Rockburst failure time prediction based on a fuzzy comprehensive evaluation method using the acoustic emission

Abstract

Rockbursts have become one of the most serious disasters in underground engineering around the world, which seriously threaten the construction safety of underground engineering. The effective prediction of rockbursts is of great significance for the safe production management of deep engineering. In this study, the uniaxial compression tests were carried out on sandstone and granite specimens with different shapes and sizes. A multi-index fuzzy comprehensive evaluation model was established based on the acoustic emission (AE) characteristic parameters to quantitatively evaluate the possibility of rock failure. In the fuzzy comprehensive evaluation model, the exponential distribution function in reliability theory was introduced, and the membership function was constructed by Gaussian distribution. The analytic hierarchy process (AHP) and entropy weight method (EWM) were utilized to determine the subjective and objective weights of each index respectively, and the distance function was employed to obtain the synthesized weight. Thereafter, the comprehensive prediction results were obtained by variable fuzzy pattern recognition (VFPR). The results show that for both sandstone and granite specimens with different shapes and sizes, the time advance (Δt) of rock failure forecasting is in the range of 145-491 ​s, and the forecasting point is 0.761-0.889 of the total loading time of rock failure. The prediction results are mainly affected by lithology, while the impact of rock shape and size is relatively insignificant. The sensitivity of fuzzy comprehensive evaluation index is: granite ​> ​sandstone. This research can provide a useful reference for the prediction of rockburst.

Keywords

Rockburst forecasting / Fuzzy comprehensive evaluation / Acoustic emission / Analytic hierarchy process / Entropy weight method

CRediT authorship contribution statement

Yuantao Wen: Writing - original draft, Formal analysis. Fanzhen Meng: Conceptualization. Pengyuan Liu: Visualization. Zhiyuan Li: Visualization. Qijin Cai: Methodology. Feili Wang: Funding acquisition. Jie Liu: Writing - review & editing.

Declaration of competing interests

The author declares no conflicts of interest or competitive interests related to this study.

Acknowledgments

We gratefully acknowledge financial support from Taishan Scholars Program (Nos.2019KJG002 and 2019RKB01083) and the National Natural Science Foundation of China (Nos. 51879135 and 52309130). This work is also supported by Open Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering Safety (No. SKLGME023003) and Natural Science Foundation of Shandong Province (No. ZR2022QD004).

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