Mechanical behavior and acoustic emission characteristics of initially damaged coal under triaxial cyclic loading and unloading
Bibliographic record
Abstract
During deep coal mining, an instability failure of coal usually occurs under the combined effect of initial damage and triaxial cyclic loading and unloading (TCLU). Therefore, this study investigated the impact of initial damage on mechanical behavior and acoustic emission (AE) characteristics of coal under TCLU. Initial damage variables (IDVs) of coal specimens were quantified using preloading, followed by TCLU experiments to assess the deformation, energy distribution, and fracture development. The results revealed that the increase in IDVs significantly reduced the structural integrity of coal specimens, increased the cumulative irreversible strain, and enhanced the dissipated energy owing to microfracture expansion. Moreover, AE monitoring showed earlier activation of fractures and a higher occurrence of large-scale rupture events of coal specimens with high IDVs, which correlated with decreasing AE b values (reflecting the different scales of fracture within specimens) and increasing S values (reflecting the AE activity within specimens). Additionally, computed tomography analysis revealed intensified fracture networks and increasing three-dimensional fractal dimensions of coal specimens with higher IDVs. Finally, the coupling effect of TCLU and initial damage on the weakening mechanism of coal was investigated. Initial damage significantly reduced the structural integrity of coal by increasing the number of weak planes within coal specimens, contributing to the earlier activation and rapid expansion of fractures at low stress levels under TCLU and eventually accelerating the weakening process of coal. This study provides a scientific basis and theoretical support for the prevention and control of dynamic disasters in deep coal mining.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".