Numerical Evaluation of Structural Behavior of the Simply Supported FRP-RC Beams
Bibliographic record
Abstract
The main problem of steel-reinforced concrete structures is corrosion of steel reinforcements which leads to premature failure of the concrete structures. This problem costs a lot annually to rehabilitate and repair these structures. In order to improve the long-term performance of reinforced concrete structures and for preventing this corrosion problem, Fiber Reinforced Polymer (FRP) bars can be substituted of conventional steel bars for reinforcing concrete structures. This study is a numerical study to evaluate structural behavior of the simply supported concrete beams reinforced with FRP bars in comparison with steel-reinforced concrete beams. The commercial Finite Element Modeling program, ABAQUS, has been used for this purpose and the ability of aforementioned program has been investigated to model non-linear behavior of the concrete material. In order to evaluate the structural behavior of FRP-reinforced concrete beams in this study, two different aspects have been considered; effect of different types and ratios of reinforcements and effect of different concrete qualities. For the first case, different types and ratios of reinforcements, four types of reinforcing bars; CFRP, GFRP, AFRP and steel, have been considered. In addition, the concrete material assumed to be of normal strength quality. For verifying the modeling results, all models for this case have been modeled based on an experimental study carried out by Kassem et al. (2011). For the second case, it is assumed that all the models contain high strength concrete (HSC) and the mechanical properties of concrete material in this case are based on an experimental study performed by Hallgren (1996). Hence, for comparing the results of the HSC and NSC models, mechanical properties of reinforcements used for the second case are the same as the first case. Furthermore, a detailed study of the non-linear behavior of concrete material and FE modeling of reinforced concrete structures has been presented. The results of modeling have been presented in terms of; moment vs. mid-span deflection curves, compressive strain in the outer fiber of concrete, tensile strain in the lower tensile reinforcement, cracking and ultimate moments, service and ultimate deflections, deformability factor and mode of failure. Finally, the results of modeling have been compared with predictions of several codes and standards such as; ACI 440-H, CSA S806-02 and ISIS Canada Model.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".