Effectiveness of Ground Penetrating Radar in Predicting Deck Repair Quantities
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
Ground penetrating radar (GPR) was examined as an alternative or supplement to visual inspection methods for predicting reinforced concrete bridge deck repairs. Visual inspection has frequently resulted in grossly inaccurate estimates of repairs causing large maintenance cost overruns. GPR-predicted deteriorations were compared to deterioration detected using the chain drag and half-cell potential methods on 24 asphalt covered reinforced concrete decks exhibiting a broad spectrum of deterioration levels. The differences among the deterioration quantities resulting from these surveys were normalized for comparison with respect to the deterioration area and deck size. Large proportions of all decks surveyed containing less than 10% and more than 50% deterioration of the total deck surface area (as measured by chain drag) exhibited significant differences between the GPR and both ground-truth survey quantities. Insignificant differences between GPR predictions and the ground-truth results were observed for six out of seven decks exhibiting deterioration levels between 10 and 50% (by chain drag). It is concluded from this investigation that a combination of visual inspection and GPR inspection surveys for all decks can improve repair estimates and reduce the occurrence of gross underestimates of repair quantities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| 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