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

Geoscience Frontiers

Thomas Böhlke¹  Runhong Zhang²  Chongzhi Wu²  Wengang Zhang²  Anthony T.C. Goh³ 

[1] Institute of Engineering Mechanics, Karlsruhe Institute of Technology (KIT), Kaiserstraße 10, 76131, Karlsruhe, Germany;School of Civil Engineering, Chongqing University, Chongqing, 400045, China;School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore;

关键词: Anisotropic clay;    NGI-ADP;    Wall deflection;    Ensemble learning;    eXtreme gradient boosting;    Random forest regression;   

DOI  :  

来源: DOAJ

【 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 eXtreme 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.

【 授权许可】

Unknown