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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".