Validation of wireless impact echo prototype for condition assessment of concrete structures
Bibliographic record
Abstract
A new wireless impact echo prototype was developed for the condition assessment of reinforced concrete slabs. The new system includes a wireless handheld sensing device which connects by Bluetooth to a tablet equipped with a user-friendly software for managing the testing grid, recording measurements, processing and transforming the data to a frequency domain, and presenting the results in real time. To validate the device, three large-scale concrete slabs of dimensions 2000 mm x 2000 mm x 300 mm were fabricated with artificial defects including voids, delamination, vertical cracks, and geometric discontinuities. The influence of slab boundaries, defect depth and location, overall thickness, steel reinforcement, and an internal conduit on the obtained measurements were investigated. The results were also compared with other commercial non-destructive testing devices including ground penetrating radar (GPR) and ultrasonic pulse velocity (UPE), and the advantages and disadvantages of each method are discussed. Overall, the results suggest that the impact echo technique using the new wireless prototype is able to accurately detect and characterise various defect types provided that the right size of impactor is used. Use of improper impactor sizes can result in alternative vibration modes depending on the slab thickness and defect depth; in that case, multiple readings may be required. The new system greatly improved the ease-of-use of the impact echo technique, although the inspector should be trained to properly interpret the results. Compared with the GPR technique, the impact echo system was not as rapid and unable to detect the location of steel reinforcement; on the other hand, it was better able to detect defects in the presence of steel bars which tended to obscure readings using GPR. UPE measurements were also faster than the impact echo tests and performed well for slab thickness measurements and detection of certain defect types; however, readings were found to be affected by the presence of steel reinforcement and in some cases were inconsistent. Overall, it may be concluded that each technique presents certain strengths and weaknesses that should be considered in the condition assessment strategy. The results obtained from each technique are complementary and may be used to develop a complete understanding of the internal state of the structure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".