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Prediction of Blasting Vibration Velocity in Open-pit Mine based on MD-PCA-BP Model
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Mo-xi ZHAO1, Yu-min YANG1, Chuan-bo ZHOU1, Sheng ZHANG2, Wen-zhong CHEN2, Mao-sen YANG3, Yu-qi ZHANG1
Blasting | 2024, 41(2) : 203 - 211
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Blasting | 2024, 41(2): 203-211
BLASTING SAFETY
Prediction of Blasting Vibration Velocity in Open-pit Mine based on MD-PCA-BP Model
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Mo-xi ZHAO1, Yu-min YANG1, Chuan-bo ZHOU1, Sheng ZHANG2, Wen-zhong CHEN2, Mao-sen YANG3, Yu-qi ZHANG1
Affiliations
  • 1.College of Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
  • 2.Inner Mongolia Shengli Zhongwei Blasting Co., Ltd., Ordos 010300, China
  • 3.Inner Mongolia Autonomous Region Public Security Department Security Management Corps, Hohhot 010051, China
Published: 2024-06-01 doi: 10.3963/j.issn.1001-487X.2024.02.025
Outline
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In order to address the problem of predicting blasting vibration in complex geological conditions at open-pit mines, an improved BP neural network prediction model based on Mahalanobis distance discrimination (MD) and principal component analysis (PCA), namely MD-PCA-BP model, is proposed. By combining the monitoring data of blasting vibration at Changtan open-pit mine in Inner Mongolia, outliers in the monitoring data are eliminated using the Mahalanobis distance discrimination method. Then, the principal component analysis method is employed to reduce the dimensionality of factors affecting blasting vibration and obtain three principal component factors. The scores of each principal component factor are calculated, and finally a nonlinear relationship between blasting vibration and principal component scores is constructed through BP neural network to establish the prediction model based on MD-PCA-BP. The results show that the fitting degree between predicted values and measured values of blasting vibration velocity prediction model established based on MD-PCA-BP reaches 0.94, indicating high prediction accuracy of this model. When compared with Sadovsky empirical formula, two improved elevation empirical formulas, MD-BP model, PCA-BP model, and BP model, most of the prediction errors of MD-PCA-BP model are within 10%, demonstrating higher reliability and accuracy compared to empirical formulas and unimproved BP prediction models. The blast vibration prediction model based on MD-PCA-BP exhibits good predictive performance for blast vibration velocity in complex terrains.

open-pit mines  /  blasting vibration  /  Mahalanobis distance  /  principal component analysis  /  BP neural network model
Mo-xi ZHAO, Yu-min YANG, Chuan-bo ZHOU, Sheng ZHANG, Wen-zhong CHEN, Mao-sen YANG, Yu-qi ZHANG. Prediction of Blasting Vibration Velocity in Open-pit Mine based on MD-PCA-BP Model[J]. Blasting, 2024 , 41 (2) : 203 -211 . DOI: 10.3963/j.issn.1001-487X.2024.02.025
  • National Natural Science Foundation of China(41972286)
Year 2024 volume 41 Issue 2
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Article Info
doi: 10.3963/j.issn.1001-487X.2024.02.025
  • Receive Date:2023-11-06
  • Online Date:2026-03-20
  • Published:2024-06-01
Article Data
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History
  • Received:2023-11-06
Funding
National Natural Science Foundation of China(41972286)
Affiliations
    1.College of Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
    2.Inner Mongolia Shengli Zhongwei Blasting Co., Ltd., Ordos 010300, China
    3.Inner Mongolia Autonomous Region Public Security Department Security Management Corps, Hohhot 010051, China

Corresponding:

ZHOU Chuan-bo (1963-), male, Anhui, professor, mainly engaged in research on geotechnical engineering and engineering blasting, (E-mail) .
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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
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