Application of ANN in Predicting the Cantilever Wall Deflection in Undrained Clay

Zhongkai Huang,Dongmei Zhang,Dongming Zhang

doi:10.3390/app11209760

Abstract

The main objective of this study is to propose an artificial neural network (ANN)-based tool for predicting the cantilever wall deflection in undrained clay. The excavation width, the excavation depth, the wall thickness, the at-rest lateral earth pressure coefficient, the soil shear strength ratio at mid-depth of the wall, and the soil stiffness ratio at mid-depth of the wall were selected as the input parameters, whereas the cantilever wall deflection was selected as an output parameter. A set of verified numerical data were utilized to train, test, and validate the ANN models. Two commonly used performance indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), were selected to evaluate the performance of the proposed model. The results indicated that the proposed model can reliably predict the cantilever wall deflection in undrained clay. Moreover, the sensitivity analysis showed that the excavation depth is the most important parameter. Finally, a graphical user interface (GUI) tool was developed based on the proposed ANN model, which is much easier and less expensive to be used in practice. The results of this study can help engineers to better understand and predict the cantilever wall deflection in undrained clay.

Highlights

In recent decades, embedded retaining structures have been increasingly used for excavations in urban areas around the world [1,2,3,4]
As the soil small strain behavior is well recognized from the case histories, the Hardening Soil model with small strain stiffness (HSsmall) available in Plaxis is used for the simulation of clays in this study
An artificial neural network (ANN) model consists of an input layer, one or more hidden layers, and an output layer

Summary

Introduction

In recent decades, embedded retaining structures have been increasingly used for excavations in urban areas around the world [1,2,3,4]. The cantilever wall is a conventional earth-retaining structure with a relatively simple construction process and is commonly used for excavations no more than 6 m deep. They provide open excavations and do not require bracing or anchoring. The control of deformation is as important as the safety requirements against collapse in the design of such retaining walls [5,6] In this regard, an accurate and practical tool for predicting the cantilever wall deflection should be of great interest to engineers and stakeholders. The hidden layer and the output layer perform the complex computations of the ANN model. The final ANN model consisted of a single input, hidden, and output layer

Objectives

Methods

Conclusion