Ground Penetrating Radar-based Deterioration Assessment of Bridge Decks
Bibliographic record
Abstract
The ASCE report card 2013 rated bridges at a grade of C+, implying their condition is moderate and require immediate attention. Moreover, the Federal Highway Administration reported that it is required to invest more than $20.5 billion each year to eliminate the bridge deficient backlog by 2028. In Canada 2012, more than 50% of bridges fall under fair, poor, and very poor categories, where more than $90 billion are required to replace these bridges. Therefore, government agencies should have an accurate way to inspect and assess the corrosiveness of the bridges under their management. \nNumerical Amplitude method is one of the most common used methods to interpret Ground Penetrating Radar (GPR) outputs, yet it does not have a fixed and informative numerical scale that is capable of accurately interpreting the condition of bridge decks. To overcome such problem, the present research aims at developing a numerical GPR-based scale with three thresholds and build deterioration models to assess the corrosiveness of bridge decks. Data, for more than 60 different bridge decks, were collected from previous research works and from surveys of bridge decks using a ground-coupled antenna with the frequency of 1.5 GHz. The amplitude values of top reinforcing rebars of each bridge deck were classified into four categories using k-means clustering technique. Statistical analysis was performed on the collected data to check the best-fit probability distribution and to choose the most appropriate parameters that affect thresholds of different categories of corrosion and deterioration. 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 on the areas of corrosion. The final result of this research is a four-category GPR scale with numerical thresholds that can assess the corrosiveness of bridge decks. The developed scale has been validated using a case study on a newly constructed bridge deck and also by comparing maps created using the developed scale and other methods. The comparison shows sound and promising results that advance the state of the art of GPR output interpretation and analysis. In addition, deterioration models and curves have been developed using Weibull Distribution based on GPR outputs and corrosion areas. The developed new GPR scale and deterioration models will help the decision makers to assess accurately and objectively the corrosiveness of bridge decks. Hence, they will be able to take the right intervention decision for managing these decks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".