Applications of Artificial Intelligence in Mining and Geotechnical Engineering
- 1st Edition - November 20, 2023
- Editors: Hoang Nguyen, Xuan Nam Bui, Erkan Topal, Jian Zhou, Yosoon Choi, Wengang Zhang
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 8 7 6 4 - 3
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 8 7 6 5 - 0
Applications of Artificial Intelligence in Mining, Geotechnical and Geoengineering provides recent advances in mining, geotechnical and geoengineering, as well as applicati… Read more
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Request a sales quoteApplications 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.
- Guides readers through the process of gathering, processing, and analyzing datasets specifically tailored for mining, geotechnical, and engineering challenges.
- Examines the evolution and practical implementation of artificial intelligence models in predicting, forecasting, and optimizing solutions for mining, geotechnical, and engineering problems.
- Offers cutting-edge methodologies to address the most demanding and complex issues encountered in the fields of mining, geotechnical studies, and engineering.
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Editors’ biography
- Preface
- Chapter 1 The role of artificial intelligence in smart mining
- Abstract
- Acknowledgments
- 1 Industry 4.0 and smart mining
- 2 Implementation levels of a smart mining site
- 3 Role of artificial intelligence in smart mining
- 4 Future perspectives
- References
- Chapter 2 Application of artificial neural networks and UAV-based air quality monitoring sensors for simulating dust emission in quarries
- Abstract
- Conflicts of Interest
- 1 Introduction
- 2 Proposed UMS-AM system
- 3 Study site
- 4 Data monitoring measurement and methodology
- 5 Results
- 6 Conclusions
- References
- Chapter 3 Application of machine learning and metaheuristic algorithms for predicting dust emission (PM2.5) induced by drilling operations in open-pit mines
- Abstract
- Acknowledgments
- 1 Introduction
- 2 Methodology
- 3 Data acquisition and preparation
- 4 Results and discussion
- 5 Conclusion
- References
- Chapter 4 Deep neural networks for the estimation of granite materials’ compressive strength using non-destructive indices
- Abstract
- 1 Introduction
- 2 Granite materials through history—A short overview
- 3 Materials and methods
- 4 Results and discussion
- 5 Limitations and future works
- 6 Conclusions
- References
- Chapter 5 Estimating the Cd2+ adsorption efficiency on nanotubular halloysites in weathered pegmatites using optimized artificial neural networks: Insights into predictive model development
- Abstract
- Acknowledgments
- 1 Introduction
- 2 Materials description
- 3 Artificial neural network
- 4 Optimization algorithms used
- 5 Framework of optimized artificial neural networks
- 6 Estimation of Cd2+ adsorption efficiency of halloysite
- 7 Discussion
- 8 Conclusion
- References
- Chapter 6 Application of artificial intelligence in predicting slope stability in open-pit mines: A case study with a novel imperialist competitive algorithm-based radial basis function neural network
- Abstract
- Acknowledgments
- 1 Introduction
- 2 Methodology
- 3 Application
- 4 Results and discussion
- 5 Conclusion
- References
- Chapter 7 Application of cubist algorithm, multi-layer perceptron neural network, and metaheuristic algorithms to estimate the ore production of truck-haulage systems in open-pit mines
- Abstract
- Acknowledgments
- 1 Introduction
- 2 Dataset used
- 3 Methodology
- 4 Results and discussions
- 5 Conclusion
- References
- Chapter 8 Application of artificial intelligence in estimating mining capital expenditure using radial basis function neural network optimized by metaheuristic algorithms
- Abstract
- Acknowledgments
- 1 Introduction
- 2 Methodology
- 3 Data preparation
- 4 Results and discussions
- 5 Conclusions
- References
- Chapter 9 Application of deep learning techniques for forecasting iron ore prices: A comparative study of long short-term memory neural network and convolutional neural network
- Abstract
- Acknowledgments
- 1 Introduction
- 2 Methodology
- 3 Dataset used
- 4 Results and discussion
- 5 Conclusion
- References
- Chapter 10 Optimization of large mining supply chains through mathematical programming
- Abstract
- 1 Overview
- 2 Modeling challenges
- 3 Mining companies applying advanced analytics
- 4 Optimization model
- 5 Case study
- 6 Conclusions
- References
- Chapter 11 Underground mine planning and scheduling optimization: Opportunities for embracing machine learning augmented capabilities
- Abstract
- 1 Introduction
- 2 Applications of machine learning in mine planning and scheduling
- 3 Conclusions
- References
- Chapter 12 Application of artificial intelligence in distinguishing genuine microseismic events from the noise signals in underground mines
- Abstract
- 1 Introduction
- 2 Database and statistical analysis
- 3 Methods
- 4 Summary and conclusions
- Appendix
- References
- Chapter 13 The implementation of AI-based modeling and optimization in mining backfill design
- Abstract
- 1 Introduction
- 2 The use of AI in backfill design
- 3 Case studies
- 4 Conclusions
- References
- Chapter 14 Application of artificial intelligence in predicting blast-induced ground vibration
- Abstract
- 1 Introduction
- 2 Case study
- 3 Methodology
- 4 Results and discussion
- 5 Conclusion
- References
- Chapter 15 Application of an expert extreme gradient boosting model to predict blast-induced air-overpressure in quarry mines
- Abstract
- Acknowledgments
- 1 Introduction
- 2 Background of case study
- 3 Methodology
- 4 Results and discussion
- 5 Conclusions
- References
- Chapter 16 Application of artificial intelligence in predicting rock fragmentation: A review
- Abstract
- Acknowledgments
- 1 Introduction
- 2 Blasting and fragmentation
- 3 Blastability in traditional literature—The empirical approach
- 4 Use of AI in blastability
- 5 Challenges and future directions
- 6 Conclusion
- References
- Chapter 17 Underground stope dilution optimization applying machine learning
- Abstract
- 1 Introduction
- 2 Applications of machine learning in underground stope dilution optimization
- 3 Conclusions
- References
- Chapter 18 Applying a novel hybrid ALO-BPNN model to predict overbreak and underbreak area in underground space
- Abstract
- 1 Introduction
- 2 Methodologies
- 3 Data preparation and performance evaluation
- 4 Results and discussion
- 5 Conclusion and summary
- References
- Chapter 19 Fragmentation by blasting size prediction using SVR-GOA and SVR-KHA techniques
- Abstract
- 1 Introduction
- 2 Data analysis and pre-processing
- 3 Method
- 4 Model development and discussion
- 5 Conclusion
- References
- Chapter 20 Application of machine vision in two-dimensional feature characterization of rock engineering
- Abstract
- 1 Introduction
- 2 Rock mass information acquisition method
- 3 Traditional image algorithms
- 4 Deep learning algorithms
- 5 Conclusion
- References
- Chapter 21 Groundwater potential assessment in Dobrogea region of Romania using artificial intelligence and bivariate statistics
- Abstract
- 1 Introduction
- 2 Study area
- 3 Data
- 4 Methods
- 5 Results and discussion
- 6 Conclusions
- References
- Chapter 22 Application of artificial intelligence techniques for the verification of pile capacity at construction site: A review
- Abstract
- 1 Introduction
- 2 Background of soft computing
- 3 Application of AI for pile capacity prediction
- 4 Discussion
- 5 Future perspective
- 6 Conclusion
- References
- Chapter 23 Landslide susceptibility in a hilly region of Romania using artificial intelligence and bivariate statistics
- Abstract
- 1 Introduction
- 2 Study area
- 3 Data
- 4 Methods
- 5 Results and discussions
- 6 Conclusions
- References
- Chapter 24 Spatial prediction of bridge displacement using deep learning models: A case study at Co Luy bridge
- Abstract
- 1 Introduction
- 2 Study area and data used
- 3 Methods
- 4 Results and analysis
- 5 Discussions
- 6 Conclusions
- References
- Index
- No. of pages: 500
- Language: English
- Edition: 1
- Published: November 20, 2023
- Imprint: Elsevier
- Paperback ISBN: 9780443187643
- eBook ISBN: 9780443187650
HN
Hoang Nguyen
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.
XB
Xuan Nam Bui
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.
ET
Erkan Topal
JZ
Jian Zhou
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.
YC
Yosoon Choi
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.
WZ
Wengang Zhang
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.