A Novel Approach for Detection of Damage in Adhesivelybonded Joints in Plastic Pipes Based on Vibration Method Using Piezoelectric Sensors
Bibliographic record
Abstract
The use of dynamic response to identify damage in engineering structures has been the focus of several research works in the recent years. Most of the vibration-based damage assessment methods developed thus far require modal properties that are obtained via the traditional Fourier transform (FT). Unfortunately, the Fourier-based modal properties, such as natural frequencies, mode shapes, etc., have been reported to be mainly insensitive to structural damage, and hence are not regarded as suitable damage indicators. This paper discusses the application of piezoelectric sensors used for the evaluation of integrity of adhesively bonded joints in PVC plastic pipes. A systematic experimental and analytical investigation was carried out to demonstrate the integrity of adhesively bonded joints. Besides the commonly used methods, a newly emerging time-frequency method, namely the empirical mode decomposition (EMD), is also employed. Two novel "damage indices" are developed based on the evaluation of vibration signals with the use of EMD and the fast Fourier integration induced energies. The results are compared to the available damage index method based on the wavelet packet transformation (WPT), which has been used in the literature review. As it will be seen, by use of the damage indices, one could effectively detect the integrity of the adhesively bonded joints. Moreover, the energy indices can distinguish the differences among disbond densities in the joints.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".