Fibre reinforced polymer reinforcing bars for bridge decks
Bibliographic record
Abstract
This paper describes the behaviour of two full-scale models of a portion of highway bridge slab reinforced with fibre reinforced polymer (FRP) reinforcement. The first slab was reinforced totally with carbon FRP (CFRP), and the second slab was reinforced with hybrid glass FRP (GFRP) and steel reinforcement. The models were tested under static loading up to failure using a concentrated load acting on each span of the continuous slab and the two cantilevers to simulate the effect of a truck wheel load. Load-deflection behaviour, crack patterns, strain distribution, and failure mode are reported. The measured values are compared to values calculated using nonlinear finite element analysis model. The accuracy of the nonlinear finite element analysis is demonstrated using independent test results conducted by others. The analytical model is used to examine the influence of various parameters, including the type of reinforcement, boundary conditions, and reinforcement ratio. Based on serviceability and ultimate capacity requirements, reinforcement ratios for using CFRP and GFRP reinforcement for typical bridge deck slabs are recommended.Key words: bridges, deflection, FRP, reinforcement, concrete, punching, slabs, shear, finite element model, strain.
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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) | 0.003 | 0.001 |
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".