstructionusedinexistingresearch.

3.1.Datafeaturerepresentation

Table4-1

and

Table4-2

summarizethetensor-basedandgraph-baseddatarepresentationmethodsusedinrelatedresearch,and

Fig.5

illustratesthetypicalrepresentationmethods.Tensor-baseddatarep-resentationismorecommonlyusedincurrentresearch,whereasgraph-basedrepresentationsarelesscommon.Tensorrepresentationismoreintuitiveandstraightforward,andthusitismorewidelyusedincurrentgenerativeAI-basedintelligentdesignmethods.Althoughgraphsarebettersuitedforexpressingtopologicalfeatures,theyrequirecomplexandsophisticateddesignstorepresentpatternandsizefeatures.

(1)Tensor-basedrepresentation

(2)Graph-basedrepresentation

3.2.Datasetconstruction

Afterdeterminingthedatafeaturerepresentationmethod,thecollecteddatawereprocessedtoconstructthedataset.

Table5

providesdetailedinformationonthedatasetsdescribedinthecurrentliterature,includingthedesignobject,datacontent,dataquantity,andwhethertheyareopensource.Currently,theconstructionofdatasetsinbuildingstructuralintelligentdesignisrelativelyscarce,andtheintroductionofdatasetsisnotsufficientlycomprehensive.Onesignificantreasonisthelimitedavailabilityofpubliclyaccessiblefielddata.

Dataaugmentationmethodsarewidelyusedtoaddresstheissueoflimiteddata.(1)Imagedataaugmentationmethods,suchasflipping,symmetry,andtranslationofimages,caneffectivelyincreasethedatauptofourtimestheoriginaldatawithoutchangingtheattributesofthestructuraldesign[

13

,

104

].Augmentingdatathroughoverallimagesegmentationcanincreasethedatabyhundredsoftimesbypartitioningthecompleteddesignimageintoseverallocaldesignimageswithoutchangingthebuildingandstructuralsize

[71

,76

].(2)Tensordataaugmentationcangeneratehundredsorthousandsofdatabyparametricdesignforstructuraltopologyandcomponentsize[

30

,46

,72

].(3)Graphdataaugmentationcanalsobeperformedbasedonthespatialrelativecoordinatepositionineachnodeofthegraph,wheredatacanbeaugmented>3000timesbyoveralltranslation,flipping,androtatingcoordinates[

70

,74

].

3.3.Summaryofdatafeaturerepresentation

Currently,thereexistsignificantvariationsintheextractionandrepresentationofdatafeaturesfordifferentbuildingstructureforms.Whetheritinvolvespixel