[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120317-en":3,"doc-seo-120317-105":30,"detail-sidebar-cat-0-en-105":90},{"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":4,"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},120317,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine learning and Big Data in deep underground engineering - Special issue editorial focus","This special issue of Deep Underground Science and Engineering highlights how machine learning and Big Data transform deep underground engineering. It emphasizes applications that improve structural safety, optimize tunneling operations, and enhance geotechnical investigations through prediction, automation, and analytics on large datasets. Featured studies cover time-series tunnel behavior modeling, pillar stability assessment with hybrid ML methods, TBM operational clustering, low-cost real-time monitoring sensors, optimization for underwater shield tunnels, and Bayesian-optimized hybrid models for post-blasting rock fragmentation.","DOI: 10.1002/dug2.70004  \nEDIT ORIAL  \nMachine learning and Big Data in deep underground engineering  \nThis special issue of Deep Underground Science and Engineering (DUSE) showcases pioneering research on the transformative role of machine learning (ML) and Big Data in deep underground engineering. Edited by guest editors Prof. Asoke Nandi (Brunel University of London, UK), Prof. Ru Zhang (Sichuan University, China), Prof. Tao Zhao (Chinese Academy of Sciences, China), and Prof. Tao Lei (Shaanxi University of Science and Technology, China), this issue highlights the innovative applications of ML technique in reshaping structural safety, tunneling operations, and geotechnical investigations.  \nAs underground engineering challenges grow in complexity, ML and Big Data have become indispensable tools for improving prediction accuracy, optimizing operational efficiency, and ensuring the long‐term safety and sustainability of infrastructure. By leveraging vast datasets, automating critical processes, and predicting complex engineering outcomes, these technologies are enabling smarter, more reliable engineering practices that drive both performance and resilience.  \nThe contributions to this special issue illustrate the diverse and impactful applications of ML and Big Data in deep underground engineering. One article introduces ALSTNet, an advanced data‐driven model that integrates long‐ and short‐term time‐series data using autoencoders to predict tunnel structural behaviors. When applied to strain monitoring data from the Nanjing Dinghuaimen tunnel, ALSTNet outperforms traditional models, offering promising potential for early disaster prevention in real‐world engineering scenarios. Another study presents two robust ML models—Gene Expression Programming (GEP) and a Decision Tree‐Support Vector Machine (DT‐SVM) hybrid algorithm—to assess pillar stability in deep underground mines. Validated with 236 case histories, these models demonstrate exceptional accuracy and provide valuable tools for project managers to evaluate pillar stability during both the design and operational phases of mining projects. Yet another study demonstrates the use of fuzzy C‐means clustering combined with ML models in Tunnel Boring Machine (TBM) operations. This innovative approach enhances prediction accuracy, providing more reliable insights for TBM tunneling processes and boosting efficiency in underground excavation projects.  \nSeveral other papers focus on optimizing monitoring systems for underground structures. One contribution presents a low‐cost micro‐electromechanical systems (MEMS) sensor designed to monitor tilt and acceleration in underground structures. Aided by ML algorithms, this sensor facilitates real‐time monitoring and early warning capabilities, thereby significantly improving safety during underground construction. Another paper introduces a ML‐based optimization model for underwater shield tunnels, showing how strategically placed monitoring points—such as at the spandrel and arch crown—can improve the accuracy of stress distribution predictions and enhance structural health monitoring.  \nAdditionally, this special issue addresses the challenge of predicting rock fragmentation post‐blasting. A suite of hybrid ML models—Random Forest, AdaBoost, and Gradient Boosting—optimized with the Bayesian Optimization Algorithm (BOA), showcases superior prediction accuracy. These models offer an advanced and highly reliable method for predicting rock fragmentation in mining engineering applications. The integration of ML with sensor technologies, optimization algorithms, and predictive models in these papers highlights the tremendous potential of AI to revolutionize deep underground engineering. As these technologies continue to evolve, they promise to drive substantial improvements in safety, efficiency, and environmental sustainability within the sector.  \nThrough this special issue, DUSE reaffirms its commitment to promote the application of ML tech","cbCaidyslRjzCmY7","https://ap.wps.com/l/cbCaidyslRjzCmY7","pdf",239597,1,2,"English","en",105,"# Editorial Overview\n## ML and Big Data for structural safety and operations\n## Featured application studies\n## Monitoring systems and sensor-driven early warning\n## Rock fragmentation prediction after blasting\n## Future directions","[{\"question\":\"What topics does this special issue cover about machine learning and Big Data in deep underground engineering?\",\"answer\":\"It focuses on improving structural safety, optimizing tunneling operations, and strengthening geotechnical investigations using ML and Big Data.\"},{\"question\":\"How is machine learning applied to tunnel monitoring in the featured studies?\",\"answer\":\"One study uses an advanced data-driven time-series model (ALSTNet) for predicting tunnel structural behavior from strain monitoring data.\"},{\"question\":\"What methods are used to predict rock fragmentation after blasting?\",\"answer\":\"Hybrid machine learning models such as Random Forest, AdaBoost, and Gradient Boosting are optimized using Bayesian Optimization Algorithm (BOA) to improve prediction accuracy.\"}]","Machine learning and Big Data in deep underground engineering - 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