Meso-scale modelling of FRP-to-concrete bond interfaces
Bibliographic record
Abstract
The bond behaviour between fiber-reinforced polymer (FRP) and concrete plays a critical role in the performance of FRP-strengthened reinforced concrete (RC) structures. While extensive research has been conducted on debonding failures, existing studies predominantly treat concrete as homogeneous, neglecting its inherent heterogeneity. This paper proposes an effective meso-scale finite element (FE) model incorporating random aggregate distributions to explicitly account for the heterogeneous nature of concrete. As only the compressive strength of concrete is usually reported in bond tests, a set of equations are identified as a guideline for calculating the material properties of mortar and coarse aggregates, as required by the damage plasticity constitutive relations of materials which are employed to model both coarse aggregates and mortar. The proposed model is validated through simulations of uniaxial tensile and compressive tests of concrete and FRP-to-concrete bonded joint experiments. Results demonstrate that the model’s capability to predict the mesoscopic damage and fracture evolution, as well as the macroscopic load-displacement curves and failure patterns. A parametric study reveals that increasing the coarse aggregate fraction from 30% to 50% enhances bond strength and displacement by 7–8%. This meso-scale approach provides a robust tool for developing bond strength and bond-slip models, incorporating concrete’s meso-structural characteristics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".