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Rockburst Prediction Model with Support Vector Machine Optimized Based on Improved Salp Swarm Algorithm
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Rui TIAN1, Yanqing LI1, Zhanning LIU1, Chuangye WANG2, Shijiang CHEN2, Lilin CHEN2, Zhihong ZHANG3, Zhendong GUO3
Mining and Metallurgical Engineering | 2023, 43(2) : 5 - 9
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Mining and Metallurgical Engineering | 2023, 43(2): 5-9
• MINING •
Rockburst Prediction Model with Support Vector Machine Optimized Based on Improved Salp Swarm Algorithm
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Rui TIAN1, Yanqing LI1, Zhanning LIU1, Chuangye WANG2, Shijiang CHEN2, Lilin CHEN2, Zhihong ZHANG3, Zhendong GUO3
Affiliations
  • 1.Anyang Institute of Technology, Anyang 455000, Henan, China
  • 2.Inner Mongolia University of Science and Technology, Baotou 014010, Inner Mongolia, China
  • 3.Inner Mongolia Yitai Group Co Ltd, Ordos 017000, Inner Mongolia, China
Published: 2023-04-01 doi: 10.3969/j.issn.0253-6099.2023.02.002
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A database including 336 sets of engineering practical samples was established for rockburst prediction based on literature research. An improved salp swarm algorithm (ISSA) was adopted to optimize the support vector machine (SVM), and then an ISSA-SVM model was constructed for predicting the rockburst intensity grade and its effectiveness was also verified. Results show that this ISSA-SVA model for rockburst prediction can have accuracy up to 94.0%, much higher than other model, which can provide a certain scientific basis for rockburst prevention and control.

rockburst  /  rockburst intensity  /  prediction model  /  salp swarm algorithm (SSA)  /  support vector machine (SVM)
Rui TIAN, Yanqing LI, Zhanning LIU, Chuangye WANG, Shijiang CHEN, Lilin CHEN, Zhihong ZHANG, Zhendong GUO. Rockburst Prediction Model with Support Vector Machine Optimized Based on Improved Salp Swarm Algorithm[J]. Mining and Metallurgical Engineering, 2023 , 43 (2) : 5 -9 . DOI: 10.3969/j.issn.0253-6099.2023.02.002
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Article Info
doi: 10.3969/j.issn.0253-6099.2023.02.002
  • Receive Date：2022-10-23
  • Online Date：2026-03-05
  • Published：2023-04-01
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  • Received：2022-10-23
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Affiliations
    1.Anyang Institute of Technology, Anyang 455000, Henan, China
    2.Inner Mongolia University of Science and Technology, Baotou 014010, Inner Mongolia, China
    3.Inner Mongolia Yitai Group Co Ltd, Ordos 017000, Inner Mongolia, China
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https://castjournals.cast.org.cn/joweb/kygczz/EN/10.3969/j.issn.0253-6099.2023.02.002
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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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