Key Takeaways & Executive Findings
- •• Initial damage from engineering disturbances significantly reduces the load-bearing capacity of coal, as evidenced by decreased resistivity and increased acoustic emission counts during reloading. • Time-frequency analysis reveals that acoustic emission spectra evolve from bimodal to broadband with enhanced low- and high-frequency components, while resistivity spectra show bandwidth broadening and high-frequency advancement. • 3D AE localization and fractal-dimension analysis indicate that the spatial fractal dimension of cracks rises significantly during compaction, providing insights into damage evolution. • An integrated early-warning model combining multiscale entropy fusion, TCN-Transformer forecasting, recurrence-network analysis, and Bayesian framework achieves a 28.4-second lead time for dynamic hazard prediction.
Abstract
Initial damage from engineering disturbances in deep coal mining degrades mechanical properties and heightens dynamic-hazard risks, challenging conventional monitoring. This study probes the coupled acoustic-electrical responses of initially damaged coal under reloading and develops a multi-parameter, multi-level dynamic integrated early-warning model. Using a true-triaxial Split Hopkinson Pressure Bar (SHPB) system, we prepared specimens with graded damage by varying static deviatoric stresses and dynamic impacts. Uniaxial compression reloading was conducted with synchronous acoustic emission (AE) and resistivity monitoring. Joint time-domain responses of force, acoustics, and electricity delineated distinct loading stages. Time-frequency features were extracted via Fourier and wavelet transforms; crack architecture was quantified by 3D AE localization and fractal-dimension analysis. Initial damage markedly reduced load-bearing capacity. Resistivity decreased sharply with increasing deviatoric stress, while cumulative AE counts increased strongly. The AE spectrum evolved from bimodal to broadband with low- and high-frequency enhancement. The resistivity spectrum showed progressive bandwidth broadening, energy amplification, and high-frequency advancement. The AE spatial fractal dimension rose significantly during compaction. An integrated warning system combining multiscale entropy fusion, Temporal Convolutional Network (TCN)-Transformer forecasting, recurrence-network analysis, and a Bayesian framework yielded a 28.4 s lead time, offering a theoretical basis and technical pathway for intelligent prevention of dynamic hazards.
1. Introduction
As shallow coal resources are progressively depleted, coal mining is inevitably being extended into Earth's deep subsurface [1]. In China, mining depths are increasing by approximately 10–25 m per year, and many mines have entered, or are poised to enter, kilometer-scale depths [2]. At depth, the in situ environment of the coal–rock mass is increasingly complex—characterized by high in situ stress, elevated temperature, high pore pressure, and intense mining-induced disturbances—and the risks of dynamic hazards, including deformation and failure of surrounding rock in roadways and rockbursts, are thereby sharply elevated, threatening safe and efficient mine production [3]. Therefore, rigorous elucidation of damage-evolution mechanisms in deep coal–rock masses under complex stress paths, together with the development of an effective disaster forecasting and early-warning system, is of considerable theoretical and engineering significance for ensuring the safe and efficient extraction of deep resources.
Roadway excavation is among the activities that most severely disturb in situ stress equilibrium during deep mining operations [4,5]. As shown in the full text, the induced damage can significantly alter the mechanical and geophysical properties of coal, necessitating advanced monitoring and early-warning approaches.
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Xiayan Zhang, Enyuan Wang, Rongxi Shen, Huihan Yang, Haishan Jia, Shenglei Zhao, Zhoujie Gu, Zhenhua Hu, Chong Li, Meng Wang (2026). Investigation of coupled acoustic and electrical responses and early warning approaches during re-loading of damaged coal. SinoTechIntel Verified Research. https://doi.org/10.1016/j.ijmst.2026.01.004
Research & Educational Purpose Only:The translations, structured abstracts, analytical annotations, and data reports provided by SinoTechIntel are intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.
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Frequently Asked Questions
What is the main objective of this study?
The study investigates the coupled acoustic-electrical responses of initially damaged coal under reloading and develops a multi-parameter, multi-level dynamic integrated early-warning model for dynamic hazards in deep coal mining.
How was the initial damage in coal specimens induced?
Specimens were prepared with graded damage using a true-triaxial Split Hopkinson Pressure Bar (SHPB) system by varying static deviatoric stresses and dynamic impacts.
What are the key findings regarding acoustic emission and resistivity responses?
Initial damage markedly reduced load-bearing capacity. Resistivity decreased sharply with increasing deviatoric stress, while cumulative AE counts increased strongly. The AE spectrum evolved from bimodal to broadband, and the resistivity spectrum showed progressive bandwidth broadening and high-frequency advancement.
What early-warning model was developed and what lead time was achieved?
An integrated warning system combining multiscale entropy fusion, Temporal Convolutional Network (TCN)-Transformer forecasting, recurrence-network analysis, and a Bayesian framework was developed, achieving a 28.4-second lead time for dynamic hazard prediction.
What is the significance of this research for deep mining safety?
The research provides a theoretical basis and technical pathway for intelligent prevention of dynamic hazards such as rockbursts, enhancing safety in deep coal mining operations.
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