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Study on Mechanical and Rheological Properties of Gobi Sand-Tailings Synergistic Cemented Filling Body in a Mine and Optimization of Neural Network Prediction Model
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Jie FAN1, 2, Ke JIANG1, 2, 3, Shuo LIU3, Lei HE4, Liang PENG1, 2, Weiqing MA4, Weipeng LYU1, 2, Wei LIU1, 2
Mining Research and Development | 2025, 45(12) : 83 - 90
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Mining Research and Development | 2025, 45(12): 83-90
Study on Mechanical and Rheological Properties of Gobi Sand-Tailings Synergistic Cemented Filling Body in a Mine and Optimization of Neural Network Prediction Model
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Jie FAN1, 2, Ke JIANG1, 2, 3, Shuo LIU3, Lei HE4, Liang PENG1, 2, Weiqing MA4, Weipeng LYU1, 2, Wei LIU1, 2
Affiliations
  • 1.Changsha Institute of Mining Research Co., Ltd., Changsha, Hunan 410012, China
  • 2.State Key Laboratory of Safety Technology for Metal Mines, Changsha, Hunan 410012, China
  • 3.School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan, Hubei 430070, China
  • 4.China Huaye Group Co., Ltd., Beijing 100176, China
Published: 2025-12-25
Outline
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Aiming at the problem of high filling cost and poor process stability caused by insufficient tailings production in a mine, the mechanical and rheological properties of Gobi sand-tailings synergistic filling body were studied. The effects of slurry mass concentration (74%, 76%, 78%), cement-sand ratio (1∶6, 1∶8, 1∶10, 1∶12), Gobi sand content (0, 10%, 20%, 30%) and curing age (3 d, 7 d, 14 d, 28 d) on the compressive strength of filling body and the transport resistance of filling slurry were discussed. Based on the MATLAB platform, the Random Forest (RF) and Convolutional Neural Network (CNN) models were constructed to predict the compressive strength of the filling body. The results show that the uniaxial compressive strength of filling body increases with the increase of slurry mass concentration, curing age and cement-sand ratio, and increases first and then tends to be stable with the increase of Gobi sand content. The transport resistance of filling slurry increases with the increase of slurry mass concentration, and decreases first and then increases with the increase of Gobi sand content. According to the uniaxial compressive strength of the filling body and the slurry transport resistance, the optimal content of Gobi sand is 20%. In the neural network model, the prediction accuracy of the CNN model is better than that of the RF model, determination coefficient R2=0.970 34, the average absolute error MAE=0.101 81, and the error between the predicted value and the experimental value is 0-10.53%. The research results can provide reference for the collaborative utilization of low-grade aggregate and the reduction of filling cost in the same type of mines.

Gobi sand-tailings synergistic cemented filling body  /  Mechanical property  /  Transport resistance  /  Neural network  /  Predictive analysis
凡杰, 江科, 刘硕, 何磊, 彭亮, 马维清, 吕伟鹏, 刘伟. 某矿戈壁砂–尾砂协同胶结充填体力学与流变性能研究及神经网络预测模型优化. 矿业研究与开发, 2025 , 45 (12) : 83 -90 .
Jie FAN, Ke JIANG, Shuo LIU, Lei HE, Liang PENG, Weiqing MA, Weipeng LYU, Wei LIU. Study on Mechanical and Rheological Properties of Gobi Sand-Tailings Synergistic Cemented Filling Body in a Mine and Optimization of Neural Network Prediction Model[J]. Mining Research and Development, 2025 , 45 (12) : 83 -90 .
Year 2025 volume 45 Issue 12
53
27
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Article Info
  • Receive Date:2024-12-20
  • Online Date:2026-10-28
  • Published:2025-12-25
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  • Received:2024-12-20
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Affiliations
    1.Changsha Institute of Mining Research Co., Ltd., Changsha, Hunan 410012, China
    2.State Key Laboratory of Safety Technology for Metal Mines, Changsha, Hunan 410012, China
    3.School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan, Hubei 430070, China
    4.China Huaye Group Co., Ltd., Beijing 100176, 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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