Soft computing approach for prediction of surface settlement induced by earth pressure balance shield tunneling

作     者：W.G.Zhang H.R.Li C.Z.Wu Y.Q.Li Z.Q.Liu H.L.Liu 

作者机构：Key Laboratory of New Technology for Construction of Cities in Mountain AreaChongqing UniversityChongqing 400045China National Joint Engineering Research Center of Geohazards Prevention in the Reservoir AreasChongqing UniversityChongqing 400045China School of Civil EngineeringChongqing UniversityChongqing 400045China Norwegian Geotechnical Institute(NGI)Sognsveien 720855 OsloNorway 

出 版 物：《Underground Space》 (地下空间（英文）)

年 卷 期：2021年第6卷第4期

页      面：353-363页

核心收录：

学科分类：081406[工学-桥梁与隧道工程] 08[工学] 0814[工学-土木工程] 082301[工学-道路与铁道工程] 0823[工学-交通运输工程] 

基　　金：supported by the National Natural Science Foundation of China(No.51608071) Technology Plan Project(2019-0045) 

主　　题：EPB Surface settlement Soft computing XGBoost Multivariate adaptive regression spline 

摘      要：Estimating surface settlement induced by excavation construction is an indispensable task in tunneling,particularly for earth pressure balance(EPB)shield *** this study,predictive models for assessing surface settlement caused by EPB tunneling were established based on extreme gradient boosting(XGBoost),artificial neural network,support vector machine,and multivariate adaptive regression *** from three tunnel construction projects in Singapore were used,with main input parameters of cover depth,advance rate,earth pressure,mean standard penetration test(SPT)value above crown level,mean tunnel SPT value,mean moisture content,mean soil elastic modulus,and grout *** performances of these soft computing models were evaluated by comparing predicted deformation with measured *** demonstrate the acceptable accuracy of the model in predicting ground settlement,while XGBoost demonstrates a slightly higher *** addition,the ensemble method of XGBoost is more computationally efficient and can be used as a reliable alternative in solving multivariate nonlinear geo-engineering problems.