Ultrasonic Testing of a Grouted Steel Tank for Debonding Conditions
Bibliographic record
Abstract
Abstract The non-invasive detection of debonding conditions and cavities beneath the wall of a steel tank are common applications in a variety of engineering and construction fields. Compressional ultrasonic waves have been used for the evaluation of steel plate thicknesses; however, they lack energy for penetrating a Portland cement grout in contact with a steel wall to detect debonding conditions. In this work, a joint analysis of surface waves and Lamb waves (high and low frequency) is used for the detection of debonding conditions in a scale model grouting steel tank. The propagation of high frequency ultrasonic waves generated by a 50-kHz transmitter along the side of the tank model is analyzed in the time domain and the frequency domain. In addition, using instrumented hammers with plastic or aluminum tips (low frequency sources) and a 50-kHz transmitter, three different configurations are used for the analysis of surface waves. The low-frequency Rayleigh waves generated by the hammer are used for void detection. Fourier spectra of the measured signals indicate that the effect of a void on the waves propagating through the medium is reduced when there is debonding. The comparisons of theoretical (high frequency) dispersion curves with experimental ones, computed from frequency wavenumber (FK) spectra, show that Lamb waves dominate the surface response in the wall of the steel tank. High frequency Lamb waves are successfully used in the detection of debonding between the tank wall and the grout because of the lower attenuation measured on top of the void.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".