Structural health monitoring for CFRP bolted joints under tension/bending by embedded PZT transducers
Bibliographic record
Abstract
Previous studies have demonstrated that health monitoring of carbon fiber-reinforced polymer (CFRP) laminates in service can be achieved using embedded piezoelectric (PZT) sensors. To extend this method to jointed structures, this study proposes a structural health monitoring approach for CFRP-bolted joints under tensile and bending load conditions using pre-embedded PZT sensors. CFRP specimens with embedded PZT sensors were fabricated, exhibiting ultimate tensile strengths of 47.92 kN (without PZT) and 49.84 kN (with PZT) under static loading. The sensor embedding positions were determined based on the numerical simulation results of the stress distribution around the embedded PZT sensor during tensile and bending loads. PZT sensor voltage signals, Acoustic Emission (AE) data, and Digital Image Correlation (DIC) images were collected during the experiments to establish correlations among different measurement methods. The results show that, in tensile tests, broad PZT signal pulses correspond to combined shear-out and tear-out failure modes, while narrow pulses indicate shear-out failures. In bending tests, low-frequency fluctuations in the PZT signal fitting curve indicate micro-damage, whereas rapid fluctuations signal catastrophic structural 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 | 0.000 |
| 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 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".