Comparative Study of Design Models for Shear Strengthening of RC Beams with NSM FRP
Bibliographic record
Abstract
This study investigates the accuracy of the existing design models for shears-strengthened reinforced concrete (RC) beams with near-surface mounted (NSM) fibre-reinforce polymer (FRP) rods and laminates. Comparative studies have been conducted on the predicted shear contributions of NSM FRP materials in the strengthened beams using state-of-the-art existing design models. To assess the accuracy of these models, the predictions were compared with the experimental results on 131 test specimens from 24 studies. The results of this study can be used for standard committees to choose the most precise models for their corresponding design standard code or guidelines. From the results of this study, it can be concluded that mechanics-based models proposed by Mofidi et al. (2023) and Bianco et al. (2014) were superior when compared to other existing models in most measured metrics. The models produced by regressions of data or neural networks only performed well under the statistical parameters for which they were fitted. Such models may not perform well when compared with the data that was not used to calibrate the models or when assessed by a metric that they are not calibrated with. On the other hand, for the mechanics-based models, due to the presence of the principles of shear mechanics and bonding in the development of such models, the mechanics-based models can perform to a satisfactory level with existing and incoming experimental test data and through different statistical test parameters.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".