[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124278-en":3,"doc-seo-124278-105":30,"detail-sidebar-cat-0-en-105":91},{"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},124278,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Optimisation of Tunnel Boring Machine Performance by Machine Learning","A doctoral thesis focused on improving tunnel boring machine (TBM) performance through machine learning. The research develops and evaluates data-driven models for predicting key TBM behaviour, including penetration rate and real-time forecasting of cutterhead torque and thrust force. Attention is given to how preprocessing choices such as data smoothing and the selection of recurrent neural network (RNN) algorithms influence predictive accuracy under operational constraints. The work also addresses machine learning applications to mechanised tunnel construction, supported by publications, numerical simulation, and broader reviews of relevant methods for geotechnical and stochastic analyses.","Optimisation of Tunnel Boring Machine Performance by Machine Learning  \nby Feng Shan  \nThesis submitted in fulfilment of the requirements for the degree of  \nDoctor of Philosophy  \nunder the supervision of Professor Daichao Sheng and Dr Xuzhen He  \nUniversity of Technology Sydney  \nFaculty of Engineering and Information Technology  \nCertificate of Original Authorship  \nI, Feng Shan, declare that this thesis is submitted in fulfilment of the requirements for the  \naward of Doctor of Philosophy, in the Faculty of Engineering and Information Technology at the University of Technology Sydney.  \nThis thesis is wholly my own work unless otherwise referenced or acknowledged. In addition, I certify that all information sources and literature used are indicated in the thesis.  \nThis document has not been submitted for qualifications at any other academic institution.  \nThis research is supported by the Australian Government Research Training Program.  \nSignature: Production Note:  \nSignature removed prior to publication.  \nDate: 30/05/2024  \nAcknowledgements  \nFirstly, I would like to express my gratitude to my supervisors Distinguished Professor Daichao Sheng and Dr Xuzhen He. I am specifically grateful to my supervisors for  \nproviding me with the opportunity to do research at the University of Technology Sydney and guiding me with great patience, encouragement and a fund of knowledge and  \nenthusiasm. Without their consistent support throughout my period of candidature it would not have been possible for me to conduct and finish this research.  \nI would like to acknowledge the University of Technology Sydney International Research Scholarship for tuition fees and the China Scholarship Council Scholarship for living  \nexpenses.  \nI would like to thank Dr Danial Jahed Armaghani, Professor Jidong Teng, and Professor Sheng Zhang for their invaluable support throughout machine learning modelling and numerical simulation efforts. I could not have completed this research successfully without their generous and unselfish assistance.  \nLiving and studying in Australia is a wonderful experience. I would also like to thank the friends along the way of my studies: Jinwen Bai, Miao Ge, Chenxi Tong, Jinbiao Wu,  \nXinyu Ye, Yuting Zhang, and other friends from the University of Newcastle; Dongle Cheng, Shaoheng Dai, Zhizhong Deng, Chaoqun Feng, Feng Gao, Qiang Hao, Wei Huang,  \nYang Jiang, Ruxia Liang, Jibin Li, Huan Liu, Hang Luong, Jie Ma, Shudi Mao, Haimin Qian, Fulin Qu, Xingdong Shi, Yihan Shi, Ye Su, Chen Wang, Li Wang, Lijuan Wang,  \nHaoding Xu, Zhengheng Xu, Bing Zhang, Jiaqi Zhang, Leijie Zhang, Zehao Zhang, Dong Zhao, Ting Zhou, and other friends from the University of Technology Sydney.  \nLast but not least, a particular thank you to my parents and sibling for their support  \nthroughout my life and studies. I am grateful to my parents for their love, support, and motivation in all of the things I do.  \nPublications  \nJournal papers related to the theme of this thesis:  \n• Shan, F., He, X., Armaghani, D. J., Zhang, P. & Sheng, D. (2022) Success and Challenges in Predicting TBM Penetration Rate using Recurrent Neural Networks, Tunnelling and Underground Space Technology 130:104728 .  \n• Shan, F., He, X., Armaghani, D. J. & Sheng, D. (2024) Effects of data smoothing and recurrent neural network (RNN) algorithms for real-time forecasting of tunnel boring machine (TBM) performance, Journal of Rock Mechanics and Geotechnical Engineering 16(5):1538-1551.  \n• Shan, F., He, X., Armaghani, D. J., Zhang, P. & Sheng, D. (2023) Response to Discussion on “Success and Challenges in Predicting TBM Penetration Rate using Recurrent Neural Networks” by Georg H. Erharter, Thomas Marcher, Tunnelling and Underground Space Technology 139:105064 .  \n• Shan, F., He, X., Xu, H., Armaghani, D. J. & Sheng, D. (2023) Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review, Eng—Advances in Engineering 4(2):1516-1535.  \n• Xu, H., He, X","cbCaifHgX0qNupdB","https://ap.wps.com/l/cbCaifHgX0qNupdB","pdf",6601419,1,199,"English","en",105,"# Certificate of Original Authorship\n# Acknowledgements\n## Supervisors and research support\n## Publication list\n## Related conference work","[{\"question\":\"What is the main research focus of the thesis?\",\"answer\":\"The thesis focuses on optimising tunnel boring machine (TBM) performance using machine learning models for prediction and real-time forecasting.\"},{\"question\":\"Which machine learning methods are emphasized for TBM prediction?\",\"answer\":\"Recurrent neural network (RNN) approaches are emphasized, including studies on how algorithm choice affects real-time forecasting performance.\"},{\"question\":\"How does data processing influence the thesis outcomes?\",\"answer\":\"The research examines the impact of data smoothing and related preprocessing steps on the accuracy and effectiveness of real-time TBM performance prediction.\"}]","Optimisation of Tunnel Boring Machine Performance by Machine Learning | PDF",1785821342,501,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"optimisation-of-tunnel-boring-machine-performance-by-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@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/optimisation-of-tunnel-boring-machine-performance-by-machine-learning/124278/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main research focus of the thesis?","Question",{"text":75,"@type":76},"The thesis focuses on optimising tunnel boring machine (TBM) performance using machine learning models for prediction and real-time forecasting.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods are emphasized for TBM prediction?",{"text":80,"@type":76},"Recurrent neural network (RNN) approaches are emphasized, including studies on how algorithm choice affects real-time forecasting performance.",{"name":82,"@type":73,"acceptedAnswer":83},"How does data processing influence the thesis outcomes?",{"text":84,"@type":76},"The research examines the impact of data smoothing and related preprocessing steps on the accuracy and effectiveness of real-time TBM performance prediction.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"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":106,"slug":138},19,"General","general"]