




版权说明:本文档由用户提供并上传,收益归属内容提供方,若内容存在侵权,请进行举报或认领
文档简介
checkfor
updates
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
温馨提示
- 1. 本站所有资源如无特殊说明,都需要本地电脑安装OFFICE2007和PDF阅读器。图纸软件为CAD,CAXA,PROE,UG,SolidWorks等.压缩文件请下载最新的WinRAR软件解压。
- 2. 本站的文档不包含任何第三方提供的附件图纸等,如果需要附件,请联系上传者。文件的所有权益归上传用户所有。
- 3. 本站RAR压缩包中若带图纸,网页内容里面会有图纸预览,若没有图纸预览就没有图纸。
- 4. 未经权益所有人同意不得将文件中的内容挪作商业或盈利用途。
- 5. 人人文库网仅提供信息存储空间,仅对用户上传内容的表现方式做保护处理,对用户上传分享的文档内容本身不做任何修改或编辑,并不能对任何下载内容负责。
- 6. 下载文件中如有侵权或不适当内容,请与我们联系,我们立即纠正。
- 7. 本站不保证下载资源的准确性、安全性和完整性, 同时也不承担用户因使用这些下载资源对自己和他人造成任何形式的伤害或损失。
最新文档
- 培训课件 养老中心
- 车辆保养培训课件
- 心理健康在创业教育中的重要性
- 智能课堂打造高效学习新模式
- 探索教育心理学与现代科技在提升学习效果中的应用
- 教育心理学与创意课程的结合实践探索
- 中考语文写作专题《最动听的声音》范文6篇
- 抖音商户直播售后服务响应时限制度
- 全球教育变革中2025年跨文化交流能力培养的创新模式研究
- 八大城市教育行业教育培训机构市场调研与消费者需求分析报告
- 中广核培训课件
- 百度公司环境管理制度
- 特殊工时制管理制度
- 驻非洲员工管理制度
- 统编版三年级语文下册同步高效课堂系列第一单元复习课件
- 2025年高考生物真题(安徽)含答案
- 2025年高考真题-政治(黑吉辽卷) 含答案(黑龙江、吉林、辽宁、内蒙古)
- 工程内业资料管理制度
- T/QX 004-2020工业清洗作业人员呼吸防护用品选择、管理、使用和维护指南
- 摩托车协议过户协议书
- 河北省石家庄市2025年七年级下学期语文期末考试卷及答案
评论
0/150
提交评论