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Mathematics
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Mathematical Modeling and Analysis in Mining Engineering
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Mathematical Modeling and Analysis in Mining Engineering

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 10 June 2025 | Viewed by 2291

Special Issue Editor

Dr. Lin Bi

E-Mail Website
Guest Editor
School of Resources and Safety Engineering, Central South University, Changsha, China
Interests: digital mine and intelligent mining

Special Issue Information

Dear Colleagues,

In the field of mining engineering, mathematics serves as a fundamental tool across various specialized domains, including (but not limited to) geological modeling and geostatistics, ventilation network analysis and solutions, mine production planning and scheduling, engineering geological model and stability analysis, and mining complex scene perception and modeling. Geological modeling and geostatistics leverage spatial interpolation methods like Kriging to estimate mineral deposit grades and variability, optimizing mine design and reducing uncertainty. Ventilation network analysis and solution employ graph theory and computational fluid dynamics to simulate airflow, ensuring worker safety and efficient contaminant dispersion. Mine production planning and scheduling utilize mathematical optimization techniques to schedule extraction activities, aligning with market demands and operational constraints. Engineering geological models incorporate stability analysis through numerical simulations and partial differential equations to assess rock mechanics and prevent failures. Mining complex scene perception and modeling integrate machine learning and computer vision algorithms to analyze sensor data, enhancing situational awareness and decision-making processes. These applications collectively demonstrate the critical role of mathematics in advancing the efficiency, safety, and sustainability of modern mining operations.

We seek papers that offer innovative approaches to geological modeling and geostatistics and that address the implementation of various interactions within mining systems. We also welcome research on topics such as ventilation network analysis and solutions, mine production planning and scheduling, engineering geological modeling and stability analysis, and mining complex scene perception and modeling. We are particularly interested in studies where the integration of mathematical and computational methods advances our understanding and solutions in these areas.

We look forward to receiving your contributions.

Dr. Lin Bi
Guest Editor

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

  • geological modeling and geostatistics
  • ventilation network analysis and solutions
  • mine production planning and scheduling
  • engineering geological model and stability analysis
  • mining complex scene perception and modeling

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Published Papers (3 papers)

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Research

19 pages, 16429 KiB  
Article
Three-Dimensional Stratigraphic Structure and Property Collaborative Modeling in Urban Engineering Construction
by Baoyi Zhang, Yanli Zhu, Tongyun Zhang, Xian Zhou, Binhai Wang, Or Aimon Brou Koffi Kablan and Jixian Huang
Mathematics 2025, 13(3), 345; https://doi.org/10.3390/math13030345 - 22 Jan 2025
Viewed by 376
Abstract
In urban engineering construction, ensuring the stability and safety of subsurface geological structures is as crucial as surface planning and aesthetics. This study proposes a novel multivariate radial basis function (MRBF) interpolant for the three-dimensional (3D) modeling of engineering geological properties, constrained by [...] Read more.
In urban engineering construction, ensuring the stability and safety of subsurface geological structures is as crucial as surface planning and aesthetics. This study proposes a novel multivariate radial basis function (MRBF) interpolant for the three-dimensional (3D) modeling of engineering geological properties, constrained by the stratigraphic structural model. A key innovation is the incorporation of a well-sampled geological stratigraphical potential field (SPF) as an ancillary variable, which enhances the interpolation of geological properties in areas with sparse and uneven sampling points. The proposed MRBF method outperforms traditional interpolation techniques by showing reduced dependency on the distribution of sampling points. Furthermore, the study calculates the bearing capacity of individual pile foundations based on precise stratigraphic thicknesses, yielding more accurate results compared to conventional methods that average these values across the entire site. Additionally, the integration of 3D geological models with urban planning facilitates the development of comprehensive urban digital twins, optimizing resource management, improving decision-making processes, and contributing to the realization of smart cities through more efficient data-driven urban management strategies. Full article
(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)
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18 pages, 54250 KiB  
Article
Surrounding Rock Squeezing Classification in Underground Engineering Using a Hybrid Paradigm of Generative Artificial Intelligence and Deep Ensemble Learning
by Shouye Cheng, Xin Yin, Feng Gao and Yucong Pan
Mathematics 2024, 12(23), 3832; https://doi.org/10.3390/math12233832 - 4 Dec 2024
Viewed by 635
Abstract
Surrounding rock squeezing is a common geological disaster in underground excavation projects (e.g., TBM tunneling and deep mining), which has adverse effects on construction safety, schedule, and property. To predict the squeezing of the surrounding rock accurately and quickly, this study proposes a [...] Read more.
Surrounding rock squeezing is a common geological disaster in underground excavation projects (e.g., TBM tunneling and deep mining), which has adverse effects on construction safety, schedule, and property. To predict the squeezing of the surrounding rock accurately and quickly, this study proposes a hybrid machine learning paradigm that integrates generative artificial intelligence and deep ensemble learning. Specifically, conditional tabular generative adversarial network is devised to solve the problems of data shortage and class imbalance for data augmentation at the data level, and the deep random forest is built based on the augmented data for subsequent squeezing classification. A total of 139 historical squeezing cases are collected worldwide to validate the efficacy of the proposed modeling paradigm. The results reveal that this paradigm achieves a prediction accuracy of 92.86% and a macro F1-score of 0.9292. In particular, the individual F1-scores on strong squeezing and extremely strong squeezing are more than 0.9, with excellent prediction reliability for high-intensity squeezing. Finally, a comparative analysis with traditional machine learning techniques is conducted and the superiority of this paradigm is further verified. This study provides a valuable reference for surrounding rock squeezing classification under a limited data environment. Full article
(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)
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18 pages, 9450 KiB  
Article
A Novel Axial Load Inversion Method for Rock Bolts Based on the Surface Strain of a Bearing Plate
by Yongchao Lei, Xingliang Xu, Suchuan Tian and Hao Shi
Mathematics 2024, 12(22), 3480; https://doi.org/10.3390/math12223480 - 7 Nov 2024
Viewed by 782
Abstract
Anchor rock bolts are among the essential support components employed in coal mine support engineering. Measuring the axial load of the supporting anchor bolts constitutes an important foundation for evaluating the support effect and the mechanical state of the surrounding rock. The existing [...] Read more.
Anchor rock bolts are among the essential support components employed in coal mine support engineering. Measuring the axial load of the supporting anchor bolts constitutes an important foundation for evaluating the support effect and the mechanical state of the surrounding rock. The existing methods for measuring the axial load of rock bolts have difficulty meeting the actual demands in terms of accuracy and means. Therefore, we propose a novel inverse method for determining the axial load of rock bolts. On the basis of the dynamic relationship between the axial load of the anchor bolt and the strain of the plate, a calculation model for the inverse analysis of the axial load from the plate strain is presented, and it is verified and corrected through finite element analysis and indoor physical experiments. By combining the calculation model with the digital image correlation method, a low costinversion of the axial load of the anchor bolt in actual support engineering is achieved. The experimental results demonstrate that the average errors of the load inversion of anchor bolts in three different states via the theory and method proposed in this paper are less than 8.8% (4 kN), 3.6% (3.2 kN), and 14.7% (5.5 kN), respectively, and the average error of the axial load of the rock bolts in the proposed method is only 4.23 kN. It possesses relatively high accuracy and can be effectively applied in the actual production processes of mines. Full article
(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)
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