Applications of Artificial Intelligence in Mining and Geotechnical Engineering

Erscheinungsjahr 2023
ISBN: 978-0-443-18764-3
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 191 mm x 234 mm, Gewicht: 790 g

Applications of Artificial Intelligence in Mining, Geotechnical and Geoengineering provides recent advances in mining, geotechnical and geoengineering, as well as applications of artificial intelligence in these areas. It serves as the first book on applications of artificial intelligence in mining, geotechnical and geoengineering, providing an opportunity for researchers, scholars, engineers, practitioners and data scientists from all over the world to understand current developments and applications. Topics covered include slopes, open-pit mines, quarries, shafts, tunnels, caverns, underground mines, metro systems, dams and hydro-electric stations, geothermal energy, petroleum engineering, and radioactive waste disposal.

In the geotechnical and geoengineering aspects, topics of specific interest include, but are not limited to, foundation, dam, tunneling, geohazard, geoenvironmental and petroleum engineering, rock mechanics, geotechnical engineering, soil mechanics and foundation engineering, civil engineering, hydraulic engineering, petroleum engineering, engineering geology, etc.

Autoren/Hrsg.

Nguyen, Hoang

Bui, Xuan Nam

Topal, Erkan

Zhou, Jian

Choi, Yosoon

Zhang, Wengang

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Energietechnik | Elektrotechnik Erdölenergietechnik
• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Primärer Sektor Bergbau
• Technische Wissenschaften Umwelttechnik | Umwelttechnologie Umwelttechnik
• Technische Wissenschaften Technik Allgemein Ingenieurwissenschaftliches Knowhow

Inhaltsverzeichnis

A. Overview of AI, learning theory and data analytics techniques1. Overview of artificial intelligence techniques used in the book (US/Europe-based contributors)2. Overview of learning theories used in the book (US/Europe-based contributors)3. Overview of data analytics techniques used in the book (US/Europe-based contributors)

B. Applications of artificial intelligence in mining4. Computer vision-based approaches for feature extractions in rock engineering 5. Intelligent optimization of design system for underground space structure of metal mine 6. A comparative study of backbreak distance prediction in the open-pit mine based on support vector regression and three kinds of bio-inspired meta-heuristic algorithms 7. The novel automatic mineral recognition techniques by optical analysis and machine learning 8. Prediction of factor of safety for circular failure slope using support vector regression with two optimization algorithms 9. Application of AI in geochemical anomaly detection 10. Application of AI in mineral prospectivity modeling and mapping 11. Application of AI in estimating mining capital cost 12. Application of AI in forecasting copper prices 13. Application of AI in mine planning 14. Application of AI in reserve and grade estimation of ore 15. Application of AI in predicting blast-induced ground vibration 16. Application of AI in predicting blast-induced air over-pressure 17. Application of AI in predicting blast-induced flyrock 18. Application of AI in predicting blast-induced back-break 19. Application of AI in predicting rock fragmentation 20. Application of AI in estimating ore production of track-haulage system 21. Application of AI in the diagnosis of problems in truck ore transport operation in underground mines 22. Application of AI in predicting air quality in open pit mines 23. Application of AI in predicting rockburst hazards 24. Application of AI in predicting slope stability in open pit mines 25. Application of AI in predicting heavy metals sorption efficiency using mining materials 26. Application of AI in forecasting moment magnitude of micro-earthquakes induced by fault structure and mining activities 27. Application of AI in predicting mine water quality 28. Application of AI for coal mine gas risk assessment 29. Application of AI for predicting hangingwall stability 30. Application of AI for estimating the gross calorific value of coal 31. Application of AI for mapping ground water

C. Applications of artificial intelligence in geotechnical and geoengineering32. Hard rock pillar stability prediction using hybrid metaheuristic algorithms and support vector machine approaches based on an updated case histories 33. Application of AI in predicting rock properties during rock drilling operations 34. Application of AI in predicting rock-mechanics parameters 35. Application of AI in predicting rock uniaxial compressive strength 36. Application of AI in mapping landslides 37. Application of AI in predicting diaphragm wall deflection in braced excavation 38. Application of AI in predicting shear strength of tilted angle connectors 39. Application of AI in predicting swelling pressure of expansive soils 40. Application of AI in predicting roadway stability 41. Application of AI in forecasting TBM advance rate 42. Application of AI in estimating the friction angle of clays 43. Application of AI in predicting the compressibility of clay 44. Application of AI in estimating the performance of tunnel boring machines 45. Application of AI in stability classification of discontinuous rock slope 46. Application of AI in rock slope block-toppling modeling and assessment 47. Application of AI in predicting Young's modulus and unconfined compressive strength of rock 48. Application of AI in ground identification of working face 49. Application of AI in predicting ground settlement in tunneling 50. Application of AI in predicting rock geomechanically properties 51.Application of AI in predicting surface settlement induced by earth pressure balance shield tunneling 52. Application of AI in predicting elastic modulus of rocks 53. Application of AI in predicting cohesion of rocks 54. Application of AI in predicting spacing and block volume in discontinuous rock masses using image processing technique

Über Autor(innen)

Zhou, Jian
Assoc. Prof. Jian Zhou received the Ph.D. degree from the School of Resources and Safety Engineering, Central South University, Changsha, China, in 2015. From 2013 to 2014, he was a Visitor scholar with the McGill University, Montreal, Canada. His current research interests in prediction and control of mining and geotechnical engineering hazards using machine learning methods, including rockburst in deep hardrock mining and high-stress conditions, pillar and stope stability analysis, blast vibration, slope stability analysis. He is currently an Associate Professor with the School of Resource and Safety Engineering, Central South University. He is a member of ISRM and invited to serve as the Editorial Board Member of Scientific Reports, International Journal of Mining Science and Technology, Advances in Civil Engineering, Applied Sciences, Metaheuristic Computing and Applications. He has published more than 100 papers in well-established ISI and Scopus journals, national and international conferences. His citation and H-index are 3500 and 33, respectively.

Topal, Erkan
Prof. Erkan Topal is a mining engineer with a Master of Science Degree (MSc) in Mineral Economics, MSc and PhD. Degree in Mining Engineering from Colorado School of Mines in U.S. His main research interests are in mine planning and optimization, mineral and energy economics. He is a world leading expert in the field of mine planning and optimization. Some of his internationally recognized research includes underground mine scheduling with stope boundary optimization, mine waste dump design with pit optimization, Application of the Real Options in Engineering Design and Decision Making He is in the editorial board of five scientific journals in the area of mining including editor-in-chief role for the International Journal of Mining, Reclamation and Environment Journal and has published well over 100 papers in reputed journals and refereed conference proceedings.

Bui, Xuan Nam
Prof. Xuan-Nam Bui received the B.Eng. and M.Eng. degrees in mining engineering from Hanoi University of Mining and Geology (HUMG), Vietnam, in 1996 and 2001, and the Dr.-Ing. degree in mining engineering from the Technische Universitaet Bergakademie Freiberg, Germany, in 2005. From 1996 to 2008, he was a Lecturer at HUMG. He was appointed an Associate Professor and Professor at the Surface Mining Department, HUMG, in 2009 and 2018. Since 2024, he is a Visiting Professor at Vietnam Institute of Geosciences and Mineral Resources. He is the author and co-author of 26 books, over 280 papers in international and national journals, and conference proceedings. His research interests include the advanced mining engineering, friendly environmental and smart mining, and the uses of AI in predicting impacts of mining and engineering activities on the environment for sustainable development. Prof. Xuan-Nam Bui was an Editor-in-Chief of the Journal of Mining and Earth Sciences of HUMG. He is also a member of the Society of Mining Professors, Vietnam Minning Science and Technology Association, Vietnam Blasting Engineering Association and some editorial boards of reputed international and national journals.

Choi, Yosoon
Prof. Yosoon Choi received a BS degree at the School of Civil, Urban and Geosystem Engineering, Seoul National University, Korea, in 2004. He received a PhD degree at the Department of Energy Systems Engineering, Seoul National University, in 2009. He was a Post-Doc fellow at the Department of Energy and Mineral Engineering at Pennsylvania State University, USA.

He is a Professor at the Department of Energy Resources Engineering at Pukyong National University, Korea. He is also leading the Geo-ICT Laboratory at Pukyong National University since 2011. He has been working in the area of Smart Mining, Renewables in Mining, AICBM (AI, IoT, Cloud, Big Data, Mobile) Convergence, Energy Resources Engineering, Mining Engineering, Geographic Information Systems (GIS), 3D Geo-modeling, Operations Research, Solar Energy Engineering.

Zhang, Wengang
Prof. Wengang ZHANG is currently full professor in School of Civil Engineering, Chongqing University, China. His research interests focus on impact assessment on the built environment induced by underground construction, as well as big data and machine learning in geotechnics and geoengineering. He published more than 90 ISI papers in Web of Sciences and 27 conference papers. He is now the member of the ISSMGE TC304 (Reliability), TC309 (Machine Learning), and TC219 (System Performance of Geotechnical Structures), SRMEG, ISRM, IAEG, CISMGE. Dr Zhang has been selected as the World's Top 2% Scientists 2020.

Nguyen, Hoang
Dr. Hoang Nguyen is a highly accomplished lecturer and researcher at the Hanoi University of Mining and Geology in Vietnam. In 2020, he obtained his PhD degree from the Surface Mining Department at the Mining Faculty of the same institution. Dr. Nguyen's academic journey has taken him to various research institutions around the world, including Pukyong National University in Busan, Korea, and the Institute of Research and Development at Duy Tan University in Da Nang, Vietnam, as a visiting researcher.

With an extensive publication record, Dr. Hoang Nguyen has authored two books and over 100 papers that are indexed in renowned databases such as Web of Sciences (SCI, SCIE, SSCI). Additionally, he serves as an editor for several esteemed journals. His research interests encompass a wide range of cutting-edge fields, including artificial intelligence, machine learning, deep learning, computer vision, optimization algorithms, metaheuristic algorithms, advanced analytics, and their applications in engineering.

Dr. Hoang Nguyen possesses deep expertise in mining, blasting, geotechnical engineering, environment, natural hazards, and natural resources research. His contributions to the field have earned him recognition, such as the Young Talent Award in Science and Technology of Hanoi University of Mining and Geology in 2019. Furthermore, he has been acknowledged as one of the World's Top 2% Scientists in both 2021 and 2022, further highlighting his exceptional achievements in the scientific community.