Numerical and theoretical investigations on flexural behavior of two-way RC slabs strengthened with BFRP grid-ECC composites
Bibliographic record
Abstract
This paper delves into the flexural behavior of reinforced concrete (RC) two-way slabs strengthened with basalt fiber reinforced polymer (BFRP) grid and engineered cementitious composite (ECC). Building upon a prior comprehensive experimental investigation on slabs strengthened with BFRP-ECC composites, this study focuses on developing reliable strength models through the proposal of accurate finite element (FE) models. By meticulously comparing failure modes, load–deflection responses and strain developments of steel reinforcements and BFRP grids between FE predictions and experimental observations, the efficacy of the FE model in accurately predicting test results is demonstrated. The average ratio of the predicted to experimental ultimate load is 1.02, with a coefficient of variation of 0.041. A subsequent parametric study shed lights on key factors influencing the flexural performance of these strengthened slabs. It turned out that increasing ECC thickness significantly enhanced the flexural capacity, with an improvement ranging from 30.2% to 140.2%. Furthermore, a design-oriented model, incorporating the development of yield line and uniformity of BFRP strain is introduced, effectively capturing the ultimate load and moment of strengthened slabs. This research not only advances the understanding of the strengthening mechanism but also offers practical insights for designing and assessing such composite systems.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".