Estimation of diaphragm wall deflections for deep braced excavation in anisotropic clays using ensemble learning  

作　　者：Runhong Zhang Chongzhi Wu Anthony T.C.Goh Thomas Bohlke Wengang Zhang 

机构地区：[1]School of Civil Engineering,Chongqing University,Chongqing,400045,China [2]School of Civil and Environmental Engineering,Nanyang Technological University,639798,Singapore [3]Institute of Engineering Mechanics,Karlsruhe Institute of Technology(KIT),Kaiserstraße 10,76131,Karlsruhe,Germany [4]Key Laboratory of New Technology for Construction of Cities in Mountain Area,Chongqing University,Ministry of Education,Chongqing,400045,China

出　　处：《Geoscience Frontiers》2021年第1期365-373,共9页地学前缘（英文版）

基　　金：supported by the High-end Foreign Expert Introduction program(No.G20190022002);Chongqing Construction Science and Technology Plan Project(2019-0045);the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJZD-K201900102);The financial support is gratefully acknowledged。

摘　　要：This paper adopts the NGI-ADP soil model to carry out finite element analysis,based on which the effects of soft clay anisotropy on the diaphragm wall deflections in the braced excavation were evaluated.More than one thousand finite element cases were numerically analyzed,followed by extensive parametric studies.Surrogate models were developed via ensemble learning methods(ELMs),including the e Xtreme Gradient Boosting(XGBoost),and Random Forest Regression(RFR)to predict the maximum lateral wall deformation(δhmax).Then the results of ELMs were compared with conventional soft computing methods such as Decision Tree Regression(DTR),Multilayer Perceptron Regression(MLPR),and Multivariate Adaptive Regression Splines(MARS).This study presents a cutting-edge application of ensemble learning in geotechnical engineering and a reasonable methodology that allows engineers to determine the wall deflection in a fast,alternative way.

关 键 词：Anisotropic clay NGI-ADP Wall deflection Ensemble learning eXtreme gradient boosting Random forest regression 

分 类 号：TU470[建筑科学—结构工程]