Application of Artificial Intelligence in the Mining Industry

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: 30 August 2024 | Viewed by 449

Special Issue Editor

Special Issue Information

Dear Colleagues,

AI has been instrumental to the world and has enhanced new techniques and new products. For the mining industry, from exploration, development, and beneficiation to reclamation, AI has been invented and will be applied in every process in mining.

Nowadays, new artificial algorithms and models have been developed and parts of them have started to be utilized in mining industries to improve efficiency and accuracy. Moreover, some models have been used for answering questions and giving advice to managers, miners, or technicians. Due to AI, mining is not an information island, and is instead a knowledge center. In this intelligent center, machines, people, the environment, geology, and engineering have been used in concert to deal with future problems in the mining industry. Many researchers have carried out the relevant work, and this Special Issue will encourage interdisciplinary communication, especially in the mining industry.

This Special Issue will publish high-quality original research papers in the following fields (among others):

  • Application of artificial intelligence;
  • Mining data processing;
  • Numerical modeling;
  • Prediction and regression for mining engineering;
  • New machine learning and deep learning algorithm application;
  • Big data analysis.

Dr. Yuantian Sun
Guest Editor

Manuscript Submission Information

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Keywords

  • AI
  • modelling
  • mining
  • prediction
  • machine learning

Published Papers (1 paper)

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Research

14 pages, 3966 KiB  
Article
Prediction of Ground Vibration Velocity Induced by Long Hole Blasting Using a Particle Swarm Optimization Algorithm
by Lianku Xie, Qinglei Yu, Jiandong Liu, Chunping Wu and Guang Zhang
Appl. Sci. 2024, 14(9), 3839; https://doi.org/10.3390/app14093839 - 30 Apr 2024
Viewed by 317
Abstract
Obtaining accurate basic parameters for long hole blasting is challenging, and the resulting vibration damage significantly impacts key surface facilities. Predicting ground vibration velocity accurately and mitigating the harmful effects of blasting are crucial aspects of controlled blasting technology. This study focuses on [...] Read more.
Obtaining accurate basic parameters for long hole blasting is challenging, and the resulting vibration damage significantly impacts key surface facilities. Predicting ground vibration velocity accurately and mitigating the harmful effects of blasting are crucial aspects of controlled blasting technology. This study focuses on the prediction of ground vibration velocity induced by underground long hole blasting tests. Utilizing the fitting equation based on the US Bureau of Mines (USBM) formula as a baseline for predicting peak particle velocity, two machine learning models suitable for small sample data, Support Vector Regression (SVR) machine and Random Forest (RF), were employed. The models were optimized using the particle swarm optimization algorithm (PSO) to predict peak particle velocity with multiple parameters specific to long hole blasting. Mean absolute error (MAE), mean Squared error (MSE), and coefficient of determination (R2) were used to assess the model predictions. Compared with the fitting equation based on the USBM model, both the Support Vector Regression (SVR) and Random Forest (RF) models accurately and effectively predict peak particle velocity, enhancing prediction accuracy and efficiency. The SVR model exhibited slightly superior predictive performance compared to the RF model. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in the Mining Industry)
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