[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126590-en":3,"doc-seo-126590-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126590,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Applications of Machine Learning in Mechanised Tunnel Construction - A Systematic Review","Tunnel Boring Machines (TBMs) have become common in underground excavation because of their efficiency and reliability. Extensive datasets from site investigations and onboard acquisition systems enable machine learning (ML) to model TBM behaviour and learn complex, non-linear relationships. This systematic review summarizes widely used ML approaches for TBM tunnelling, emphasizing data processing, algorithms, optimisation methods, and evaluation metrics. It addresses key objectives including predicting TBM performance, forecasting surface settlement, and time-series prediction, while reviewing progress, challenges, and future research directions for safer, more sustainable, and cost-effective tunnelling.","Review  \nApplications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review  \nFeng Shan , Xuzhen He *, Haoding Xu , Danial Jahed Armaghani  and Daichao Sheng  \nCitation: Shan, F.; He, X.; Xu, H.; Armaghani, D.J.; Sheng, D. Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review. Eng 2023, 4, 1516–1535. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)eng4020087  \nAcademic Editor: Antonio Gil Bravo  \nReceived: 24 April 2023  \nRevised: 15 May 2023  \nAccepted: 29 May 2023  \nPublished: 30 May 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nSchool of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia; [feng.shan@student.uts.edu.au](feng.shan@student.uts.edu.au) (F.S.); [haoding.xu@student.uts.edu.au](haoding.xu@student.uts.edu.au) (H.X.);  \n[danial.jahedarmaghani@uts.edu.au](danial.jahedarmaghani@uts.edu.au) (D.J.A.); [daichao.sheng@uts.edu.au](daichao.sheng@uts.edu.au) (D.S.)  \n* Correspondence: [xuzhen.he@uts.edu.au](xuzhen.he@uts.edu.au)  \nAbstract: Tunnel Boring Machines (TBMs) have become prevalent in tunnel construction due to their high efficiency and reliability. The proliferation of data obtained from site investigations and data acquisition systems provides an opportunity for the application of machine learning (ML) techniques. ML algorithms have been successfully applied in TBM tunnelling because they are particularly effective in capturing complex, non-linear relationships. This study focuses on commonly used ML techniques for TBM tunnelling, with a particular emphasis on data processing, algorithms, optimisation techniques, and evaluation metrics. The primary concerns in TBM applications are discussed, including predicting TBM performance, predicting surface settlement, and time series forecasting. This study reviews the current progress, identifies the challenges, and suggests future developments in the field of intelligent TBM tunnelling construction. This aims to contribute to the ongoing efforts in research and industry toward improving the safety, sustainability, and costeffectiveness of underground excavation projects.  \nKeywords: tunnel boring machine; machine learning; TBM performance; surface settlement; time series forecasting  \n1. Introduction  \nThe Tunnel of Eupalinos, the oldest known tunnel, was constructed in the 6th century BC in Greece for transporting water. The Industrial Revolution brought about a significant increase in tunnel construction used for various purposes including mining, defensive fortification, and transportation. The technology continued to evolve in modern times, and tunnel boring machines (TBMs) became widespread for tunnel excavation projects, including transportation tunnels, water and sewage tunnels, and mining operations. TBMs typically consist of a rotating cutterhead that breaks up the rock or soil and a conveyor system that removes the excavated material. TBMs are preferred over traditional drill and blast techniques due to their higher efficiency, safer working conditions, minimal environmental disturbance, and reduced project costs [1–3] . The continuous cutting, mucking, and lining installation process enables TBMs to excavate tunnels efficiently. However, the high cost of building and operating TBMs, as well as the need for regular maintenance, remains asignificant concern. Most importantly, tunnel collapse, rock bursting, water inrush, squeezing, or machine jamming can pose major challenges in complex geotechnical conditions. Therefore, optimising tunnelling operations is critical for project time management, cost control, and risk miti","cbCaikDkjOOeIjhf","https://ap.wps.com/l/cbCaikDkjOOeIjhf","pdf",1092640,2,1,20,"English","en",105,"# Introduction\n## Traditional TBM modelling approaches\n## Role of machine learning in TBM tunnelling\n# Systematic review scope\n## ML techniques for TBM tunnelling\n## Data processing and optimisation\n## Evaluation metrics and predictive tasks\n# Current progress and challenges\n## TBM performance prediction\n## Surface settlement prediction\n## Time-series forecasting\n# Future developments","[{\"question\":\"Why is machine learning suitable for mechanised tunnel construction with TBMs?\",\"answer\":\"ML can learn complex, non-linear relationships using the large volume of data collected from site investigations and data acquisition systems, making it effective for modelling TBM tunnelling behaviour.\"},{\"question\":\"Which predictive tasks does the review focus on for TBM applications?\",\"answer\":\"The review discusses predicting TBM performance, predicting surface settlement, and time series forecasting for TBM-related operations.\"},{\"question\":\"What aspects are emphasised when reviewing ML techniques for TBM tunnelling?\",\"answer\":\"The review emphasizes data processing, algorithm selection, optimisation techniques, and evaluation metrics, alongside a summary of current progress and remaining challenges.\"}]","Applications of Machine Learning in Mechanised Tunnel Construction - A Systematic Review | PDF",1785933527,50,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"applications-of-machine-learning-in-mechanised-tunnel-construction-a-systematic-review","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/applications-of-machine-learning-in-mechanised-tunnel-construction-a-systematic-review/126590/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-26","2026-08-05",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},"Why is machine learning suitable for mechanised tunnel construction with TBMs?","Question",{"text":76,"@type":77},"ML can learn complex, non-linear relationships using the large volume of data collected from site investigations and data acquisition systems, making it effective for modelling TBM tunnelling behaviour.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which predictive tasks does the review focus on for TBM applications?",{"text":81,"@type":77},"The review discusses predicting TBM performance, predicting surface settlement, and time series forecasting for TBM-related operations.",{"name":83,"@type":74,"acceptedAnswer":84},"What aspects are emphasised when reviewing ML techniques for TBM tunnelling?",{"text":85,"@type":77},"The review emphasizes data processing, algorithm selection, optimisation techniques, and evaluation metrics, alongside a summary of current progress and remaining challenges.","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":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,120,123,127,130,134],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":30,"slug":114},6,"Technology","technology",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":22,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":22,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":107,"slug":137},19,"General","general"]