Home Journals Articles Updates About
CN
image
收藏切换
Prediction of Blast-Induced Rock Fragmentation Based on ACO-BP Model
收藏切换
PDF
Shasha CHEN1, 2, Li HE1, 2, 3, Tengfei LI1, 2, Xinyue ZHANG1, 2, Sheng PENG4, Yinkang YAO3, Changbang LIU5, Jiangwei CHEN6
Mining and Metallurgical Engineering | 2024, 44(5) : 12 - 16
Less
收藏切换
Mining and Metallurgical Engineering | 2024, 44(5): 12-16
• MINING •
Prediction of Blast-Induced Rock Fragmentation Based on ACO-BP Model
Full
Shasha CHEN1, 2, Li HE1, 2, 3, Tengfei LI1, 2, Xinyue ZHANG1, 2, Sheng PENG4, Yinkang YAO3, Changbang LIU5, Jiangwei CHEN6
Affiliations
  • 1.Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan 430065, Hubei, China
  • 2.Hubei Provincial Intelligent Blasting Engineering Technology Center, Wuhan 430065, Hubei, China
  • 3.Hubei Provincial Key Laboratory of Blasting Engineering, Jianghan University, Wuhan 430056, Hubei, China
  • 4.School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, Hubei, China
  • 5.Wuhan Explosion & Blasting Co., Ltd., Wuhan 430056, Hubei, China
  • 6.China Construction Seventh Engineering Division Co., Ltd., Zhengzhou 450004, Henan, China
Published: 2024-10-01 doi: 10.3969/j.issn.0253-6099.2024.05.003
Outline
收藏切换

In order to effectively predict blast-induced rock fragmentation, a distribution of normalized rock fragmentation under different conditions was obtained by performing a designed experiment on drilling and blasting of a concrete specimen, and then the rock fragmentation exceeding 40 mm was selected for study. The correlation among variables under different testing conditions was analyzed by using Spearman correlation statistics, and the initial weights and thresholds of the BP neural network were optimized by using the ant colony optimization (ACO) to construct an ACO-BP model. The model was then trained with rock fragmentation by on-site blasting, and tested. Based on the comparison of such prediction mode with BP neural network model, random forest (RF) model and extreme gradient boosting (XGboost) model, it is found that the ACO-BP model is highly reliable in predicting blast-induced rock fragmentation, presenting a root mean square error of 0.13, an average absolute error of 0.11, and a coefficient of determination of 0.92. It is concluded that this model, with higher accuracy in prediction and applicability, can accurately predict blast-induced rock fragmentation.

rock blasting  /  rock fragmentation  /  model test  /  fragmentation predication  /  ACO-BP model
Shasha CHEN, Li HE, Tengfei LI, Xinyue ZHANG, Sheng PENG, Yinkang YAO, Changbang LIU, Jiangwei CHEN. Prediction of Blast-Induced Rock Fragmentation Based on ACO-BP Model[J]. Mining and Metallurgical Engineering, 2024 , 44 (5) : 12 -16 . DOI: 10.3969/j.issn.0253-6099.2024.05.003
Appendix
Less
Year 2024 volume 44 Issue 5
PDF
60
27
Cite this Article
BibTeX
Article Info
doi: 10.3969/j.issn.0253-6099.2024.05.003
  • Receive Date：2024-05-10
  • Online Date：2026-03-17
  • Published：2024-10-01
Article Data
Affiliations
History
  • Received：2024-05-10
Funding
Affiliations
    1.Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan 430065, Hubei, China
    2.Hubei Provincial Intelligent Blasting Engineering Technology Center, Wuhan 430065, Hubei, China
    3.Hubei Provincial Key Laboratory of Blasting Engineering, Jianghan University, Wuhan 430056, Hubei, China
    4.School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, Hubei, China
    5.Wuhan Explosion & Blasting Co., Ltd., Wuhan 430056, Hubei, China
    6.China Construction Seventh Engineering Division Co., Ltd., Zhengzhou 450004, Henan, China
References
Share
https://castjournals.cast.org.cn/joweb/kygczz/EN/10.3969/j.issn.0253-6099.2024.05.003
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
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
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House