路基壓實度預(yù)測模型的建立及評價

The Establishment and Evaluation of the Prediction Model of Subgrade Compaction Degree

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中圖分類號：U447 文獻標(biāo)志碼：A 文章編號：1005-8249（2025）01-0124-06

DOI：10.19860/j.cnki.issn1005-8249.2025.01.023

LONG Kai1，DENG Jingxiang2，LI Hongzhao3，HUI Bing3，SHI Yaqiang2， ZHANG Wenjun3 (1. Ji'ning Highway Devlopment Center， Ji'ning 272OO8，China； 2.Gezhouba Group Transportation Investment Co.，Ltd.，Yichang 4430O5，China； 3.Shandong Transportation Institute， Ji’nan 25OO31，China）

Abstract：Subradecompactioniscloselyrelatedtothequalityoftheroadanddirectlyafectstestabilityanddurabilityof the project.Inorderto establishthe prediction model of subgradecompaction，carryoutthe field testof subgradecompaction， throughthecontrolofthenumberofrollng，rollngspeedandwatercontentofthetestmethodonthecompactionofthelaw，the useof nonlinearregression，decision tree，supportvector machine，neural laticeand XGBoostalgorithms toestablishasix compactionprediction model，and itsprediction performanceevaluation.Theconclusion shows：Thesubgrade compaction degreeis positivelyproportional tothenumberofolingtimesand inverselyproportionaltotherollngspeed.Whenthewater content isattheoptimal watercontent，the subgradecompactionismaximum.Theefectsof watercontent，numberofroling timesandrollngspeedoncompactionarereduced inoder；thesupportvectormachine modelhasapoorpredictionefectonthe training set，andthe decision tree modelhasa por predictionefecton the predictionset，which isnotapplicable to the predictioofsubgradecompaction；thetwononlinearregressionmodelsofthepowerfunctionandthelogarithmicfunction，and thetwo machinelearning modelsof theneuralnetworkandthe XGBoostareapplicabletothepredictionof thecompactiondegree of the subgrade.The prediction performance of machine learning is higher than the nonlinear regression model.

Keywords：subgrade compaction degree；field test；predictive modeling；performance evaluation；machine learning

0 引言

基礎(chǔ)是建筑物最下部的承重構(gòu)件，承受建筑物的全部荷載，其施工質(zhì)量對于上覆結(jié)構(gòu)的耐久性和長期功能有著重要的影響。（剩余8618字）