Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.6
2.2
Journals
Mathematics
Special Issues
Mathematical Modeling and Analysis in Mining Engineering
share announcement

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: 31 March 2026 | Viewed by 5224

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

28 pages, 8337 KiB  
Article
Collision Detection Algorithms for Autonomous Loading Operations of LHD-Truck Systems in Unstructured Underground Mining Environments
by Mingyu Lei, Pingan Peng, Liguan Wang, Yongchun Liu, Ru Lei, Chaowei Zhang, Yongqing Zhang and Ya Liu
Mathematics 2025, 13(15), 2359; https://doi.org/10.3390/math13152359 - 23 Jul 2025
Viewed by 115
Abstract
This study addresses collision detection in the unmanned loading of ore from load-haul-dump (LHD) machines into mining trucks in underground metal mines. Such environments present challenges like heavy dust, confined spaces, sensor occlusions, and poor lighting. This work identifies two primary collision risks [...] Read more.
This study addresses collision detection in the unmanned loading of ore from load-haul-dump (LHD) machines into mining trucks in underground metal mines. Such environments present challenges like heavy dust, confined spaces, sensor occlusions, and poor lighting. This work identifies two primary collision risks and proposes corresponding detection strategies. First, for collisions between the bucket and tunnel walls, LiDAR is used to collect 3D point cloud data. The point cloud is processed through filtering, downsampling, clustering, and segmentation to isolate the bucket and tunnel wall. A KD-tree algorithm is then used to compute distances to assess collision risk. Second, for collisions between the bucket and the mining truck, a kinematic model of the LHD’s working device is established using the Denavit–Hartenberg (DH) method. Combined with inclination sensor data and geometric parameters, a formula is derived to calculate the pose of the bucket’s tip. Key points from the bucket and truck are then extracted to perform collision detection using the oriented bounding box (OBB) and the separating axis theorem (SAT). Simulation results confirm that the derived pose estimation formula yields a maximum error of 0.0252 m, and both collision detection algorithms demonstrate robust performance. Full article
(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)
►▼ Show Figures

Figure 1

17 pages, 1050 KiB  
Article
Multi-Objective Ore Blending Optimization for Polymetallic Open-Pit Mines Based on Improved Matter-Element Extension Model and NSGA-II
by Jun Xiang, Jianhong Chen, Aishu Zhang, Xing Zhao, Shengyuan Zhuo and Shan Yang
Mathematics 2025, 13(11), 1843; https://doi.org/10.3390/math13111843 - 31 May 2025
Viewed by 508
Abstract
With the increasing demand for mineral resources, sustainable mining development faces challenges such as low resource utilization efficiency. Ore blending optimization has emerged as a critical approach to enhance resource utilization. This study constructs a multi-objective ore blending optimization system for complex polymetallic [...] Read more.
With the increasing demand for mineral resources, sustainable mining development faces challenges such as low resource utilization efficiency. Ore blending optimization has emerged as a critical approach to enhance resource utilization. This study constructs a multi-objective ore blending optimization system for complex polymetallic open-pit mines based on the improved matter-element extension model and NSGA-II algorithm. By identifying key blending factors, objective functions are established to minimize both total ore quantity deviation and grade deviation, with six constraints defined to reflect production capacity limits. The NSGA-II algorithm is employed to solve the multi-objective optimization problem, generating a Pareto optimal solution set from which the optimal ore blending scheme is selected using the improved matter-element extension model. A case verification at Dabaoshan Mine demonstrates that the model-verified scheme achieves 1.035% higher total production accuracy than the planned value and 2.828% higher than actual production, while improving Cu grade deviation accuracy by 7.021% over the plan and 1.064% over actual production, and S grade deviation accuracy by 33.027% over the plan and 3.127% over actual production. This study, through the construction of systematic ore blending theory and empirical analysis, provides an important theoretical framework and methodological support for subsequent research on ore blending in polymetallic open-pit mines. It demonstrates significant practical application value in Dabaoshan Mine, offering an intelligent mine solution that combines scientific rationality and engineering practicability for polymetallic open-pit mines. Full article
(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)
►▼ Show Figures

Figure 1

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 757
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)
►▼ Show Figures

Figure 1

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
Cited by 2 | Viewed by 1012
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)
►▼ Show Figures

Figure 1

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 1215
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)
►▼ Show Figures

Figure 1

Review

Jump to: Research

26 pages, 5672 KiB  
Review
Development Status and Trend of Mine Intelligent Mining Technology
by Zhuo Wang, Lin Bi, Jinbo Li, Zhaohao Wu and Ziyu Zhao
Mathematics 2025, 13(13), 2217; https://doi.org/10.3390/math13132217 - 7 Jul 2025
Viewed by 671
Abstract
Intelligent mining technology, as the core driving force for the digital transformation of the mining industry, integrates cyber-physical systems, artificial intelligence, and industrial internet technologies to establish a “cloud–edge–end” collaborative system. In this paper, the development trajectory of intelligent mining technology has been [...] Read more.
Intelligent mining technology, as the core driving force for the digital transformation of the mining industry, integrates cyber-physical systems, artificial intelligence, and industrial internet technologies to establish a “cloud–edge–end” collaborative system. In this paper, the development trajectory of intelligent mining technology has been systematically reviewed, which has gone through four stages: stand-alone automation, integrated automation and informatization, digital and intelligent initial, and comprehensive intelligence. And the current development status of “cloud–edge–end” technologies has been reviewed: (i) The end layer achieves environmental state monitoring and precise control through a multi-source sensing network and intelligent equipment. (ii) The edge layer leverages 5G and edge computing to accomplish real-time data processing, 3D dynamic modeling, and safety early warning. (iii) The cloud layer realizes digital planning and intelligent decision-making, based on the industrial Internet platform. The three-layer collaboration forms a “perception–analysis–decision–execution” closed loop. Currently, there are still many challenges in the development of the technology, including the lack of a standardization system, the bottleneck of multi-source heterogeneous data fusion, the lack of a cross-process coordination of the equipment, and the shortage of interdisciplinary talents. Accordingly, this paper focuses on future development trends from four aspects, providing systematic solutions for a safe, efficient, and sustainable mining operation. Technological evolution will accelerate the formation of an intelligent ecosystem characterized by “standard-driven, data-empowered, equipment-autonomous, and human–machine collaboration”. Full article
(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop