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Blasting Fragmentation Prediction based on PSO-BPNN Model
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Ying LIU1, Yu MAO1, Shi-chao XU1, Bin LI1, Hong ZHANG1, Yun GU2, Ji-kui ZHANG2, Nan JIANG2
Blasting | 2024, 41(2) : 136 - 142
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Blasting | 2024, 41(2): 136-142
• BLASTING IN ORE AND ROCK •
Blasting Fragmentation Prediction based on PSO-BPNN Model
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Ying LIU1, Yu MAO1, Shi-chao XU1, Bin LI1, Hong ZHANG1, Yun GU2, Ji-kui ZHANG2, Nan JIANG2
Affiliations
  • 1.State Grid Xinyuan Shanxi Datong Pumped Storage Power Company Limited, Datong 037000, China
  • 2.Nuclear Industry Nanjing Construction Group Co., Ltd., Nanjing 210000, China
Published: 2024-06-01 doi: 10.3963/j.issn.1001-487X.2024.02.017
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The impact of fragmentation size and gradation on the stability and permeability of rockfill in hydraulic engineering is of great significance. Accurate prediction of fragmentation size has become a key focus in rock blasting research. In this study, a PSO-BPNN model is developed based on the Backpropagation Neural Networks (BPNN) with optimized network weights and biases using the Particle Swarm Optimization (PSO) algorithm. The model is trained and tested using representative blasting data, and its reliability and applicability are validated through its application in the Hunyuan Pumped Storage Power Station project in Shanxi. Results demonstrate that the PSO-BPNN model exhibits short computation time and high reliability for predicting fragmentation size, with a maximum relative error between the model output and actual average fragmentation size of 6.56%. Therefore, this model demonstrates high predictive accuracy and applicability, providing precise guidance for construction of rock-fill dams at the Hunyuan Pumped Storage Power Station in Shanxi province.

blasting fragmentation  /  PSO-BPNN model  /  model prediction  /  engineering application
Ying LIU, Yu MAO, Shi-chao XU, Bin LI, Hong ZHANG, Yun GU, Ji-kui ZHANG, Nan JIANG. Blasting Fragmentation Prediction based on PSO-BPNN Model[J]. Blasting, 2024 , 41 (2) : 136 -142 . DOI: 10.3963/j.issn.1001-487X.2024.02.017
Funding
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  • The Science and Technology Project of State Grid Xinyuan Group Co., Ltd.(SGXYKJ-2021-143)
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Year 2024 volume 41 Issue 2
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Article Info
doi: 10.3963/j.issn.1001-487X.2024.02.017
  • Receive Date：2023-09-06
  • Online Date：2026-03-20
  • Published：2024-06-01
Article Data
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History
  • Received：2023-09-06
Funding
The Science and Technology Project of State Grid Xinyuan Group Co., Ltd.(SGXYKJ-2021-143)
Affiliations
    1.State Grid Xinyuan Shanxi Datong Pumped Storage Power Company Limited, Datong 037000, China
    2.Nuclear Industry Nanjing Construction Group Co., Ltd., Nanjing 210000, China

Corresponding:

JIANG Nan (1986-), Male, associate professor, Ph. D, mainly engaged in engineering blasting, rock dynamics research, (E-mail) .
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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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