[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120306-en":3,"doc-seo-120306-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120306,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Enhancing understanding of 3D rectangular tunnel heading stability in c-φ soils with surcharge loading - A comprehensive FELA analysis using three stability factors and machine learning","This study examines the stability of three-dimensional rectangular tunnel headings in drained c-ϕ soils, incorporating surcharge effects using 3D Finite Element Limit Analysis (FELA). It focuses on the upper and lower bound solutions for three stability factors: cohesion, surcharge, and soil unit weight (Nc, Ns, and Nγ). Based on Terzaghi’s principle of superposition, the analysis evaluates tunnel stability under varying cover-depth ratio (H/D), width-depth ratio (B/D), and friction angle (ϕ). Machine learning models (ANN, XGBoost) develop correlations, while a relative importance index quantifies parameter influence, supporting practical design charts.","Artificial Intelligence in Geosciences 6 (2025) 100111  \nContents lists available at ScienceDirect  \nArtificial Intelligence in Geosciences  \n[journal homepage: www.keaipublishing.com/en/journals/artificial-intelligence-in-geosciences](journal homepage: www.keaipublishing.com/en/journals/artificial-intelligence-in-geosciences)  \n| Enhancing understanding of 3D rectangular tunnel heading stability in c-φ soils with surcharge loading: A comprehensive FELA analysis using three stability factors and machine learning\u003Cbr>Suraparb Keawsawasvonga , Jim Shiaub , Nhat Tan Duongc,d , Thanachon Promwichaia, Rungkhun Banyonga, Van Qui Laic,d,* \u003Cbr>a Research Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures, Department of Civil Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, 12120, Thailand\u003Cbr>b School of Engineering, University of Southern Queensland, Toowoomba, 4350, QLD, Australia\u003Cbr>c Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Viet Nam d Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Viet Nam |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>3D tunnel Stability factors Terzaghi Superposition FELA\u003Cbr>ANN\u003Cbr>XGBoost |  | This study examines the stability of three-dimensional rectangular tunnel headings in drained c-ϕ soils, incorporating surcharge effects using 3D Finite Element Limit Analysis (FELA). It focuses on the upper and lower bound solutions for three stability factors: cohesion, surcharge, and soil unit weight (Nc, Ns, and Nγ). Based on Terzaghi’s principle of superposition, the analysis evaluates tunnel stability under varying parameters, such as cover-depth ratio (H/D), width-depth ratio (B/D), and friction angle (ϕ). The results align closely with previous studies, and practical design charts are provided for calculating minimum support pressures. Additionally, machine learning models (ANN and XGBoost) are used to develop accurate correlations between input parameters and stability results. A relative importance index analysis is conducted to assess the impact of these parameters. This research enhances understanding of tunnel stability and offers practical insights for tunnel design. |\n\n1. Introduction  \nRectangular tunnels are commonly used in urban areas for transportation, utilities, pedestrian passageways, mining, and various other purposes. They offer efficient use of space, and therefore are particularly well-suited for utility applications, such as for electrical cables, water pipes, or sewage systems. Although rectangular tunnels may not always be the first choice in terms of tunnel geometry, they have experienced increased popularity due to their distinct benefits.  \nEnsuring the stability of tunnels is one of the central challenges in the field of geotechnical engineering. For this purpose, various researchers have employed the Finite Element Limit Analysis (FELA) method to evaluate safety factors or collapse loads in diverse geotechnical problems (Drucker et al., 1952; Chen and Liu, 2012; Sloan, 2013; Sangjinda et al., 2023). Earlier research focused on tunnel stability has presented  \nupper bound (UB) and lower bound (LB) solutions for various tunnel shape and soil condition including circular tunnel in undrained soil (Wilson et al., 2011; Shiau and Keawsawasvong, 2022; Keawsawasvong and Ukritchon, 2022), drained soil (Yamamoto et al., 2011a; Xiao et al., 2019a) and rock masses (Zhang et al., 2019). Furthermore, there were several studies on square and rectangular shape tunnel stability for drained and undrained (Sloan and Assadi, 1991, Yamamoto et al., 2011b; Abbo et al., 2013; Wilson et al., 2015; Xiao et al., 2019b; Bhattacharya and Dutta, 2023). Recent studies have significantly enhanced the understanding of tunnel stability, including research on s","cbCaik3HDmvJbrkX","https://ap.wps.com/l/cbCaik3HDmvJbrkX","pdf",12555594,1,16,"English","en",105,"# Introduction\n## Background and gap in rectangular tunnel stability research\n# Methodology\n## 3D finite element limit analysis (FELA)\n## Stability factors and Terzaghi superposition\n# Parameter study\n## Geometry ratios and friction angle effects\n# Machine learning correlations\n## ANN and XGBoost modeling\n# Relative importance and design charts","[{\"question\":\"What stability factors are analyzed for the 3D rectangular tunnel heading?\",\"answer\":\"The study evaluates three factors: cohesion, surcharge, and soil unit weight, corresponding to Nc, Ns, and Nγ.\"},{\"question\":\"How does the work incorporate surcharge loading and soil strength into the stability assessment?\",\"answer\":\"Surcharge effects are included within a 3D FELA framework, and Terzaghi’s principle of superposition is used to assess stability under varying parameters such as H/D, B/D, and friction angle ϕ.\"},{\"question\":\"What role do machine learning models (ANN and XGBoost) play in the research?\",\"answer\":\"ANN and XGBoost are used to build accurate correlations between input parameters and the resulting stability measures, complementing the FELA-based analysis.\"}]","Enhancing understanding of 3D rectangular tunnel heading stability in c-φ soils with surcharge loading - A comprehensive FELA analysis using three stability factors and machine learning | PDF",1785729369,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"enhancing-understanding-of-3d-rectangular-tunnel-heading-stability-in-c-soils-with-surcharge-loading-a-comprehensive-fela-analysis-using-three-stability-factors-and-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/enhancing-understanding-of-3d-rectangular-tunnel-heading-stability-in-c-soils-with-surcharge-loading-a-comprehensive-fela-analysis-using-three-stability-factors-and-machine-learning/120306/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What stability factors are analyzed for the 3D rectangular tunnel heading?","Question",{"text":76,"@type":77},"The study evaluates three factors: cohesion, surcharge, and soil unit weight, corresponding to Nc, Ns, and Nγ.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the work incorporate surcharge loading and soil strength into the stability assessment?",{"text":81,"@type":77},"Surcharge effects are included within a 3D FELA framework, and Terzaghi’s principle of superposition is used to assess stability under varying parameters such as H/D, B/D, and friction angle ϕ.",{"name":83,"@type":74,"acceptedAnswer":84},"What role do machine learning models (ANN and XGBoost) play in the research?",{"text":85,"@type":77},"ANN and XGBoost are used to build accurate correlations between input parameters and the resulting stability measures, complementing the FELA-based analysis.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":29,"slug":119},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]