Two dimensional non-destructive testing data maps for reinforced concrete slabs with simulated damage
Bibliographic record
Abstract
This research presents the use of a total of five Non-Destructive Testing Techniques (NDTs) and their combination to detect and quantify subsurface simulated defects in Reinforced Concrete slabs. The NDT techniques were applied on a total of nine 1800 mm × 460 mm reinforced concrete slabs with varying thicknesses of 100 mm, 150 mm and 200 mm. Contour data maps from each technique were prepared. This Data article presents the Non-Destructive Testing Techniques' specifications, experimental set-up and converted 2-Dimensional NDT data maps for reinforced concrete slabs with simulated damage. The experimental research shows that combining multiple techniques together in evaluating the defects give significantly lower error and higher accuracy compared to that from a standalone test. For more details on the accuracy model of the NDTs, refer to the full length article entitled "Sub-surface simulated damage detection using Non-Destructive Testing Techniques in reinforced-concrete slabs" https://doi.org/10.1016/j.conbuildmat.2019.04.223 Rathod et al., 2019.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".