Correlation-Based Model for Evaluating Ground Penetrating Radar (GPR) Data of Concrete Bridge Decks
Bibliographic record
Abstract
Correlation-Based Model for Evaluating Ground Penetrating Radar (GPR) Data of Concrete Bridge Decks K. Dinh, T. Zayed Pages 44-53 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: The Ground Penetrating Radar (GPR) has been studied for a long time as a non-destructive evaluation (NDE) technology for inspection of concrete structures. Currently, the most widely used technique for interpreting GPR data of concrete bridge decks is the one that based on the amplitudes measured at various interfaces such as asphalt-concrete, top rebar, or slab bottom. The assumption behind this technique is that a sound concrete deck should have the same reflection amplitude at these interfaces while any low number would indicate some sign of deterioration. Unfortunately, this assumption is not completely valid in most cases. As a consequence, the reported test results usually do not reflect real condition of concrete bridge decks in question. The main goal of this paper is therefore twofold: (1) to discuss the limitations of the amplitude analysis method, and (2) to propose a new model that interprets GPR data of concrete bridge decks. The model methodology is based on the comparison of GPR A-scans between two inspections, using the so-called correlation analysis. The results, indicated by the correlation coefficients, are then employed to develop a contour map that estimates different levels of deterioration. The model is then implemented to a case study in order to illustrate its methodology. Keywords: Ground Penetrating Radar (GPR), Non-destructive Evaluation (NDE), Concrete Structures, Concrete Bridge Decks, and Bridge Inspection. DOI: https://doi.org/10.22260/ISARC2013/0005 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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) | 0.002 | 0.001 |
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".