Inversion of short-term precursor of acoustic emission in uniaxial compression based on SOM neural network
Bibliographic record
Abstract
A good understanding of the precursor characteristics of rock failure is essential for geo-mechanical rock engineering. This paper proposes an inversion method for acoustic emission (AE) precursor signals based on a self-organizing map neural network. The feature of this method lies in a construction of cyclic segmentation iteration process. By segmenting and approximating the set of AE parameters, the AE precursor signals are extracted at 97% of the peak stress moment. The inversion results of the rock failure precursors in different lithology tests verified the rationality of this method. Compared with traditional AE precursor phenomena (including b-value decrease, fractal dimension decrease, and entropy sudden increase), the occurrence time of the precursor signals inverted in this study is closer to the time of rock failure. This indicates that these precursor signals are the approaching points from rock deformation to rock failure, proving the potential application value of these signals in short-term precursors and short-term warnings of rock failure. Considering the damage evolution characteristics of rock failure, the reasons for the generation of precursor signals were preliminarily explored, and the generation of precursor signals was attributed to the sudden increase in damage during the loading process. The obtained results will help develop a deeper understanding of the precursor phenomena of rock failure.
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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 | 0.001 |
| 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 | 0.001 |
| 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".