Finite element analysis of glass fiber reinforced polymer bridge decks
Bibliographic record
Abstract
Deterioration of concrete bridge decks has become a serious problem in the past few decades. Fortunately, non-corrosive, light-weight Fiber Reinforced Polymer (FRP) material provides an excellent alternative. More than 117 bridges in the USA have been built or repaired with FRP. In Canada, no FRP bridge deck has been used in the field, yet. However, Wardrop Engineering Inc., Faroex Ltd., and ISIS Canada have successfully designed, manufactured, and patented the filament-wound Glass Fiber Reinforced Polymer (GFRP) bridge deck. Since there is no design code for FRP bridge decks, a finite element method, labeled “L&D”, is proposed in this thesis to help bridge engineers better understand the structural behavior of FRP bridge decks. The L&D method is validated by comparing the analysis results with the experimental results of three filament-wound GFRP bridge decks. This L&D method is also applicable for analyzing FRP bridge decks manufactured by other processes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".