Study on the seismic damage and dynamic support of roadway surrounding rock based on reconstructive transverse and longitudinal waves

He, Shengquan; Shen, Feng; Chen, Tuo; Mitri, Hani; Ren, Ting; Song, Dazhao

doi:10.46690/ager.2023.09.04

Cited by 3 publications

(2 citation statements)

References 36 publications

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“…Within the realm of mining engineering, ensuring the safe extraction of coal stands as a paramount objective. Moreover, proactive measures 2 , 3 in early detection and scientific prevention of disasters arising from coal mining operations and engineering constructions form the cornerstone of coal mining practices.…”

Section: Introductionmentioning

confidence: 99%

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Classification of coal bursting liability of some chinese coals using machine learning methods

Wang,

Liu,

et al. 2024

Sci Rep

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The classification of coal bursting liability (CBL) is essential for the mitigation and management of coal bursts in mining operations. This study establishes an index system for CBL classification, incorporating dynamic fracture duration (DT), elastic strain energy index (WET), bursting energy index (KE), and uniaxial compressive strength (RC). Utilizing a dataset comprising 127 CBL measurement groups, the impacts of various optimization algorithms were assessed, and two prominent machine learning techniques, namely the back propagation neural network (BPNN) and the support vector machine (SVM), were employed to develop twelve distinct models. The models’ efficacy was evaluated based on accuracy, F1-score, Kappa coefficient, and sensitivity analysis. Among these, the Levenberg–Marquardt back propagation neural network (LM-BPNN) model was identified as superior, achieving an accuracy of 96.85%, F1-score of 0.9113, and Kappa coefficient of 0.9417. Further validation in Wudong Coal Mine and Yvwu Coal Industry confirmed the model, achieving 100% accuracy. These findings underscore the LM-BPNN model’s potential as a viable tool for enhancing coal burst prevention strategies in coal mining sectors.

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Section: Introductionmentioning

confidence: 99%

“…Subsequently, hybrid models were developed for comprehensive research in CBL. 3. By conducting a thorough comparison of accuracy, F1-score, and Kappa coefficient, alongside a sensitivity analysis of pertinent indicators, a refined optimization mechanism tailored to the CBL model was formulated.…”

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confidence: 99%

Classification of coal bursting liability of some chinese coals using machine learning methods

Wang,

Liu,

et al. 2024

Sci Rep

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Asymmetric Dynamic Support for Roadways in Subvertical Coal Seams

He,

Shen,

Chen

et al. 2024

Int. J. Geomech.

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A coal bursting liability evaluation model based on fuzzy set theory and analysis of three influencing factors

Wang,

Jin,

Liu

et al. 2024

Front. Earth Sci.

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The classification of coal bursting liability is of great significance for the prevention and control of rock burst. To address the shortcomings in existing bursting liability classification methods, a comprehensive evaluation model for bursting liability based on a combination of weighted-fuzzy set theory and three influencing factor analyses is proposed. The model selects four evaluation indicators: dynamic failure time (DT), elastic energy index (WET), bursting energy index (KE), and uniaxial compressive strength (RC). Two types of membership functions, trapezoidal fuzzy numbers (TFN) and Gaussian fuzzy numbers (GFN), are used to quantitatively describe the fuzziness between indicator levels. The Delphi method and a random forest feature identification method are combined to obtain a subjective and objective combined weighting, determining the optimal combination weight of the four indicators. Based on Zadeh operator (ZO), maximum-minimum operator (MMO), weighted-average operator (WAO), and all-around restrictive operator (ARO), calculations are carried out for the synthesis of indicator weights and memberships. Maximal membership principle (MMP) and Credible identification principle (CIP) are utilized as evaluation principle to assess the bursting liability level, constructing 16 fuzzy comprehensive evaluation models. The impact of membership functions, fuzzy operators, and evaluation principle on evaluation results are systematically analyzed based on the discrimination results of 127 sample sets. The results show that the optimal fuzzy comprehensive evaluation model is constructed using the trapezoidal fuzzy numbers, weighted average operator, and maximal membership principle (TFN-WAO-MMP), with a classification accuracy of 97.64%. Finally, the optimal model is applied to 10 engineering instances, and the evaluation results are consistent with the actual situation, verifying the reliability and effectiveness of the model. Overall, these findings contribute to the development of a more sophisticated and accurate method for assessing the rock burst tendency of coal specimens. By leveraging the theory of fuzzy sets, this approach provides a more nuanced and nuanced evaluation of rock burst tendency, and thus offers the potential to improve workplace safety and efficiency in the coal mining industry.

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Study on the seismic damage and dynamic support of roadway surrounding rock based on reconstructive transverse and longitudinal waves

Abstract: D. Study on the seismic damage and dynamic support of roadway surrounding rock based on reconstructive transverse and longitudinal waves.

Cited by 3 publications

References 36 publications

Classification of coal bursting liability of some chinese coals using machine learning methods

Classification of coal bursting liability of some chinese coals using machine learning methods

Asymmetric Dynamic Support for Roadways in Subvertical Coal Seams

A coal bursting liability evaluation model based on fuzzy set theory and analysis of three influencing factors

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