Applications of Artificial Intelligence in Mining and Geotechnical Engineering

Hoang Nguyen, Xuan Nam Bui, Erkan Topal, Jian Zhou, Yosoon Choi, Wengang Zhang

• Publisher: Elsevier
• Year: 2023
• Print ISBN: 9780443187643
• eBook ISBN: 9780443187650
• Topic: Sciences biologiques
• Subtopic: Théorie économique

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. - 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.

Table of contents

• Cover
• Front Matter
• Table of Contents
• Copyright
• Contributors
• Editors’ biography
• Preface
• List of Illustrations
• List of Tables
• Chapter 1 : The role of artificial intelligence in smart mining
• Chapter 2 : Application of artificial neural networks and UAV-based air quality monitoring sensors for simulating dust emission in quarries
• Chapter 3 : Application of machine learning and metaheuristic algorithms for predicting dust emission (PM2.5) induced by drilling operations in open-pit mines
• Chapter 4 : Deep neural networks for the estimation of granite materials’ compressive strength using non-destructive indices
• Chapter 5 : Estimating the Cd2+ adsorption efficiency on nanotubular halloysites in weathered pegmatites using optimized artificial neural networks: Insights into predictive model development
• 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
• 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
• Chapter 8 : Application of artificial intelligence in estimating mining capital expenditure using radial basis function neural network optimized by metaheuristic algorithms
• 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
• Chapter 10 : Optimization of large mining supply chains through mathematical programming
• Chapter 11 : Underground mine planning and scheduling optimization: Opportunities for embracing machine learning augmented capabilities
• Chapter 12 : Application of artificial intelligence in distinguishing genuine microseismic events from the noise signals in underground mines
• Chapter 13 : The implementation of AI-based modeling and optimization in mining backfill design
• Chapter 14 : Application of artificial intelligence in predicting blast-induced ground vibration
• Chapter 15 : Application of an expert extreme gradient boosting model to predict blast-induced air-overpressure in quarry mines
• Chapter 16 : Application of artificial intelligence in predicting rock fragmentation: A review
• Chapter 17 : Underground stope dilution optimization applying machine learning
• Chapter 18 : Applying a novel hybrid ALO-BPNN model to predict overbreak and underbreak area in underground space
• Chapter 19 : Fragmentation by blasting size prediction using SVR-GOA and SVR-KHA techniques
• Chapter 20 : Application of machine vision in two-dimensional feature characterization of rock engineering
• Chapter 21 : Groundwater potential assessment in Dobrogea region of Romania using artificial intelligence and bivariate statistics
• Chapter 22 : Application of artificial intelligence techniques for the verification of pile capacity at construction site: A review
• Chapter 23 : Landslide susceptibility in a hilly region of Romania using artificial intelligence and bivariate statistics
• Chapter 24 : Spatial prediction of bridge displacement using deep learning models: A case study at Co Luy bridge
• Index
• A