Performance comparison of machine learning algorithms for maximum displacement prediction in soldier pile wall excavation

作     者：Danial Sheini Dashtgoli Mohammad Hossein Dehnad Seyed Ahmad Mobinipour Michela Giustiniani 

作者机构：Department of MathematicsInformaticsand GeosciencesUniversity of TriesteTrieste 34127Italy National Institute of Oceanography and Applied Geophysics-OGSBorgo Grotta Gigante 42/CTriesteSgonico 34010Italy Department of Civil EngineeringUniversity of QomQom ***Iran 

出 版 物：《Underground Space》 (地下空间（英文）)

年 卷 期：2024年第16卷第3期

页      面：301-313页

核心收录：

学科分类：081406[工学-桥梁与隧道工程] 08[工学] 0814[工学-土木工程] 082301[工学-道路与铁道工程] 0823[工学-交通运输工程] 

基　　金：supported by any funding source 

主　　题：Soldier pile wall Lateral displacements XGBoost Machine learning Artificial intelligence 

摘      要：One of the common excavation methods in the construction of urban infrastructures as well as water and wastewater facilities is the excavation through soldier pile *** maximum lateral displacement of pile wall is one of the important variables in controlling the stability of the excavation and its adjacent ***,the application of machine learning methods is widely used in engineering sciences due to its low cost and high speed of *** paper utilized three intelligent machine learning algorithms based on the excavation method through soldier pile walls,namely eXtreme gradient boosting(XGBoost),least square support vector regressor(LS-SVR),and random forest(RF),to predict maximum lateral displacement of pile *** results showed that the implemented XGBoost model performed excellently and could make predictions for maximum lateral displacement of pile walls with the mean absolute error of 0.1669,the highest coefficient of determination 0.9991,and the lowest root mean square error *** the LS-SVR,and RF models were less accurate than the XGBoost model,they provided good prediction results of maximum lateral displacement of pile walls for numerical ***,a sensitivity analysis was performed to determine the most effective parameters in the XGBoost *** analysis showed that soil elastic modulus and excavation height had a strong influence on of maximum lateral displacement of pile wall prediction.