Strengthening full-scale damaged prestressed concrete bridge girders with CFRP sheets
Bibliographic record
Abstract
A large percentage of bridges in Canada were constructed over thirty years ago and their condition steadily declining. A product of deterioration and corrosive environments, many structures have been rendered unfit as per design codes and [are] structurally unsound. Constructing new structures and conventional repair methods are financially costly. A solution lies in fibre reinforced polymers (FRP). This thesis summarizes experimental projects regarding FRP usage in field applications. An actual damaged bridge girder was removed and rehabilitated with the FRP system. It was loaded incrementally and statically, nearing failure, investigating the reliability of the rehabilitation technique proposed to revive strength capacity to an acceptable level. A finite element computer simulation was created, modeling the load-history of the rehabilitated girder, as well as three full-scale damaged duble-tee girders, recently rehabilitated and loaded to collapse. This full-scale testing program and computer replication shall provide engineers with confidence in using FRP technology in girder strengthening.
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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 | 0.000 |
| 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) | 0.001 | 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".