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| Prediction method for mining-induced deformation in coal mine roadways based on Bayesian updating |
| ZHANG Bin1, JIA Haibin1, LI Atao2, WANG Huaiyuan3, CHEN Yuchuan1, YU Wanquan1, QIN Changkun4*
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1. ShandongXinjulong Energy Co., Ltd., Heze 274918, Shandong, China;
2. Research Center for Rock Burst Prevention and Control of Shandong Energy Group Co., Ltd., Jinan 250014, Shandong, China;
3. Shandong Energy Group Luxi Mining Co., Ltd., Heze 274700, Shandong, China;
4. State Key Laboratory of Geomechanics and Geotechnical Engineering Safety, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, Hubei, China |
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Abstract Mining roadways were found to be susceptible to severe roof subsidence, large deformation, and structural failure under intense mining-induced disturbances. Accurately predicting deformation trends was considered crucial for ensuring mine safety and achieving effective surrounding rock control. Based on deformation monitoring data from mining faces at the Xin Julong Coal Mine, the deformation evolution characteristics of mining roadways were analyzed, and an empirical prediction model suitable for coal mining roadways was proposed. Considering that traditional empirical models were limited in capturing dynamic deformation behavior and parameter uncertainties, a Bayesian updating algorithm was introduced to construct a dynamically updated prediction model. By continuously adjusting the posterior distribution of model parameters with real-time monitoring data, prediction accuracy was improved, and uncertainty was reduced. Model validation was performed using monitoring data from working faces 6305 and 2305 of Xin Julong Coal Mine. It was indicated that, as data accumulated and Bayesian updating iterations proceeded, posterior parameter estimated stabilized, significantly enhancing prediction accuracy. The final predicted deformation values were found to closely match measured values, with determination coefficients(R2)exceeding 0.98 and root mean square errors(RMSE)significantly reduced. Additionally, extended validation was conducted using monitoring data from another coal mine's working face, further confirming the generalization capability and applicability of the proposed method under different geological conditions. The Bayesian updating prediction approach proposed in this research was demonstrated to effectively addresses the dynamic variations and parameter uncertainties in surrounding rock deformation, providing reliable data support for surrounding rock stability control in coal mine roadways.
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Published: 28 March 2025
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