[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127711-en":3,"doc-seo-127711-105":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},127711,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Developing AI Systems for EPB TBM Utilizing Sensing Data and Machine Learning","This dissertation develops an integrated framework of artificial intelligence systems for earth pressure balance (EPB) tunnel boring machine (TBM) tunneling, designed around a feedback loop and a human cognitive model of sensing, perceiving, and decision-making. It studies TBM data properties and how aggregation and feature selection affect prediction, including knowledge-based feature taxonomy and selection benefits. It then proposes supervised and unsupervised real-time perception from sensor streams for geologic interpretation and anomaly detection, plus RF-based ground movement estimation using TBM data and tunnel geometry. Finally, it models tunneling decision-making using probabilistic graph structure learning and multi-output supervised control of steering parameters.","UC Berkeley  \nUC Berkeley Electronic Theses and Dissertations  \nTitle  \nDeveloping AI Systems for EPB TBM Utilizing Sensing Data and Machine Learning  \nPermalink  \n[https://escholarship.org/uc/item/6sj1m77w](https://escholarship.org/uc/item/6sj1m77w)  \nAuthor  \nApoji, Dayu  \nPublication Date  \n2023  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nDeveloping AI Systems for EPB TBM Utilizing Sensing Data and Machine Learning  \nby  \nDayu Apoji  \nA dissertation submitted in partial satisfaction of the requirements for the degree of  \nDoctor of Philosophy  \nin  \nCivil and Environmental Engineering  \nand the Designated Emphasis  \nin  \nComputational and Data Science and Engineering  \nin the  \nGraduate Division  \nof the  \nUniversity of California, Berkeley  \nCommittee in charge:  \nProfessor Kenichi Soga, Chair  \nProfessor Dimitrios Zekkos  \nProfessor Giles Hooker  \nSpring 2023  \nDeveloping AI Systems for EPB TBM Utilizing Sensing Data and Machine Learning  \nCopyright 2023  \nby  \nDayu Apoji  \n1  \nAbstract  \nDeveloping AI Systems for EPB TBM Utilizing Sensing Data and Machine Learning  \nby  \nDayu Apoji  \nDoctor of Philosophy in Civil and Environmental Engineering and the Designated Emphasis in  \nComputational and Data Science and Engineering  \nUniversity of California, Berkeley  \nProfessor Kenichi Soga, Chair  \nThis dissertation presents a development of an integrated framework of artificial intelligence (AI) systems for earth pressure balance (EPB) tunnel boring machine (TBM) tunneling. The framework is constructed based on the feedback loop control system. The AI systems are developed using machine learning algorithms and structured to follow the human cognitive model, i.e. , sensing, perceiving, and decision-making. The development of the systems is conducted in three parts.  \nThe first part discusses the characteristics of TBM data and the effects of data preparation on data-driven models. This is achieved by (i) proposing a knowledge-based EPB TBM feature taxonomy and (ii) investigating the effects of data aggregation and feature selection on prediction models. The investigation shows that models developed using different data aggregation levels produce comparable prediction trends and similar feature importance rank in the conditions of sufficient observations and predictors. However, models with a coarser aggregation level may appreciate higher prediction performance due to the lower variance. The investigation also shows that the knowledge-guided TBM feature selection offers benefits over embedded machine learning-based feature selections. The developed model can produce relatively consistent feature importance in different tunneling cases, indicating better generalizability of the model.  \nThe second part proposes AI systems that perceive tunneling environments in real-time based on the streams of sensor data during tunneling operation. This is achieved by developing (i) a supervised AI system to interpret the encountered geologic conditions,(ii) an unsupervised AI system to detect the encountered geologic anomalies, and (iii) a supervised AI system that connects TBM data to the ground monitoring data and estimates tunneling-induced  \n2  \nground movements. The proposed geologic interpretation system uses either Random Forests (RF) classification or regression algorithms to infer the geologic transitions along the tunnel alignment. The proposed geologic anomaly detection system combines Principal Component Analysis (PCA) to project the data into a lower dimension space and Local Outlier Factor (LOF) to measure the degree of the anomaly of the projected data points. The proposed tunneling-induced ground movement estimation system uses RF regression to approximate any shape of ground movements solely based on TBM operation data and tunnel spatial geometries without prior assumptions on the ground movement shape, geologic material param","cbCaim87nT6newSw","https://ap.wps.com/l/cbCaim87nT6newSw","pdf",66391116,1,246,"English","en",105,"# Introduction\n## Background\n## Problem Statements\n## Objectives\n## Dissertation Outline\n# EPB TBM Tunneling\n## Introduction\n## Historical Development\n## TBM Overview\n## EPB TBM Components","[{\"question\":\"What is the main contribution of this dissertation?\",\"answer\":\"It presents an integrated AI framework for EPB TBM tunneling that follows a feedback loop and a sensing–perceiving–decision-making cognitive structure.\"},{\"question\":\"How does the work handle geologic interpretation and anomaly detection in real time?\",\"answer\":\"It develops supervised learning for interpreting encountered geologic conditions and unsupervised learning to detect anomalies using PCA and Local Outlier Factor on sensor data streams.\"},{\"question\":\"How are tunneling-induced ground movements estimated?\",\"answer\":\"A Random Forest regression model estimates ground movements based solely on TBM operation data and tunnel spatial geometries, without assuming a specific movement shape or prior material parameters.\"}]","Developing AI Systems for EPB TBM Utilizing Sensing Data and Machine Learning | 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is the main contribution of this dissertation?","Question",{"text":76,"@type":77},"It presents an integrated AI framework for EPB TBM tunneling that follows a feedback loop and a sensing–perceiving–decision-making cognitive structure.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the work handle geologic interpretation and anomaly detection in real time?",{"text":81,"@type":77},"It develops supervised learning for interpreting encountered geologic conditions and unsupervised learning to detect anomalies using PCA and Local Outlier Factor on sensor data streams.",{"name":83,"@type":74,"acceptedAnswer":84},"How are tunneling-induced ground movements estimated?",{"text":85,"@type":77},"A Random Forest regression model estimates ground movements based solely on TBM operation data and tunnel spatial geometries, without assuming a specific movement shape or prior material 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