Finite element model for new continuous reinforced concrete pavement (CRCP) using GFRP bars
Bibliographic record
Abstract
Substitution of conventional steel reinforcing bars with Glass Fibre Reinforced Polymer (GFRP) bars in Continuously Reinforced Concrete Pavement (CRCP) gives solutions to the problems caused by corrosion of reinforcement. Concrete volume change is known to cause crack development in CRCP in the early age, afterwards, wheel load will be the governing factor for the development and the propagation of further cracks. In this study, the stress levels in the GFRP bars and the displacement in CRCP are predicted by analytical methods. The effects of bar diameters, soil interaction and concrete aggregates have been investigated. Two different computer programs utilizing the finite element method have been used. A design of a CRCP using GFRP reinforcing bars has been proposed. The results revealed that using GFRP bars as reinforcement in CRCP reduces the stress in the reinforcement and increases the displacement. Also, the area of the reinforcement has affected the crack pattern of CRCP. As well, the friction from the pavement sub-base has important effects on the development of the reinforcement's stresses and CRCP displacement. It was concluded that the stress levels in GFRP reinforcing bars and the CRCP displacements of the proposed pavement are shown to be within the design requirements.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".