Ground penetrating radar-based deterioration assessment of RC bridge decks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Although ground penetrating radar (GPR) technology is commonly used to assess the condition of reinforced-concrete (RC) bridge decks, the GPR data interpretation is not straightforward. Further, the thresholds that define the severity of deterioration are selected arbitrarily. This paper aims to solve a problem associated with GPR results generated by using a numerical amplitude method to assess corrosiveness of bridge decks. Design/methodology/approach Data, for more than 50 different bridge decks, were collected using a ground-coupled antenna. Depth-correction was performed for the collected data to normalize the reflected amplitude. Using k-means clustering technique, the amplitude values of each bridge deck were classified into four categories. Later, statistical analysis was performed where the threshold values of different categories of corrosion and deterioration are chosen. Monte-Carlo simulation technique was used to validate the value of these thresholds. Moreover, a sensitivity analysis was performed to realize the effect of changing the thresholds in the areas of corrosion. Findings The final result of this research is a four-category (good, fair, poor and critical) GPR scale with three fixed numerical thresholds (−7.71 dB, −10.04 dB and −14.63 dB) that define these categories. Besides, deterioration curves have been modeled using Weibull function and based on GPR outputs and corrosion areas. Originality/value The developed numerical GPR-based scale and deterioration models are expected to help the decision-makers in assessing the corrosiveness of bridge decks accurately and objectively. Hence, they will be able to take the right intervention decision for managing these decks.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it