EFFECTIVENESS OF GROUND PENETRATING RADAR FOR PREPARING PRE-TENDER DETERIORATION ESTIMATES ON ASPHALT COVERED REINFORCED CONCRETE BRIDGE DECKS
Bibliographic record
Abstract
The Penetradar Integrated Radar Inspection System (IRIS) Ground Penetrating Radar (GPR) was selected by Dalhousie University as the most appropriate technology for assessing the condition of asphalt-covered reinforced concrete bridge decks because of its ability to penetrate asphalt concrete overlays and data collection at traffic speeds up to 75-80 km/hr. This technology was selected from a list of other nondestructive test methods such as infrared thermography, ultrasonic methods, and impact echo testing. A research program was designed to examine the accuracy and confidence with which GPR can be used to predict the quantity and location of top layer reinforcement delaminations and damage from freezing and thawing at the asphalt/concrete interface. Seventy-two asphalt-covered reinforced concrete bridge decks were surveyed at traffic speeds using GPR for deterioration estimation. Data were recorded by collecting adjacent 0.75-m wide strips along the deck length. The GPR data were processed manually to determine areas of excess signal attenuation and areas of high concrete relative dielectric constant. Deterioration predictions made using GPR were compared quantitatively and spatially to ground-truthing data obtained from nine bridge decks using the well-established chain drag and half-cell potential surveys after the asphalt was removed from each bridge deck just prior to repair. Good to excellent correlation between the GPR predicted deterioration quantity and locations were observed on each of the nine bridge decks with the quantity and location of deterioration found on the decks using the ground-truthing methods. On a network level, the GPR results were observed to underestimate the actual repair quantity by 1.5% of the bridge deck surface area. The 95% upper and lower confidence limits of the GPR prediction of the deterioration quantities as a percent of the deck surface area were observed to be 8.3% underestimation and 4.6% overestimation with respect to the actual repair quantities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".