Nguyen H., Bui X.N., Topal E., Zhou J., Choi Y., Zhang W. (eds.) Applications of Artificial Intelligence in Mining and Geotechnical Engineering

Elsevier, 2023. — 448 p. — ISBN: 978-0443187649.Applications of Artificial Intelligence in Mining, Geotechnical, and Geoengineering provide 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.The role of artificial intelligence in smart mining.
Application of artificial neural networks and UAV-based air quality monitoring sensors for simulating dust emission in quarries.
Application of machine learning and metaheuristic algorithms for predicting dust emission (PM2.5) induced by drilling operations in open-pit mines.
Deep neural networks for the estimation of granite materials’ compressive strength using non-destructive indices.
Estimating the Cd2+ adsorption efficiency on nanotubular halloysites in weathered pegmatites using optimized artificial neural networks: Insights into predictive model development.
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.
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.
Application of artificial intelligence in estimating mining capital expenditure using radial basis function neural network optimized by metaheuristic algorithms.
Application of deep learning techniques for forecasting iron ore prices: A comparative study of long short-term memory neural network and convolutional neural network.
Optimization of large mining supply chains through mathematical programming.
Underground mine planning and scheduling optimization: Opportunities for embracing machine learning augmented capabilities.
Application of artificial intelligence in distinguishing genuine microseismic events from the noise signals in underground mines.
The implementation of AI-based modeling and optimization in mining backfill design.
Application of artificial intelligence in predicting blast-induced ground vibration.
Application of an expert extreme gradient boosting model to predict blast-induced air-overpressure in quarry mines.
Application of artificial intelligence in predicting rock fragmentation: A review.
Underground stope dilution optimization applying machine learning.
Applying a novel hybrid ALO-BPNN model to predict overbreak and underbreak areas in underground space.
Fragmentation by blasting size prediction using SVR-GOA and SVR-KHA techniques.
Application of machine vision in two-dimensional feature characterization of rock engineering.
Groundwater potential assessment in Dobrogea region of Romania using artificial intelligence and bivariate statistics.
Application of artificial intelligence techniques for the verification of pile capacity at construction site: A review.
Landslide susceptibility in a hilly region of Romania using artificial intelligence and bivariate statistics.
Spatial prediction of bridge displacement using deep learning models: A case study at Co Luy bridge.