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Study on Measurement Method of Fragment Spatial Distribution by Adaptive Stratification of Blasting Pile
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Zhen-yang XU1, 2, Bao-qian HUAN1, Ping-feng LI3, Xue-song WANG4, Cheng-ping ZHOU3
Blasting | 2024, 41(1) : 27 - 36
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Blasting | 2024, 41(1): 27-36
• THEORETICAL AND TECHNOLOGICAL EXPLORATION •
Study on Measurement Method of Fragment Spatial Distribution by Adaptive Stratification of Blasting Pile
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Zhen-yang XU1, 2, Bao-qian HUAN1, Ping-feng LI3, Xue-song WANG4, Cheng-ping ZHOU3
Affiliations
  • 1.School of Mining Engineering, University of Science and Technology Liaoning, Anshan 114051, China
  • 2.Liaoning Engineering Technology Research Center for Efficient Mining and Utilization of Metal Mineral Resources, Anshan 114051, China
  • 3.Hongda Demolition Engineering Group Co., Guangzhou 510623, China
  • 4.School of Architecture and Civil Engineering, Shenyang University of Technology, Shenyang 110870, China
Published: 2024-03-01 doi: 10.3963/j.issn.1001-487X.2024.01.005
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The distribution characteristics of blasting pile is an important index indicator to evaluate blasting effect. In view of the inadequacy of the current direct and indirect methods of measuring fragment size of the blast pile, a spatial distribution measurement method for adaptive stratification of the blast pile is proposed. It uses the GA-LSSVM model to predict the shape parameters α and β of the Weibull function and sets multiple prediction points to predict the three-dimensional blasting pile morphology. By converting and fusing the parameters of the Kuz-Ram fragment prediction model, a distance prediction model of blast pile stratification is established and applied to the Weibull-GA-LSSVM model to achieve an automatic stratification of the blast pile. Through field application, the stratification design is continuously optimized for the best stratification position to realize the adaptive stratification. The results show that: (1) the Weibull-GA-LSSVM model can accurately predict the morphology of the blast pile with a good stability that the average relative error of the prediction results of the maximum forward distance of the blast pile is only 5.6% and the relative error of the prediction results of the looseness coefficient is mostly around 9%. (2) The Kuz-Ram-based blast pile stratification model can reasonably output the layer distance and number before blast, which ensures the shoveling efficiency after blast. (3) The optimal layer distance formula is derived to achieve the adaptive stratification of the blast pile, and the measurement accuracy of the fragment size distribution of the blast pile is significantly improved, which is closer to the overall fragment size distribution.

blast pile  /  adaptive stratification  /  fragment size distribution  /  layer distance  /  blasting effect
Zhen-yang XU, Bao-qian HUAN, Ping-feng LI, Xue-song WANG, Cheng-ping ZHOU. Study on Measurement Method of Fragment Spatial Distribution by Adaptive Stratification of Blasting Pile[J]. Blasting, 2024 , 41 (1) : 27 -36 . DOI: 10.3963/j.issn.1001-487X.2024.01.005
Funding
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  • National Natural Science Foundation of China(51974187)
  • Key Projects of Liaoning Provincial Department of Education(LJKZ0282)
Appendix
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Year 2024 volume 41 Issue 1
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84
36
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Article Info
doi: 10.3963/j.issn.1001-487X.2024.01.005
  • Receive Date：2023-08-24
  • Online Date：2026-03-20
  • Published：2024-03-01
Article Data
Affiliations
History
  • Received：2023-08-24
Funding
National Natural Science Foundation of China(51974187)
Key Projects of Liaoning Provincial Department of Education(LJKZ0282)
Affiliations
    1.School of Mining Engineering, University of Science and Technology Liaoning, Anshan 114051, China
    2.Liaoning Engineering Technology Research Center for Efficient Mining and Utilization of Metal Mineral Resources, Anshan 114051, China
    3.Hongda Demolition Engineering Group Co., Guangzhou 510623, China
    4.School of Architecture and Civil Engineering, Shenyang University of Technology, Shenyang 110870, China
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https://castjournals.cast.org.cn/joweb/bp/EN/10.3963/j.issn.1001-487X.2024.01.005
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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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