Prediction of Rock Burst Intensity Based on the ISCSO-KELM Model

LEI Xueliang; ZHOU Zonghong; LIU Jian; FENG Zhansuo; JING Mingqiang

doi:10.11858/gywlxb.20240913

Volume 39 Issue 8

Aug 2025

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Chinese Journal of High Pressure Physics > 2025 > 39(8): 085303.

LEI Xueliang, ZHOU Zonghong, LIU Jian, FENG Zhansuo, JING Mingqiang. Prediction of Rock Burst Intensity Based on the ISCSO-KELM Model[J]. Chinese Journal of High Pressure Physics, 2025, 39(8): 085303. doi: 10.11858/gywlxb.20240913

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LEI Xueliang, ZHOU Zonghong, LIU Jian, FENG Zhansuo, JING Mingqiang. Prediction of Rock Burst Intensity Based on the ISCSO-KELM Model[J]. Chinese Journal of High Pressure Physics, 2025, 39(8): 085303. doi: 10.11858/gywlxb.20240913

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Prediction of Rock Burst Intensity Based on the ISCSO-KELM Model

doi: 10.11858/gywlxb.20240913

1.
School of Land, Resources and Engineering, Kunming University of Science and Technology, Kunming 650093, Yunnan, China
2.
Yunnan Yun Tian Hua Ju Lin New Material Co., Ltd., Zhaotong 657000, Yunnan, China

Received Date: 17 Oct 2024
Rev Recd Date: 24 Nov 2024
Accepted Date: 24 Nov 2024
Issue Publish Date: 05 Aug 2025

Abstract

Abstract

In order to reduce the occurrence of rock burst accidents during construction, the rock burst intensity should be assessed. In this paper, we propose a new rock burst prediction model based on the improved sandcat swam optimization-kernel based extreme learning machhine (ISCSO-KELM) algorithm. The maximum tangential stress, uniaxial compressive strength, uniaxial tensile strength and rock elastic energy index were selected as the evaluation indexes of rock burst. 105 domestic and international examples of rock burst were selected as samples for machine learning. Comparison of the relative ratios of the model presented herein with confusion matrix predicted by models including random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) and kernel based extreme learning machhine (KELM) models shows that, the ISCSO-KELM model is superior at assessing both evaluation accuracy and recall. The evaluation accuracy of the model reached 96.774 2%, indicating the superiority of ISCSO-KELM. Relevant engineering cases were used to verify the rock burst intensity. The results show that ISCSO-KELM model is more effective in capturing the connection between rock burst intensity and the indexes, thus providing a new highly applicable method for rock burst prediction.
- nuclear limit learning machine,
- sand cat group optimization algorithm,
- confusion matrix,
- rock burst prediction

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References(23)

References

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Prediction of Rock Burst Intensity Based on the ISCSO-KELM Model

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