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Research on Explosive-Rock Matching System based on XGBoost
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Xue-jiao CUI1, 2, Qi-yue LI1, Ming TAO1, Zhi-xian HONG1, Ming-sheng ZHAO2, 3, Jie LI2, Jian-min ZHOU2, Hong-bing YU2
Blasting | 2023, 40(3) : 31 - 38
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Blasting | 2023, 40(3): 31-38
THEORETICAL AND TECHNOLOGICAL EXPLORATION
Research on Explosive-Rock Matching System based on XGBoost
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Xue-jiao CUI1, 2, Qi-yue LI1, Ming TAO1, Zhi-xian HONG1, Ming-sheng ZHAO2, 3, Jie LI2, Jian-min ZHOU2, Hong-bing YU2
Affiliations
  • 1.School of Resources and Safety Engineering, Central South University, Changsha 410083, China
  • 2.Poly Xianlian Blasting Engineer Limited Corp, Guiyang 550002, China
  • 3.Mining Institute, Guizhou University, Guiyang 550025, China
Published: 2023-09-01 doi: 10.3963/j.issn.1001-487X.2023.03.005
Outline
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In modern blasting engineering research, the matching model of explosive and rock provides a scientific basis for revealing the internal mechanism of blasting process and predicting the economic benefits of blasting system, which has become an irreplaceable important tool. However, due to the diversity and complexity of soil-rock medium and the uncertainty of explosion process, the interaction between explosive and rock is more complex and uncertain, and it is difficult to study the matching of explosive and rock from their interaction process. Earlier studies mainly relied on empirical formulas and field tests for calculation and summary, which often had high eigenvalues and harsh application environment. However, the feature of machine learning is that it only considers the beginning and the result, and does not care about the middle process, which ensures its universality in the study of explosive-rock matching model. The XGBoost algorithm, together with multi-threading, data compression and fragmentation method, has the advantages of high efficiency in the case of largedata amount, and is suitable for training of a large amount of field data. In view of this, a field test was carried out in a mine in Guizhou province, and XGBoost algorithm was used to establish a matching system between explosives and rocks. The network was trained through successful examples, and the trained neural network was applied to practical projects. The results show that the performance of the explosives selected by the matching system based on this method is similar to that of the industrial explosives used at present, and the error is within±10%, which has a high reliability, and further verifies the rationality of the explosiverock matching system based on XGBoost algorithm.

mixed explosive  /  XGBoost algorithm  /  match model  /  small sample prediction
Xue-jiao CUI, Qi-yue LI, Ming TAO, Zhi-xian HONG, Ming-sheng ZHAO, Jie LI, Jian-min ZHOU, Hong-bing YU. Research on Explosive-Rock Matching System based on XGBoost[J]. Blasting, 2023 , 40 (3) : 31 -38 . DOI: 10.3963/j.issn.1001-487X.2023.03.005
  • National Natural Science Foundation of China(52064003)
  • Guizhou Province Science and Technology Platform and Talent Team Construction Project(黔科合平台人才〔2020〕5019)
  • Guizhou Provincial Program on Commercialization of Scientific and Technological Achievements(黔科合成果〔2020〕2Y049)
  • Guizhou Provincial Department of Science and Technology Central Guidance Local Science and Technology Development Fund Project(黔科中引地方〔2021〕4004)
Year 2023 volume 40 Issue 3
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Article Info
doi: 10.3963/j.issn.1001-487X.2023.03.005
  • Receive Date:2023-07-03
  • Online Date:2026-03-20
  • Published:2023-09-01
Article Data
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History
  • Received:2023-07-03
Funding
National Natural Science Foundation of China(52064003)
Guizhou Province Science and Technology Platform and Talent Team Construction Project(黔科合平台人才〔2020〕5019)
Guizhou Provincial Program on Commercialization of Scientific and Technological Achievements(黔科合成果〔2020〕2Y049)
Guizhou Provincial Department of Science and Technology Central Guidance Local Science and Technology Development Fund Project(黔科中引地方〔2021〕4004)
Affiliations
    1.School of Resources and Safety Engineering, Central South University, Changsha 410083, China
    2.Poly Xianlian Blasting Engineer Limited Corp, Guiyang 550002, China
    3.Mining Institute, Guizhou University, Guiyang 550025, China
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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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