Modeling of Flexural Behavior and Punching Shear of Concrete Bridge Decks with FRP Stay-in-Place Forms Using the Theory of Plates
Bibliographic record
Abstract
A robust analytical model for predicting full response and ultimate load of concrete bridge decks constructed with fiber-reinforced polymer (FRP) stay-in-place (SIP) structural forms is presented. It adopts the plate theory to establish surface deflections, while incorporating concrete nonlinearity in compression and cracking in tension, as well as the degree of bond between the FRP SIP form and the concrete. The model accounts for various boundary conditions at the edges of the deck in both directions, including both finite and infinite width in the direction of traffic, and either fixed or hinged conditions in the other direction, depending on the connection to the support girders. A punching shear failure criterion was incorporated to predict the ultimate load. The model was validated against a large experimental database, and reasonable agreement was observed. The average percent difference in ultimate loads was 5.5%. The model was then used in a parametric study to assess the FRP reinforcement ratio in terms of the FRP plate thickness, the width of the deck parallel to traffic, and the span of the deck, which is the girder spacing. It was shown that reducing the FRP reinforcement ratio from 10.7 to 2.7% results in about a 20% reduction in punching shear ultimate load. The ultimate loads obtained for decks with (width/span) aspect ratios of 2.73, 1.33, 0.87, and 0.55 were 100, 94, 83, and 73%, respectively, of the ultimate load of the real condition of infinite width. Finally, the punching shear load decreased by about 18% as the deck span-to-depth ratio increased from 10 to 16.5.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".