Experimental Study on Full-Scale Pretensioned Bridge Girder Damaged by Vehicle Impact and Repaired with Fiber-Reinforced Polymer Technology
Bibliographic record
Abstract
A bridge was damaged when a dump truck violated the height clearance limitation on Highway 401 in Ontario, Canada. The collision caused extensive damage to the AASHTO Type-III precast/prestressed bridge girders, which led to the closure of the two-lane bridge. Crack mapping showed extensive torsion-shear cracks between the girder quarter points, horizontal crack at the flange-web junctions, and spalled concrete at point of impact. Preliminary elastic testing on the girder established that the flexural capacity of the girder had not been significantly affected. As such, flexural strengthening was not necessary. Crack patterns and severity, followed by analysis, have shown that the girder is deficient in shear capacity. Therefore, the girder was strengthened for shear throughout its entire length using carbon fiber–reinforced polymer (CFRP) sheets. This paper presents a summary of the design and detailing of the elastic behavior test conducted before repair, the girder repair methodology, and results from proof load testing of the repaired girder. It was shown that the rehabilitated girder could sustain flexural live load demand. A field application was also carried out using the same rehabilitation technique on another impact-damaged bridge in Ontario. It was viewed as a major budget-saving project compared to the girder replacement alternative, because of the speed of rehabilitation and the minor traffic disruptions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".