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19 pages, 3623 KB

Open AccessArticle

Off-Site Geological Surveying of Longwall Face Based on the Fusion of Multi-Source Monitoring Data

by Mengbo Zhu, Ruoyu Rong, Zhizhen Liu, Xuebin Qin, Haonan Zhang and Shuaihong Kang

Mathematics 2025, 13(18), 3008; https://doi.org/10.3390/math13183008 - 17 Sep 2025

Viewed by 396

Abstract

A high-precision coal seam model is crucial to improving the adaptability of unmanned mining technology to geological conditions. However, the accuracy of a coal seam model constructed with boreholes and geophysical data is far from the required accuracy of unmanned mining (sub-decimeter level). [...] Read more.

A high-precision coal seam model is crucial to improving the adaptability of unmanned mining technology to geological conditions. However, the accuracy of a coal seam model constructed with boreholes and geophysical data is far from the required accuracy of unmanned mining (sub-decimeter level). Therefore, it is necessary to collect geological data revealed by mining and to update the coal seam model dynamically. As a solution to this problem, this paper proposes a new method for conducting off-site geological surveying of longwall faces by integrating multi-source monitoring data. The spatial attitudes of hydraulic supports are monitored to estimate the local dip angles of longwall face. A roof line calculation model was established, which integrates the local inclination angle of the longwall face, the number of hydraulic supports, and the roof elevation of the two roadways. Meanwhile, the local coal–rock columns at the camera observation point are extracted automatically using image segmentation and a proportional relationship between the picture and the actual scene. Coal and rock walls and a support guarding plate in the longwall face image are identified accurately using the coal-rock support segmentation model trained with U-net. Then, the height of the coal (or rock) wall above the coal–rock interface is estimated automatically according to the image segmentation and the similar proportion equation of actual longwall face and longwall face image. Combined with mining height information, the local coal–rock column can be extracted. Finally, the geological surveying profile of longwall face can be obtained by integrating the estimated roof line and local coal–rock columns. The field test demonstrated the efficacy of the method. This study helps to address a long-standing limitation of insufficient geological adaptability of intelligent mining technology. Full article

(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)

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19 pages, 1130 KB

Open AccessArticle

Optimizing Mine Ventilation Systems: An Advanced Mixed-Integer Linear Programming Model

by Deyun Zhong, Lixue Wen, Yulong Liu, Zhaohao Wu and Liguan Wang

Mathematics 2025, 13(18), 2906; https://doi.org/10.3390/math13182906 - 9 Sep 2025

Cited by 1 | Viewed by 901

Abstract

In the underground mine ventilation area, the absence of robust solutions for nonlinear programming models has impeded progress for decades. To overcome the enduring difficulty of solving nonlinear optimization models for mine ventilation optimization, a major technical bottleneck, we first develop an advanced [...] Read more.

In the underground mine ventilation area, the absence of robust solutions for nonlinear programming models has impeded progress for decades. To overcome the enduring difficulty of solving nonlinear optimization models for mine ventilation optimization, a major technical bottleneck, we first develop an advanced linear optimization technique. This method transforms the nonlinear ventilation optimization and regulation model into a linear control model, avoiding the limitation of difficulty in solving the nonlinear mathematical model. The linear strategy opens up a new solution idea for the nonlinear calculation of the mine ventilation optimization and regulation. Furthermore, this study introduces evaluation metrics for ventilation scheme quality, including minimal energy consumption, fewest adjustment points, and optimal placement of these points, enhancing flexibility in ventilation network optimization. By analyzing the ventilation model control objectives and constraints, we formulated a linear optimization model and developed a multi-objective mixed-integer programming model for ventilation network optimization. This paper constructs and verifies a calculation example model for mine ventilation optimization, assessing its reliability based on airflow distribution calculations. Full article

(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)

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28 pages, 8337 KB

Open AccessArticle

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 1000

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)

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17 pages, 1050 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1110

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)

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19 pages, 16429 KB

Open AccessArticle

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 1114

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 KB

Open AccessArticle

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 1386

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 KB

Open AccessArticle

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

Cited by 2 | Viewed by 1653

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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Review

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26 pages, 5672 KB

Open AccessReview

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

Cited by 4 | Viewed by 3166

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)

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