Clustering-Based Threshold Model for Condition Assessment of Concrete Bridge Decks with Ground-Penetrating Radar
Bibliographic record
Abstract
Ground-penetrating radar (GPR) has been extensively studied for condition assessment of concrete bridge decks in North America. Although several methods for analyzing GPR data have been proposed, the commonly accepted method evaluates the condition of concrete bridge decks on the basis of the difference between reflection amplitudes of the top rebar layer. It is assumed in the method that strong reflection indicates sound concrete, whereas the area with high-amplitude attenuation is associated with concrete corrosion. The final result is a contour map of reflection amplitude in decibel scale with the thresholds selected arbitrarily to define the severity of concrete deterioration. Because subjective determination of threshold values may lead to inconsistency in the result obtained, this paper proposes a robust method for resolving that issue. Specifically, after depth correction was performed for top rebar amplitudes, on the basis of K-means clustering technique these amplitude data were grouped into a number of condition categories. Through two case studies in North America, the methodology was implemented and compared with the results provided by other technologies, namely, concrete resistivity, half-cell potential, and laboratory chloride content analysis. The implementation showed that while the proposed method was simple to employ, it still provided reasonable results that were in line with the outputs provided by the other techniques.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".