Physics

Geology

Finite element method

Regression analysis

Deflection (physics)

Mathematics

Multivariate adaptive regression splines

Structural engineering

Parametric statistics

Optics

Statistics

Machine learning

Engineering

Soft computing

Geotechnical engineering

Computer science

Ensemble learning

Boosting (machine learning)

Artificial neural network

Bayesian multivariate linear regression

Geoscience Frontiers

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.

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