收藏切换
Study on Prediction Formula of Peak Particle Velocity Induced by Open-pit Mine Blasting
收藏切换
PDF
Lei SHAO1, Mei ZHANG2, Ding DENG1, Lian-jun GUO1, Jiu-qing GAO3, Xin ZHAO3
Blasting | 2025, 42(2) : 178 - 181
Less
收藏切换
Blasting | 2025, 42(2): 178-181
BLASTING SAFETY
Study on Prediction Formula of Peak Particle Velocity Induced by Open-pit Mine Blasting
Full
Lei SHAO1, Mei ZHANG2, Ding DENG1, Lian-jun GUO1, Jiu-qing GAO3, Xin ZHAO3
Affiliations
  • 1.School of Architecture and Civil Engineering, Shenyang University of Technology, Shenyang 110870, China
  • 2.Xuanhua Vocational College of Science & Technology, Zhangjiakou 075100, China
  • 3.China Railway 19 Bureau Group Mining Investment Co., Ltd., Beijing 100161, China
Published: 2025-06-01 doi: 10.3963/j.issn.1001-487X.2025.02.021
Outline
收藏切换

To enhance the accuracy of blasting vibration predictions in an open-pit mine stripping project, a new peak particle velocity (PPV) prediction formula is proposed, incorporating geological elevation differences and slope effects. Based on the principles of dimensional analysis, the traditional Sadovsky formula was modified by introducing the elevation difference (H) and slope coefficient (γ), resulting in a new prediction model (Formula 11). Notably, when H=0, the new formula reverts to the traditional Sadovsky formula, ensuring its reliability. A field vibration monitoring test was conducted in the mine, with 5 monitoring points at elevation differences of 0.222 m, 0.176 m, 0.865 m, 1.617 m, and 2.465 m. Using the TC-4850 blasting vibration meter, vibration data were recorded, and multiple predictions, including the Sadovsky and the newly proposed formula, were fitted using multivariate nonlinear regression. Results show that the proposed formula achieves the highest correlation coefficient (R2=0.905), surpassing other models. Furthermore, the new formula exhibits improved prediction accuracy, with a maximum relative error of 20.85% and an average error of 8.11%, compared to 24.89% and 10.31% for the original Sadovsky formula. By considering the factors of elevation and slope, the proposed prediction formula significantly improves the precision of PPV predictions under complex terrain conditions, providing a scientific basis for blasting vibration control and safety management. Applying the specific scheme and data proves the effectiveness and practicality of the formula.

open pit mine  /  blasting vibration  /  dimensional analysis  /  peak vibration velocity  /  Sadovsky formula
Lei SHAO, Mei ZHANG, Ding DENG, Lian-jun GUO, Jiu-qing GAO, Xin ZHAO. Study on Prediction Formula of Peak Particle Velocity Induced by Open-pit Mine Blasting[J]. Blasting, 2025 , 42 (2) : 178 -181 . DOI: 10.3963/j.issn.1001-487X.2025.02.021
  • Supported by the National Natural Science Foundation of China(51974187)
  • Study on the Energy Dissipation Mechanism of Rock Breakage under Impact Based on Microscopic Features
Year 2025 volume 42 Issue 2
PDF
131
62
Cite this Article
BibTeX
Article Info
doi: 10.3963/j.issn.1001-487X.2025.02.021
  • Receive Date:2024-10-31
  • Online Date:2026-03-19
  • Published:2025-06-01
Article Data
Affiliations
History
  • Received:2024-10-31
Funding
Supported by the National Natural Science Foundation of China(51974187)
Study on the Energy Dissipation Mechanism of Rock Breakage under Impact Based on Microscopic Features
Affiliations
    1.School of Architecture and Civil Engineering, Shenyang University of Technology, Shenyang 110870, China
    2.Xuanhua Vocational College of Science & Technology, Zhangjiakou 075100, China
    3.China Railway 19 Bureau Group Mining Investment Co., Ltd., Beijing 100161, China

Corresponding:

GUO Lian-jun (1963-), male, born in Beipiao city, Liaoning province, professor, doctoral supervisor, Ph. D, mainly engaged in research on mining engineering and blasting theory technology, (E-mail) .
References
Share
https://castjournals.cast.org.cn/joweb/bp/EN/10.3963/j.issn.1001-487X.2025.02.021
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