Intelligent assessment of rock structure degradation based on entropy features of piezoelectric signals and their algorithm-optimized fusion
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Bibliographic record
Abstract
The process of assessing structural degradation is crucial, as it ensures structural integrity and prevents potential hazards, while also aiding in the development of maintenance strategies. This article introduces an entropy-based methodology for extracting structural degradation features from piezoelectric signals to evaluate the degradation of rock structures. Initially, the Fourier transform decomposes the piezoelectric signal into various harmonic components, followed by the reconstruction of frequency-band component signals through the superposition of harmonic signals within a predetermined frequency band. The optimal frequency bandwidth and the frequency position of the reconstructed components are determined using a predetermination dataset based on the correlation coefficient. An intelligent multifeature fusion algorithm, based on a genetic algorithm, is designed to assign weights to each feature and introduce a bias to correct fusion errors. Experimental studies on granite demonstrate over 90% accuracy in assessing the rock health index using piezoelectric signals to identify degradation states.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it