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

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

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 pa...

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Bibliographic Details
Main Authors: Zhang, Runhong, Wu, Chongzhi, Goh, Anthony Teck Chee, Böhlke, Thomas, Zhang, Wengang
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2021
Subjects:
Engineering::Civil engineering
Anisotropic Clay
NGI-ADP
Online Access:https://hdl.handle.net/10356/146967
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Institution: Nanyang Technological University
Language: English
Description
Summary: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.

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