收藏切换
Prediction of Peak Velocity of Blasting Vibration Based on SSA-BP
收藏切换
PDF
Pan-yun LI1, Wen-xue GAO1, Xiao-jun ZHANG1, Mao-lin HE1, Chen-yu GE2, Lin WANG2
Blasting | 2024, 41(3) : 205 - 211
Less
收藏切换
Blasting | 2024, 41(3): 205-211
BLASTING SAFETY
Prediction of Peak Velocity of Blasting Vibration Based on SSA-BP
Full
Pan-yun LI1, Wen-xue GAO1, Xiao-jun ZHANG1, Mao-lin HE1, Chen-yu GE2, Lin WANG2
Affiliations
  • 1.College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China
  • 2.Beijing Municipal Road and Bridge Co., Ltd., Beijing 100045, China
Published: 2024-09-01 doi: 10.3963/j.issn.1001-487X.2024.03.024
Outline
收藏切换

To accurately predict the peak particle velocity (PPV) and effectively reduce the hazards of blasting vibration, a prediction model was built by BP neural network based on the blasting project of Xingguang No. 1 openpit mine. Seven influencing factors as core distance, plugging length, minimum resistance line, explosives unit consumption, maximum single-hole charge, total extension time, and maximum single-delay charge, were selected as input variables, and the correlation between each factor and PPV was evaluated by using the grey correlation analysis method. The Sparrow Search Algorithm (SSA) optimized the BP neural network to predict the three-way peak vibration velocity. By comparing and analyzing the prediction results of the BP neural network model, the average errors of the prediction results of the SSA-BP neural network model were 6.08%, 7.34%, and 1.91%, respectively, and that of the prediction results of the BP neural network model was 22.19%, 54.01%, and 25.29%, respectively. The results show that the SSA-BP neural network model comprehensively considers the influence of multiple blasting design parameters on the peak vibration velocity. The sparrow search optimization algorithm can effectively solve the problem of the traditional BP neural network model, which quickly falls into the local optimum. The prediction results are more accurate, and the vibration velocity monitoring value is more consistent with smaller errors. Meanwhile, it can significantly shorten the learning and training time of the sample data to speed up the convergence speed of BP. Additionally, it can also significantly shorten the training time of sample data and accelerate the convergence speed of the BP neural network prediction model.

blasting vibration  /  open-pit mines  /  peak particle velocity prediction  /  BP neural network  /  SSA-BP neural network model
Pan-yun LI, Wen-xue GAO, Xiao-jun ZHANG, Mao-lin HE, Chen-yu GE, Lin WANG. Prediction of Peak Velocity of Blasting Vibration Based on SSA-BP[J]. Blasting, 2024 , 41 (3) : 205 -211 . DOI: 10.3963/j.issn.1001-487X.2024.03.024
  • Hubei Key Laboratory of Blasting Engineering Foundation(BL2021-23)
Year 2024 volume 41 Issue 3
PDF
80
33
Cite this Article
BibTeX
Article Info
doi: 10.3963/j.issn.1001-487X.2024.03.024
  • Receive Date:2023-08-20
  • Online Date:2026-03-20
  • Published:2024-09-01
Article Data
Affiliations
History
  • Received:2023-08-20
Funding
Hubei Key Laboratory of Blasting Engineering Foundation(BL2021-23)
Affiliations
    1.College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China
    2.Beijing Municipal Road and Bridge Co., Ltd., Beijing 100045, China

Corresponding:

GAO Wen-xue (1962-), male, professor, doctoral supervisor, mainly engaged in teaching and research of roadbed and tunnel engineering, (E-mail) .
References
Share
https://castjournals.cast.org.cn/joweb/bp/EN/10.3963/j.issn.1001-487X.2024.03.024
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT