An analytical approach towards determining the strength of FRP-reinforced/prestressed concrete beams
Bibliographic record
Abstract
Non-corrosive fiber-reinforced polymers (FRP) are becoming a desirable replacement to steel bars in reinforcing/prestressing concrete structures. However, the difference between the two materials is not only related to the properties in the longitudinal direction of the bars as most of the current research work and design guidelines are concerned with. The properties in the transverse direction of the bars have basic differences, which may influence the beam strength and its mode of failure. This paper presents a comprehensive analytical modeling for evaluating the strength of concrete beams reinforced and (or) prestressed with FRP bars and the corresponding mode of failure. It takes into account significant parameters such as the crack path geometry, the crack width, and the properties of the bars in both longitudinal and transverse directions. The proposed analysis identifies any premature failure of beam due to the dowel failure of FRP reinforcement and determines the contribution of stirrups, if any, based on the number and actual tensile strain of the stirrups crossing the failure crack. A good agreement has been observed between the results of the developed model and the results of an experimental program conducted at the University of Windsor, as well as other published experimental programs.Key words: ACM, FRP, cracks, dowel action, reinforced concrete, strength.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".