Bibliographic record
Abstract
Synopsis: This paper presents the results of a finite element analysis for three different bridges that have been recently constructed and tested in North America. In these bridges, different types of reinforcement (steel and FRP reinforcing bars) were used as reinforcement for the concrete deck slabs. Two bridges, Magog Bridge and Cookshire-Eaton Bridge, are located in Quebec, Canada, while the third one, Morristown Bridge, is located in Vermont, USA. The three bridges are girder-type with main girders made of either steel or prestressed concrete. The main girders were either simply or continuously supported over spans ranging from 26.2 to 43.0 m. The deck was a 200 to 230 mm thickness concrete slab continuous over spans of 2.30 to 2.8 m. Different types, sizes, and reinforcement ratios of glass and carbon FRP reinforcing bars were used. Furthermore, the three bridges are located on different road or highway categories, which mean different traffic volumes and environments. The bridges were tested for service performance using calibrated truckloads. The results of the field load tests were used to verify the finite element model. Comparisons showed that FEM can predict the behavior of such elements. Then, the model was used to investigate the effect of the FRP reinforcement type and ratio on the service and ultimate behavior of these bridge decks. According to the findings, a proposed reinforcement ratio was recommended and verified using the FEM to meet the strength and serviceability requirements of the design codes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".