Performance evaluation of granite rock based on the quantitative piezoceramic sensing technique
Bibliographic record
Abstract
Granite is a common engineering material that exhibits complex mechanical properties under external loads. This study conducted experimental research and analysis in conjunction with the active monitoring technology of piezoelectric ceramics. A quantitative analysis method for the mechanical properties of rock materials based on piezoelectric health monitoring was established, and for the first time, the piezoelectric monitoring results of rock were mapped and compared with the uniaxial compression performance indicators of rock. In this study, two sets of cyclic impact experiments were conducted on granite samples using a drop hammer. The piezoelectric signals of the granite samples were detected using piezoelectric ceramic active sensing technology. A piezoelectric ceramic damage monitoring method was proposed, and the damage factor of the granite samples was calculated using the wavelet packet energy method. Subsequently, uniaxial compression experiments were performed on the damaged granite samples to obtain mechanical performance data. Finally, a mathematical relationship model was established between the piezoelectric signal and the uniaxial compressive strength of the rocks. It was found that the damage factor of the piezoelectric monitoring signal of the damaged rock were linearly related to the uniaxial compressive strength of the damaged rock.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".