Condition assessment of critical infrastructure with GPR
Bibliographic record
Abstract
Tunnels, bridges and dams represent some of the most critical public infrastructure. This paper presents case studies in novel application of GPR for condition assessments of these structures. The 200st Overpass in Langley, BC, Canada case study is a successful application of multiple frequency GPR systems to assess the structural condition of a critical highway overpass along the Trans Canada corridor. The GPR survey revealed construction deficiencies including mapping subsurface voids which was necessary in order to design proper remediation. The Mission, BC, Canada case study illustrates using Pipe Penetrating Radar (PPR), the underground, inpipe application of GPR for mapping voids outside a reinforced concrete storm sewer pipe. The same void was located and confirmed from an above ground GPR survey, thus successfully combining the results of in pipe, high frequency PPR with above ground low frequency GPR surveys. A 33 inch diameter vitrified clay pipe (VCP) that experienced catastrophic failures despite being installed only seven years ago in a California municipality was the subject of the third case study. PPR and CCTV inspection of over 9,000 ft of pipe provided quantitative pipe condition data and allowed the asset owners to design the most suitable and cost effective rehabilitation and replacement strategy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".