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The study applies three intelligent machine learning algorithms—XGBoost, LS-SVR, and Random Forest—to predict maximum lateral displacement of pile walls. The XGBoost model achieves strong performance with low error and high determination, while LS-SVR and RF provide good predictions. A sensitivity analysis identifies soil elastic modulus and excavation height as key influential parameters.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/performance-comparison-of-machine-learning-algorithms-for-maximum-displacement-prediction-in-soldier-pile-wall-excavation-research-paper/291196/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/performance-comparison-of-machine-learning-algorithms-for-maximum-displacement-prediction-in-soldier-pile-wall-excavation-research-paper/291196.png","ImageObject",300,407,{"name":92,"@type":93},"Valentina","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-05","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the main goal of the paper?","Question",{"text":113,"@type":114},"To compare machine learning algorithms for predicting the maximum lateral displacement of soldier pile wall excavation.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which algorithm performed best in the study?",{"text":118,"@type":114},"The XGBoost model performed excellently, yielding the lowest error metrics and the highest coefficient of determination among the tested methods.",{"name":120,"@type":111,"acceptedAnswer":121},"Which parameters were found to be most influential?",{"text":122,"@type":114},"The sensitivity analysis shows that soil elastic modulus and excavation height have a strong influence on the predicted maximum lateral displacement.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291196,1790030213,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},13056703020460,"https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923","Available online [at www.sciencedirect.com](at www.sciencedirect.com)  \nScienceDirect  \nUnderground Space 16 (2024) 301–313  \n[www.keaipublishing.com/undsp](www.keaipublishing.com/undsp)  \nResearch Paper  \nPerformance comparison of machine learning algorithms for maximum displacement prediction in soldier pile wall excavation  \nDanial Sheini Dashtgolia,b,⇑ , Mohammad Hossein Dehnadc, Seyed Ahmad Mobinipour c,  \nMichela Giustiniani b  \na Department of Mathematics, Informatics, and Geosciences, University of Trieste, Trieste 34127, Italy  \nb National Institute of Oceanography and Applied Geophysics-OGS, Borgo Grotta Gigante 42/C, Trieste, Sgonico 34010, Italy  \nc Department of Civil Engineering, University of Qom, Qom 3716146611, Iran  \nReceived 28 February 2023; received in revised form 3 August 2023; accepted 9 September 2023  \nAvailable online 9 January 2024  \nAbstract  \nOne of the common excavation methods in the construction of urban infrastructures as well as water and wastewater facilities is the excavation through soldier pile walls. The maximum lateral displacement of pile wall is one of the important variables in controlling the stability of the excavation and its adjacent structures. Nowadays, the application of machine learning methods is widely used in engineering sciences due to its low cost and high speed of calculation. This 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 walls. The 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 0.3544. Although 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 outcomes. Furthermore, a sensitivity analysis was performed to determine the most effective parameters in the XGBoost model. This analysis showed that soil elastic modulus and excavation height had a strong influence on of maximum lateral displacement of pile wall prediction.  \nKeywords: Soldier pile wall; Lateral displacements; XGBoost; Machine learning; Artificial intelligence  \n1 Introduction  \nDeep excavation is now required today for a variety of projects, including the construction of underground facilities, water and sewage pumping stations, underground stations, and the high-rise building basements. Especially in urban construction, the safety and serviceability of surrounding structures are at risk if the displacement of the excavation exceeds a certain level. As a result, determining the maximum displacement of a deep excavation is a  \n⇑ Corresponding author at: Department of Mathematics, Informatics, and Geosciences, University of Trieste, Trieste 34127, Italy.  \nE-mail address: [dsheinidashtgoli@ogs.it](dsheinidashtgoli@ogs.it) (D. Sheini Dashtgoli).  \ncrucial global problem. Engineers and researchers have conducted a detailed analysis of lateral wall displacement due to excavation. To date several research papers have investigated the behaviour of different walls affected by deep excavations, including diaphragm walls (Addenbrooke et al., 2000; Bica & Clayton, 1998; Donget al., 2014; Elbaz et al., 2018; Goh et al., 2017; Hashash & Whittle, 1996; Hsiung, 2009; Hsiung et al., 2016; Kunasegaram & Takemura, 2021; Ou et al., 2020; Tan & Wei, 2012), sheet piling (Gajan, 2011; Gopal Madabhushi & Chandrasekaran, 2005; Wong & Broms, 1989), nailing (Cheuk et al., 2005; Hu et al., 2020; Liu et al., 2021; Singh & Sivakumar Babu, 2010; Yuan et al., 2019), and soldier pile wall (Vermeer et al., 2001; Athmarajah & de  \n[https://doi.org/10.1016/j.undsp.2023.09.013]","cbCaimbXVTP8BxcT","https://ap.wps.com/l/cbCaimbXVTP8BxcT","pdf",2675254,13,"English","# Introduction\n## Problem background: deep excavation and displacement control\n## Machine learning approach for prediction\n## Algorithm comparison results\n## Sensitivity analysis for key parameters","[{\"question\":\"What is the main goal of the paper?\",\"answer\":\"To compare machine learning algorithms for predicting the maximum lateral displacement of soldier pile wall excavation.\"},{\"question\":\"Which algorithm performed best in the study?\",\"answer\":\"The XGBoost model performed excellently, yielding the lowest error metrics and the highest coefficient of determination among the tested methods.\"},{\"question\":\"Which parameters were found to be most influential?\",\"answer\":\"The sensitivity analysis shows that soil elastic modulus and excavation height have a strong influence on the predicted maximum lateral displacement.\"}]","Performance comparison of machine learning algorithms for maximum displacement prediction in soldier pile wall excavation - Research paper | PDF",1789643677,33]