Page 195 - 《水资源与水工程学报》2024年第5期
P. 195
!35 "!5 # & ' ( ) & * + , - Vol.35No.5
2024 $ 10 % JournalofWaterResources&WaterEngineering Oct.,2024
DOI:10.11705/j.issn.1672-643X.2024.05.23
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MNOPQ:U455 RSTUV:A RWXQ:1672643X(2024)05019110
TBM excavationdatapreprocessingmethodandengineeringcaseverification
2
1
2
1
1
NIEQitan,XIAOHaohan,LIUFei,LIULipeng,NIURuiqiang
(1.GuangdongYuehaiYuexiWaterSupplyCo.,Ltd.,Zhanjiang524000,China;
2.ChinaInstituteofWaterResourcesandHydropowerResearch,Beijing100048,China)
Abstract:Thefullfacehardrocktunnelboringmachine(TBM)automaticallygeneratesmassiveexcava
tiondataduringthetunnelconstructionprocess.Properscreeningandcleansingofexcavationdataiscru
cialtodataquality,whichalsohasgreatguidingsignificancefortheintelligentconstructionoftunnelen
gineering.Therefore,basedonthecharacteristicsofTBMexcavationdataintheYinchuoProject,anin
tegratedTBMexcavationdatapreprocessingmethodisproposed,whichincludescompleteexcavationseg
mentextraction ,internalexcavationsegmentation,andexcavationparameternoisereduction.Toverify
theeffectivenessoftheproposeddatapreprocessingmethod ,atorquecutdepthindex(TPI)prediction
modelisdevelopedbythelongshorttermmemory(LSTM)algorithm,whichhasstrongtemporalpredic
tioncapabilities.Theresultsdemonstratethattheproposeddatapreprocessingmethodcansignificantly
improvedataqualityandenhancethepredictionaccuracyofdeeplearningmodels.Forthevalidation
2
dataset ,R increasesfrom0.503to0.721,R′ascendsfrom0.809to0.900,andMREplummetsfrom
3.107to0.096.Theseresearchachievementsbearprofoundimplicationsforenhancingtheprecisionand
reliabilityofTBMtunnelingdata ,therebyofferinginvaluableinsightsforfurtherexplorationintherelated
domain.
Keywords:tunnelboringmachine(TBM);datapreprocessing;kerneldensityestimation;Butterworth
filtering;longshorttermmemory(LSTM)
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