Case-Based Reasoning for Predicting Bond Strength in Fiber Reinforced Polymer (FRP) and Concrete
Bibliographic record
Abstract
The increasing adoption of Fiber Reinforced Polymer (FRP) bars in concrete structures necessitates accurate prediction of bond strength to ensure structural integrity and reliability. This study introduces Case-Based Reasoning (CBR) as an interpretable and efficient approach for predicting FRP-concrete bond strength. Utilizing a dataset of 227 experimental results, the CBR model achieves high accuracy, with an R 2 of 0.98 and a low Mean Squared Error (MSE) of 0.226 MPa. Sensitivity analysis identifies critical parameters such as bar diameter ( d b ), concrete compressive strength ( fc ’), cover-to-bar diameter ratio ( c/d b ), and embedment length-to-bar diameter ratio ( l d /d b ), demonstrating their varying influence across different surface types: helical lugged, spiral-wrapped, and sand-coated. The findings emphasize the practical applicability of CBR in tailoring design strategies for FRP-reinforced structures, offering engineers an interpretable and reliable tool to optimize performance while reducing computational complexity.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".