基于支持向量機(jī)回歸的黃土濕陷性預(yù)測(cè)研究

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關(guān)鍵詞：黃土濕陷性；物性指標(biāo)；相關(guān)性；支持向量機(jī)；預(yù)測(cè)模型

中圖分類(lèi)號(hào)：TU444 文獻(xiàn)標(biāo)志碼：A 文章編號(hào)：1005-8249（2025）03-0073-05

DOI：10.19860/j.cnki.issn1005-8249.2025.03.014

Abstract：Toadressthechallenges inrapidandacurate predictionof loess collapsibilityin Central Gansuusingconventional methods，asupportvectormachineregression-based predictive modelwas developed.Leveragingdata fromarailwayprojectin thisregion，soil samplescollctedfrom manuall excavated testpitsunderwentcomprehensivelaboratorygeotechnicaltestingto obtainkey physical propertyidices.Correlations between losscollpsecoeffcientsandphysicalindicesweresystematicaly investigated through literatureanalysisandmathematical statistics.AGaussankemelfunction-basedSVMpredictionmodel wassubsequentlyestablished.The modelwastrainedwith45experimentaldatasets，while15datasetswereutilized forvalidationand eroranalysis.Resultsdemonstratethatthe modelachievesameanabsoluteeror（MAE）ofO.OO3，meanbias eror（MBE）ofO.Ol，androt meansquare error（RMSE）of O.O159onthetraining set.Forthetesting set，performance metrics indicate MAE=0.0132 ， MBE=0.0018 ，and RMSE=0.015 .These findings confirm the model’s high predictive accuracyfortimatinglosscollapsecoefcientsinCentralGansu，providingareliableovelmethodologyforpracticalegining applications.

Key words：loess colapsibility；physical property index；correlation；support vector machine；prediction model

0 引言

濕陷性土壤在受到地下水或降雨作用后，會(huì)出現(xiàn)顯著的變形，這對(duì)基礎(chǔ)設(shè)施、房屋及其他工程建設(shè)構(gòu)成潛在威脅[1]。（剩余4952字）