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Research on Optimization of Stope Structure Parameters Based on DBO-MLP-SVM Algorithm Combined with Numerical Simulation
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Kun Li1, Gang Wang2, Li Zhao3, Dengfeng Su1, Yingpeng Hu1, Baohui Tan1, Rui Wang1, xinxin Hou1
Mining Research and Development | 2026, 46(6) : 32 - 41
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Mining Research and Development | 2026, 46(6): 32-41
Research on Optimization of Stope Structure Parameters Based on DBO-MLP-SVM Algorithm Combined with Numerical Simulation
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Kun Li1, Gang Wang2, Li Zhao3, Dengfeng Su1, Yingpeng Hu1, Baohui Tan1, Rui Wang1, xinxin Hou1
Affiliations
  • 1.School of Environment and Resources, Southwest University of Science and Technology, Mianyang, Sichuan 621010, China
  • 2.Jinchuan Group Co., Ltd., Jinchang, Gansu 737100, China
  • 3.Sichuan Zhuchuang Safety Technology Co., Ltd., Chengdu, Sichuan 610046, China
Published: 2026-06-18
Outline
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Aiming at the problems that the traditional engineering analogy method is easily affected by subjective evaluation, and that the simple numerical simulation method involves complex modeling and heavy workload in the optimization of stope structure parameters, an artificial intelligence stope parameter optimization method based on DBO-MLP-SVM is proposed. The Dung Beetle Optimizer (DBO) was used to optimize the performance of Multilayer Perceptron (MLP) and the Support Vector Machine (SVM). The two were fused to overcome the defect that MLP is easy to fall into a local optimum, and the relevant mine stope parameters and stope stability classification data were collected to train the proposed model. Taking a limestone mine in southwest China as an example, the stability classification under different pillar widths was obtained by substituting the model, and the stope displacement, pillar deformation and surface settlement were analyzed and verified by FLAC3D numerical simulation. The results show that the DBO-MLP-SVM model predicts that the stope is stable when the pillar width is greater than 8 m, and the optimal stope structure parameter is pillar width×spacing = 8 m×8 m, which is consistent with the numerical simulation results. Compared with the traditional engineering analogy method, the production capacity of the stope after intelligent collaborative optimization increased by 51.80%, and the loss rate and dilution rate changed little, which effectively balances production efficiency and safety. This method provides an efficient and accurate technical approach for the optimization of stope structure parameters, which can serve as a reference for the optimization of mine stope parameters.

Stope structure parameter  /  DBO-MLP-SVM algorithm  /  Stope stability  /  Pillar width  /  Numerical simulation
李坤, 王刚, 赵黎, 粟登峰, 胡颖鹏, 谭宝会, 王瑞, 侯鑫鑫. 基于DBO-MLP-SVM算法联合数值模拟的采场结构参数优化研究. 矿业研究与开发, 2026 , 46 (6) : 32 -41 .
Kun Li, Gang Wang, Li Zhao, Dengfeng Su, Yingpeng Hu, Baohui Tan, Rui Wang, xinxin Hou. Research on Optimization of Stope Structure Parameters Based on DBO-MLP-SVM Algorithm Combined with Numerical Simulation[J]. Mining Research and Development, 2026 , 46 (6) : 32 -41 .
Year 2026 volume 46 Issue 6
35
9
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Article Info
  • Receive Date:2025-07-04
  • Online Date:2026-10-27
  • Published:2026-06-18
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  • Received:2025-07-04
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Affiliations
    1.School of Environment and Resources, Southwest University of Science and Technology, Mianyang, Sichuan 621010, China
    2.Jinchuan Group Co., Ltd., Jinchang, Gansu 737100, China
    3.Sichuan Zhuchuang Safety Technology Co., Ltd., Chengdu, Sichuan 610046, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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