Exploration and Improvement of Fuzzy Evaluation Model for Rockburst

Wang, Qiwei; Wang, Chao; Liu, Yu; Xu, Jianhui; Wang, Tuanhui; Li, Yuefeng; Liu, Quanrui

doi:10.1007/s42461-024-00933-3

Cited by 4 publications

(2 citation statements)

References 72 publications

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“…In the sphere of machine learning, the initial phase of model deployment is underscored by data preprocessing, a critical procedure adopted across a spectrum of machine learning methodologies within mining engineering [41][42][43][44][45] to bolster model precision and reliability. This investigation pursues the identification of the superior model via a sensitivity analysis centered on data preprocessing techniques specific to index data.…”

Section: Sensitivity Analysismentioning

confidence: 99%

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

Wang,

Liu,

et al. 2024

Sci Rep

Self Cite

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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: Sensitivity Analysismentioning

confidence: 99%

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

Wang,

Liu,

et al. 2024

Sci Rep

Self Cite

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“…As the working surface progressively advances, the mining space expands, resulting in a continuous increase in the area of the direct roof overhang. Once the immediate roof attains its maximum span, it begins to collapse [42][43][44][45][46]. As the working face advances, the undrawn top coal and the backward rock mass accumulate in the goaf.…”

Section: The Main Filling Structural Factors Affecting the Filling Ef...mentioning

confidence: 99%

Study on the Partial Paste Backfill Mining Method in a Fully Mechanized Top-Coal Caving Face: Case Study from a Coal Mine, China

Du,

Chen,

et al. 2024

Sustainability

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Paste backfill mining is an significant part of green coal mining, which can improve resource utilization and extend the service life of mines. It is important for solving the “three under, one above” mining problem and avoiding industrial wastes such as coal gangue and fly ash that occupy farmland and pollute the environment. To address the difficult filling problem of a fully mechanized top-coal caving face (FMT-CCF), a new method of partial paste backfill mining is herein proposed. First, the partial paste backfill mining method and implementation steps of the FMT-CCF are introduced in detail. Then, the mechanistic model of the roof beam in partial paste backfill mining is established. Then, the filling structural factors on the filling effect of the 42105 FMT-CCF are determined. Dependent on the assay of the migration law of overlying stratum after filling, numerical simulation analysis is used to research the feature effect of the main filling structural factors on the filling effect. Finally, the paste filling rate, filling width, and filling strength suitable for the 42105 FMT-CCF are obtained. When the filling rate reaches 100%, a significant alteration takes place, resulting in the efficient decrease of the overlying rock stress arch shell’s height. As the width of the filling body expands from 10 m at each end to 20 m, the stress arch of the overlying rock experiences maximum reduction, specifically decreasing by approximately 14 m. When the strength of the filling body is greater than 0.4 GPa, the filling effect is better. This study has important guidance and reference significance for the partial paste backfill of FMT-CCF in thick seam mining.

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Classification forecasting research of rock burst intensity based on the BO-XGBoost-Cloud model

Yuan,

Ji,

et al. 2024

Earth Sci Inform

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Exploration and Improvement of Fuzzy Evaluation Model for Rockburst

Cited by 4 publications

References 72 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

Study on the Partial Paste Backfill Mining Method in a Fully Mechanized Top-Coal Caving Face: Case Study from a Coal Mine, China

Classification forecasting research of rock burst intensity based on the BO-XGBoost-Cloud model

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