Machine learning-based prediction of crack width and bond-dependent coefficient (kb) in GFRP-reinforced concrete beams
Bibliographic record
Abstract
Glass fiber reinforced polymer (GFRP)-reinforced concrete (RC) flexural elements are typically designed to fulfill the serviceability criteria, including deflection and crack width. Crack width control in RC structures enhances durability, increases service life, and improves aesthetic appearance. The design provisions of ACI 440.11-22 and CSA S806-12 incorporate a bond-dependent coefficient, k b , into equations controlling crack width to account for the bond between GFRP bars and concrete. The primary objective of this research is to apply eight machine learning (ML) models, including extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), adaptive boosting (AdaBoost), gradient boosting (GB), random forest (RF), K nearest neighbors (KNN), Ridge, and Lasso, to enhance crack width prediction accuracy in GFRP-RC beams. The study also considers the importance of the k b coefficient in estimating the crack width and investigates the key factors that primarily impact its value. The results indicated that existing design provisions generally overestimate crack width, whereas ML models demonstrate a noticeably closer alignment with experimental data. Additionally, AdaBoost stands out as the most accurate predictor of crack width. SHapley Additive exPlanations (SHAP) analysis highlights that GFRP bar strain, bar spacing, and concrete cover significantly impact the k b coefficient.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".