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Mathematics
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Numerical Model and Artificial Intelligence in Mining Engineering
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Numerical Model and Artificial Intelligence in Mining Engineering

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 6539

Special Issue Editors

Dr. Zhi Yu

E-Mail Website
Guest Editor
Zijin School of Geology and Mining, Fuzhou University, Fuzhou 350116, China
Interests: artificial intelligence; numerical simulation; vibration analysis; mining; machine learning; rock blasting
Prof. Dr. Jianhua Hu

E-Mail Website
Guest Editor
Zijin School of Geology and Mining, Fuzhou University, Fuzhou 350116, China
Interests: underground mining; deep rock mechanics; slope stability; safety engineering; paste filling; cement paste microstructure
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jian Zhou

E-Mail Website
Guest Editor
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Interests: mining; rock blasting; statistical learning; predictive modeling; statistics; tunneling; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Binglei Li

E-Mail Website
Guest Editor
Zijin School of Geology and Mining, Fuzhou University, Fuzhou 350116, China
Interests: rock mechanics; numerical simulation; slope engineering; goaf stability

Special Issue Information

Dear Colleagues,

We invite you to contribute to this Special Issue on “Numerical Model and Artificial Intelligence in Mining Engineering”, with original research focused on numerical simulations or artificial intelligence techniques addressing engineering challenges in the mining sector.

In this Special Issue, original research articles and reviews are welcome. Topics of interest include but are not limited to, rock mechanics, drilling and blasting, rockburst prevention, and the application of machine learning techniques in mining engineering.

With the rapid advancement of computer technology, numerical simulation and artificial intelligence have emerged as innovative tools for addressing mining-related challenges. Numerical simulation technology allows for analyzing material unit scales, enabling researchers to gain a deeper understanding of on-site engineering issues and to analyze the underlying scientific principles. Artificial intelligence, as one of today's leading-edge technologies, mimics the process of human brain learning and knowledge application, offering novel solutions to complex nonlinear problems in mining. Consequently, new theories, technologies, and practical applications in these two areas merit significant attention.

We look forward to receiving your contributions.

Dr. Zhi Yu
Prof. Dr. Jianhua Hu
Prof. Dr. Jian Zhou
Dr. Binglei Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • surface mining
  • underground mining
  • rock mechanics
  • drilling and blasting
  • rockburst
  • finite element method
  • discrete element method
  • machine learning technique
  • deep-learning theory
  • metaheuristic algorithm

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Published Papers (5 papers)

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Research

19 pages, 5678 KiB  
Article
Microseismic Data-Driven Short-Term Rockburst Evaluation in Underground Engineering with Strategic Data Augmentation and Extremely Randomized Forest
by Shouye Cheng, Xin Yin, Feng Gao and Yucong Pan
Mathematics 2024, 12(22), 3502; https://doi.org/10.3390/math12223502 - 9 Nov 2024
Cited by 3 | Viewed by 848
Abstract
Rockburst is a common dynamic geological disaster in underground mining and tunneling engineering, characterized by randomness, abruptness, and impact. Short-term evaluation of rockburst potential plays an outsize role in ensuring the safety of workers, equipment, and projects. As is well known, microseismic monitoring [...] Read more.
Rockburst is a common dynamic geological disaster in underground mining and tunneling engineering, characterized by randomness, abruptness, and impact. Short-term evaluation of rockburst potential plays an outsize role in ensuring the safety of workers, equipment, and projects. As is well known, microseismic monitoring serves as a reliable short-term early-warning technique for rockburst. However, the large amount of microseismic data brings many challenges to traditional manual analysis, such as the timeliness of data processing and the accuracy of rockburst prediction. To this end, this study integrates artificial intelligence with microseismic monitoring. On the basis of a comprehensive consideration of class imbalance and multicollinearity, an innovative modeling framework that combines local outlier factor-guided synthetic minority oversampling and an extremely randomized forest with C5.0 decision trees is proposed for the short-term evaluation of rockburst potential. To determine the optimal hyperparameters, the whale optimization algorithm is embedded. To prove the efficacy of the model, a total of 93 rockburst cases are collected from various engineering projects. The results show that the proposed approach achieves an accuracy of 90.91% and a macro F1-score of 0.9141. Additionally, the local F1-scores on low-intensity and high-intensity rockburst are 0.9600 and 0.9474, respectively. Finally, the advantages of the proposed approach are further validated through an extended comparative analysis. The insights derived from this research provide a reference for microseismic data-based short-term rockburst prediction when faced with class imbalance and multicollinearity. Full article
(This article belongs to the Special Issue Numerical Model and Artificial Intelligence in Mining Engineering)
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14 pages, 15387 KiB  
Article
Optimization and Numerical Verification of Microseismic Monitoring Sensor Network in Underground Mining: A Case Study
by Chenglu Hou, Xibing Li, Yang Chen, Wei Li, Kaiqu Liu, Longjun Dong and Daoyuan Sun
Mathematics 2024, 12(22), 3500; https://doi.org/10.3390/math12223500 - 9 Nov 2024
Viewed by 875
Abstract
A scientific and reasonable microseismic monitoring sensor network is crucial for the prevention and control of rockmass instability disasters. In this study, three feasible sensor network layout schemes for the microseismic monitoring of Sanshandao Gold Mine were proposed, comprehensively considering factors such as [...] Read more.
A scientific and reasonable microseismic monitoring sensor network is crucial for the prevention and control of rockmass instability disasters. In this study, three feasible sensor network layout schemes for the microseismic monitoring of Sanshandao Gold Mine were proposed, comprehensively considering factors such as orebody orientation, tunnel and stope distributions, blasting excavation areas, construction difficulty, and maintenance costs. To evaluate and validate the monitoring effectiveness of the sensor networks, three layers of seismic sources were randomly generated within the network. Four levels of random errors were added to the calculated arrival time data, and the classical Geiger localization algorithm was used for locating validation. The distribution of localization errors within the monitoring area was analyzed. The results indicate that when the arrival time data are accurate or the error is between 0% and 2%, scheme 3 is considered the most suitable layout; when the error of the arrival time data is between 2% and 10%, scheme 2 is considered the optimal layout. These research results can provide important theoretical and technical guidance for the reasonable design of microseismic monitoring systems in similar mines or projects. Full article
(This article belongs to the Special Issue Numerical Model and Artificial Intelligence in Mining Engineering)
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17 pages, 3678 KiB  
Article
Predicting Factor of Safety of Slope Using an Improved Support Vector Machine Regression Model
by Daxing Lei, Yaoping Zhang, Zhigang Lu, Hang Lin and Zheyuan Jiang
Mathematics 2024, 12(20), 3254; https://doi.org/10.3390/math12203254 - 17 Oct 2024
Cited by 2 | Viewed by 1425
Abstract
To reduce the disasters caused by slope instability, this paper proposes a new machine learning (ML) model for slope stability prediction. This improved SVR model uses support vector machine regression (SVR) as the basic prediction tool and the grid search method with 5-fold [...] Read more.
To reduce the disasters caused by slope instability, this paper proposes a new machine learning (ML) model for slope stability prediction. This improved SVR model uses support vector machine regression (SVR) as the basic prediction tool and the grid search method with 5-fold cross-validation to optimize the hyperparameters to improve the prediction performance. Six features, namely, unit weight, cohesion, friction angle, slope angle, slope height, and pore pressure ratio, were taken as the input of the model, and the factor of safety was taken as the model output. Four statistical indicators, namely, the coefficient of determination (R2), mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE), were introduced to assess the generalization performance of the model. Finally, the feature importance score of the features was clarified by calculating the importance of the six features and visualizing them. The results show that the model can well describe the nonlinear relationship between features and the factor of safety. The R2, MAPE, MAE, and RMSE of the testing dataset were 0.901, 7.41%, 0.082, and 0.133, respectively. Compared with other ML models, the improved SVR model had a better effect. The most sensitive feature was unit weight. Full article
(This article belongs to the Special Issue Numerical Model and Artificial Intelligence in Mining Engineering)
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17 pages, 1048 KiB  
Article
II-LA-KM: Improved Initialization of a Learning-Augmented Clustering Algorithm for Effective Rock Discontinuity Grouping
by Yihang Xu, Junxi Wu, Guoyan Zhao, Meng Wang and Xing Zhou
Mathematics 2024, 12(20), 3195; https://doi.org/10.3390/math12203195 - 12 Oct 2024
Viewed by 1039
Abstract
Rock mass discontinuities are an excellent information set for reflecting the geometric, spatial, and physical properties of the rock mass. Using clustering algorithms to analyze them is a significant way to select advantageous orientations of structural surfaces and provide a scientific theoretical basis [...] Read more.
Rock mass discontinuities are an excellent information set for reflecting the geometric, spatial, and physical properties of the rock mass. Using clustering algorithms to analyze them is a significant way to select advantageous orientations of structural surfaces and provide a scientific theoretical basis for other rock mass engineering research. Traditional clustering algorithms often suffer from sensitivity to initialization and lack practical applicability, as discontinuity data are typically rough, low-precision, and unlabeled. Confronting these challenges, II-LA-KM, a learning-augmented clustering algorithm with improved initialization for rock discontinuity grouping, is proposed. Our method begins with heuristically selecting initial centers to ensure they are well-separated. Then, optimal transport is used to adjust these centers, minimizing the transport cost between them and other points. To enhance fault tolerance, a learning-augmented algorithm is integrated that iteratively reduces clustering costs, refining the initial results toward optimal clustering. Extensive experiments on a simulated artificial dataset and a real dataset from Woxi, Hunan, China, featuring both orientational and non-orientational attributes, demonstrate the effectiveness of II-LA-KM. The algorithm achieves a 97.5% accuracy on the artificial dataset and successfully differentiates between overlapping groups. Its performance is even more pronounced on the real dataset, underscoring its robustness for handling complex and noisy data. These strengths make our approach highly beneficial for practical rock discontinuity grouping applications. Full article
(This article belongs to the Special Issue Numerical Model and Artificial Intelligence in Mining Engineering)
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23 pages, 8640 KiB  
Article
Investigation of Micro-Scale Damage and Weakening Mechanisms in Rocks Induced by Microwave Radiation and Their Associated Strength Reduction Patterns: Employing Meta-Heuristic Optimization Algorithms and Extreme Gradient Boosting Models
by Zhongyuan Gu, Xin Xiong, Chengye Yang and Miaocong Cao
Mathematics 2024, 12(18), 2954; https://doi.org/10.3390/math12182954 - 23 Sep 2024
Viewed by 1435
Abstract
Microwave-assisted mechanical rock breaking represents an innovative technology in the realm of mining excavation. The intricate and variable characteristics of geological formations necessitate a comprehensive understanding of the interplay between microwave-induced rock damage and the subsequent deterioration in rock strength. This study conducted [...] Read more.
Microwave-assisted mechanical rock breaking represents an innovative technology in the realm of mining excavation. The intricate and variable characteristics of geological formations necessitate a comprehensive understanding of the interplay between microwave-induced rock damage and the subsequent deterioration in rock strength. This study conducted microwave irradiation damage assessments on 78 distinct rock samples, encompassing granite, sandstone, and marble. A total of ten critical parameters were identified: Microwave Irradiation Time (MIT), Microwave Irradiation Power (MIP), Longitudinal Wave Velocity prior to Microwave Treatment (LWVB), Longitudinal Wave Velocity post-Microwave Treatment (LWVA), Percentage Decrease in Longitudinal Wave Velocity (LWVP), Porosity before Microwave Treatment (PB), Porosity after Microwave Treatment (PA), Percentage Increase in Porosity (PP), and Uniaxial Compressive Strength following Microwave Treatment (UCSA). Utilizing the Pied Kingfisher Optimizer (PKO) alongside Extreme Gradient Boosting (XGBoost), we developed a PKO-XGBoost machine learning model to elucidate the relationship between UCSA and the nine additional parameters. This model was benchmarked against other prevalent machine learning frameworks, with Shapley additive explanatory methods employed to assess each parameter’s influence on UCSA. The findings reveal that the PKO-XGBoost model provides superior accuracy in delineating relationships among rock physical properties, microwave irradiation variables, microscopic attributes of rocks, and UCSA. Notably, PA emerged as having the most significant effect on UCSA, indicating that microwave-induced microscopic damage is a primary contributor to reductions in rock strength. Additionally, MR exhibited substantial influence; under identical microwave irradiation conditions, rocks with lower density demonstrated greater susceptibility to strength degradation. Furthermore, during microwave-assisted rock breaking operations, it is imperative to establish optimal MIT and MIP values to effectively diminish UCSA while facilitating mechanical cutting processes. The insights derived from this research offer a more rapid, cost-efficient approach for accurately assessing correlations between microwave irradiation parameters and resultant rock damage—providing essential data support for enhancing mechanical rock-breaking efficiency. Full article
(This article belongs to the Special Issue Numerical Model and Artificial Intelligence in Mining Engineering)
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