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

Applications of Artificial Intelligence in Mining and Geotechnical Engineering

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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.

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

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.

Affiliations and expertise

Lecturer, Surface Mining Department, Mining Faculty, Hanoi University of Mining and Geology (HUMG), Hanoi, Vietnam

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 Technische Univesitaet Bergakademie Freiberg, Germany, in 2005. From 1996 to 2008, he was a Lecturer at HUMG. He was an Associate Professor with the Surface Mining Department, HUMG, in 2009. Since 2018, he has been appointed as a Full Professor there. He is the author and co-author of 24 books, more than 200 articles, including 67 ISI and 38 Scopus articles. His research interests include the uses of AI in building models, evaluating and predicting impacts of mining and engineering activities on the environment for sustainable development. He is an Editor-in-Chief of the Journal of Mining and Earth Sciences of HUMG. Prof. Xuan-Nam Bui is a member of the Society of Mining Professors and some editorial boards of national and international journals.

Affiliations and expertise

Mining Faculty, Hanoi University of Mining and Geology, Hanoi, Vietnam

Erkan Topal

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.

Affiliations and expertise

Mining Engineering and Metallurgical Engineering, Western Australian School of Mines, Faculty of Science and Engineering, Curtin University, Australia

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.

Affiliations and expertise

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.

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.

Affiliations and expertise

Professor, Department of Energy Resources Engineering, Pukyong National University, Busan, South Korea

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.

Affiliations and expertise

Full Professor, School of Civil Engineering, Chongqing University, China