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Prediction of Mining Area Blasting Vibration Based on I-GWO-BP Neural Network
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Min XU1, Weixing LIN2, Lei SHI1, Renze OU2, Zhenjian YU1, Yongchao GONG2, Like HU3, Junsheng HU4
Mining Research and Development | 2025, 45(10) : 121 - 128
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Mining Research and Development | 2025, 45(10): 121-128
Prediction of Mining Area Blasting Vibration Based on I-GWO-BP Neural Network
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Min XU1, Weixing LIN2, Lei SHI1, Renze OU2, Zhenjian YU1, Yongchao GONG2, Like HU3, Junsheng HU4
Affiliations
  • 1.Anhui Development Mining Co., Ltd., Lu’an, Anhui 237474, China
  • 2.Changsha Institute of Mining Research Co., Ltd., Changsha, Hunan 410012, China
  • 3.Minmetals Mining Holdings Limited, Hefei, Anhui 230091, China
  • 4.Anshan Chentaigou Mining Co., Ltd., China Minmetals Corporation, Anshan, Liaoning 114051, China
Published: 2025-10-25
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Aiming at the problem of low prediction accuracy of existing blasting vibration velocity prediction formulas in complex ground environments, a BP neural network model based on improved grey wolf optimization (I-GWO) Algorithm was proposed. The grey wolf algorithm was improved by changing the convergence factor function of the neural network to enhance optimization accuracy, initializing the wolf pack position through chaotic mapping to accelerate solution speed, and dynamically adjusting weights based on step size Euclidean distance to improve optimization efficiency. Based on the monitoring data of blasting vibration velocity at the Lilou-Wuji Iron Mine, the I-GWO-BP model was established by selecting the blast center distance, the maximum single-stage charge amount, and total charge amount as input parameters. The results show that the convergence speed and accuracy of the I-GWO-BP model are better than those of the GWO-BP model and BP model, and the optimization effect is significant. The predicted values of the I-GWO-BP model are basically within the confidence band of the measured values ±0.08 cm/s, with an average absolute percentage error of 13.84%. Its prediction performance is significantly better than other prediction methods, and its prediction accuracy is high. The research results can provide some reference for predicting the blasting vibration velocity in mines.

Blasting vibration velocity  /  BP neural network  /  Improved grey wolf optimization algorithm  /  Predictive model  /  Prediction accuracy
Min XU, Weixing LIN, Lei SHI, Renze OU, Zhenjian YU, Yongchao GONG, Like HU, Junsheng HU. Prediction of Mining Area Blasting Vibration Based on I-GWO-BP Neural Network[J]. Mining Research and Development, 2025 , 45 (10) : 121 -128 .
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Year 2025 volume 45 Issue 10
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Article Info
  • Receive Date：2024-11-01
  • Online Date：2026-02-06
  • Published：2025-10-25
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  • Received：2024-11-01
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Affiliations
    1.Anhui Development Mining Co., Ltd., Lu’an, Anhui 237474, China
    2.Changsha Institute of Mining Research Co., Ltd., Changsha, Hunan 410012, China
    3.Minmetals Mining Holdings Limited, Hefei, Anhui 230091, China
    4.Anshan Chentaigou Mining Co., Ltd., China Minmetals Corporation, Anshan, Liaoning 114051, 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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