Finite Element Analysis of the Radial Cracks at Glass Fiber Rinforced Polymer (GFRP) Reinforced Concrete: Effect of the Concrete Hydration Process
Bibliographic record
Abstract
This study presents a numerical analysis of the occurrence of radial damage in concrete reinforced with glass fiber-reinforced polymer (GFRP) rebars.The difference in radial thermal expansion coefficients between GFRP rebars and concrete can induce cracking in the surrounding concrete.Furthermore, the behavior of early-age concrete is significantly influenced by the hydration process, which was simulated using the finite element software ABAQUS.Numerical simulations were conducted to assess the evolution of cracking in early-age concrete reinforced with GFRP rebars.The thermal strain, dependent on the degree of hydration, was incorporated into the simulation.The temperature evolution due to the heat of hydration was also modeled, with peak hydration temperatures reaching approximately 50℃, to capture the development of thermal gradients in the concrete.Mazar's damage model was employed to describe radial cracking.An analytical model was applied to study early-age concrete, wherein its thermo-mechanical properties were defined as functions of the hydration degree.Numerical results reveal that radial damage in early-age concrete is particularly pronounced around GFRP reinforcement.A comparison of the numerical results with analytical solutions confirms the validity of the model, with relative error of 8.57 × 10 -6 highlighting the accuracy of the proposed numerical approach.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".