A New Method to Assess Thick, Hard Roof-Induced Rock Burst Risk Based on Mining Speed Effect on Key Energy Strata

Li, Wenlong; Tu, Shihao; Tu, Hongsheng; Liu, Xun; Miao, Kaijun; Zhao, Hongbin; Ma, Jieyang; Tang, Long; Li, Yan

doi:10.3390/su142215054

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…If the mining speed is too fast, the disturbance to the coal strata will increase, resulting in a small scope of action of the bearing pressure-boosting section, a high peak value and a close distance to the coal wall. The possibility of forming stress concentration will increase, increasing the risk of dynamic disaster [44]. As a consequence, the risk of dynamic disasters will increase due to the possibility of stress concentration.…”

Section: Mining Technology Factorsmentioning

confidence: 99%

Risk Assessment of Compound Dynamic Disaster Based on AHP-EWM

Zhang,

Tian,

Wang

et al. 2023

Applied Sciences

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The coal mine in deep mining can easily form a compound dynamic disaster with the characteristics of rock burst and gas outburst. In this paper, the analytic hierarchy process (AHP) and the entropy weight method (EWM) are combined, and the fuzzy comprehensive evaluation (FCE) secondary evaluation model of compound dynamic disaster is proposed to evaluate the risk of compound dynamic disaster, which avoids the problems of the imperfect evaluation index system and strong subjectivity of index weight. Based on the statistical analysis of typical compound dynamic disaster cases in China, three first-level indicators were established, and sixteen second-level indicators were developed. The verification results show that the accuracy and weight are better than the traditional evaluation methods. Combined with geological and mining data, the compound dynamic disaster risk assessment was carried out on the second mining area of mine B, in the Pingdingshan mining area, and the result was grade II (weak risk). Corresponding prevention measures and parameters were implemented, and no compound dynamic disaster occurred during the working face excavation.

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Section: Mining Technology Factorsmentioning

confidence: 99%

Risk Assessment of Compound Dynamic Disaster Based on AHP-EWM

Zhang,

Tian,

Wang

et al. 2023

Applied Sciences

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“…11 coal mine [26] Compound roof 2 Shangwan mine [27] Cracked roof under hallow mining conditions 3 Jinchuan nickel mine [28] Nickel mine roof 5 Xinjulong coal mine [29] Deep coal mine roadway roof located in fault zone 5 Gaohe coal mine [30] Paste false roof 9 Great Wall #3 coal mine [31] Thin roof 2 He caogou coal mine [32] Longwall coal mining 5 Sanshandao gold mine [33] Undersea roof 3 No. 2130 coal mine [34] Multi-layer gangue roof 11 Boertai coal mine [35] Thick and hard roof 34 Sheilei coal mine [36] Hard roof 4 Yuwu coal mine [37] Compound roof 5 Gaokeng coal mine [38] Compound roof 4 Lishi coal mine [39] Extra-thick compound mudstone roof 8 Dongtan coal mine [40] Thick and hard roof 7 Sangshuping coal mine [41] Hard roof 7 Zhangshuanglou coal mine [42] Hard roof 5 Yungaishan coal mine [43] Hard roof 9 Zhuxianzhuang coal mine [44] Hard thick roof 7 a coal mine in Guizhou [45] Compound roof 12 Pingdingshan No. 2 coal mine [46] Hard roof 1 Linsheng coal mine [47] Hard roof 10 Sheilei coal mine [48] Compound roof 4 Dianping coal mine [49] Hard and thick roof 5…”

Section: Name Of Coal Mine Roof Geological Condition Sample Data Sizementioning

confidence: 99%

Identification Method of Optimal Copula Correlation Characteristic for Geological Parameters of Roof Structure

Cao,

Wang,

Zhu

et al. 2023

Sustainability

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Limited by the actual investigation of coal mine engineering, the measured data obtained are often based on small sample characteristics. How to probabilistically de-integrate the prior information to obtain meaningful statistical values has received increasing attention from geotechnical engineers. In this study, an optimal copula function identification method for multidimensional geotechnical structures of coal mine roofs under the Bayesian approach is proposed. Firstly, the characterization method of multidimensional roof parameter correlation structures is proposed based on copula theory, and 167 sets of measured data from 24 coal mines at home and abroad are collected to study the measured identification results using the Bayesian method. Secondly, Monte Carlo simulation is utilized to compare the correct recognition rates of the commonly used AIC criterion and the Bayesian approach under different correlation structures. Finally, the influencing factors affecting the successful recognition rate of the Bayesian approach are analyzed. The results show that compared with the traditional AIC criterion, the Bayesian approach has more marked advantages in correctly recognizing the multidimensional parameter structures of roofs, and the number of measured samples, the strength of correlation coefficients, and the prior information have a major effect on the correct recognition rate of the optimal copula function under different real copula functions. In addition, the commonly used Gaussian copula has a better characterization effect in characterizing the multidimensional parameter correlation structure of the coal mine roofs, which can be prioritized to be used as a larger prior probability function in the evaluation process.

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