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AutomationinConstruction157(2024)105187
Contentslistsavailableat
ScienceDirect
AutomationinConstruction
journalhomepage:
/locate/autcon
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
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