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Velocity Prediction for Blast-Induced Vibration in Open-Pit Mine Based on Bi-LSTM Algorithm
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Wei ZHANG1, Bin NI1, Li WANG2, Wei XIE1, Shiyu WEI3
Mining and Metallurgical Engineering | 2025, 45(1) : 21 - 26
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Mining and Metallurgical Engineering | 2025, 45(1): 21-26
• MINING •
Velocity Prediction for Blast-Induced Vibration in Open-Pit Mine Based on Bi-LSTM Algorithm
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Wei ZHANG1, Bin NI1, Li WANG2, Wei XIE1, Shiyu WEI3
Affiliations
  • 1.China Nonferrous Metals Industry Xi'an Survey and Design Institute Co., Ltd., Xi'an 710000, Shaanxi, China
  • 2.Lanzhou Nonferrous Metallurgy Design and Research Institute Co., Ltd., Lanzhou 730000, Gansu, China
  • 3.School of Civil Engineering and Mapping & Surveying Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, Jiangxi, China
Published: 2025-02-01 doi: 10.3969/j.issn.0253-6099.2025.01.004
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The traditional formula for prediction of blast-induced vibration has low accuracy, thus a prediction model for blast-induced vibration velocity in open-pit mines was constructed based on bidirectional long-short-term memory network (Bi-LSTM). This model can process time series data in both directions while capturing the dependency between inputs of the past and future information at upper and lower layers and the outputs. From the monitoring data of blasting operation in Gaocun Iron Mine of Maanshan Iron and Steel Group, the relevant data were selected as the inputs, and the prediction results by Bi-LSTM were compared with those based on Sadaovsky formula. The results show that the blast-induced vibration velocity predicted based on Sadaovsky formula has a mean error of 26.87%, and the blast-induced vibration velocity predicted by Bi-LSTM algorithm has a mean error of 8.95%. It is shown that the Bi-LSTM model can have the prediction results in a high degree of agreement with the measured results. In the future, this Bi-LSTM model will be trained with the monitoring data of other mines to improve its generalization ability, and also will be implanted by transfering learning into a real-time safety monitoring and early warning platform for mines.

open-pit mine  /  blast-induced vibration  /  vibration velocity  /  prediction model  /  Bi-LSTM  /  deep learning algorithm
Wei ZHANG, Bin NI, Li WANG, Wei XIE, Shiyu WEI. Velocity Prediction for Blast-Induced Vibration in Open-Pit Mine Based on Bi-LSTM Algorithm[J]. Mining and Metallurgical Engineering, 2025 , 45 (1) : 21 -26 . DOI: 10.3969/j.issn.0253-6099.2025.01.004
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doi: 10.3969/j.issn.0253-6099.2025.01.004
  • Receive Date：2024-08-16
  • Online Date：2026-03-17
  • Published：2025-02-01
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  • Received：2024-08-16
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Affiliations
    1.China Nonferrous Metals Industry Xi'an Survey and Design Institute Co., Ltd., Xi'an 710000, Shaanxi, China
    2.Lanzhou Nonferrous Metallurgy Design and Research Institute Co., Ltd., Lanzhou 730000, Gansu, China
    3.School of Civil Engineering and Mapping & Surveying Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, Jiangxi, 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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