Effects of Rebar Size and Volume Fraction of Glass Fibers on Tensile Strength Retention of GFRP Rebars in Alkaline Environment via RSM and SHAP Analyses
Bibliographic record
Abstract
This study evaluates the degradation of glass fiber reinforced polymer (GFRP) rebars in alkaline environment under accelerating aging in terms of tensile strength retention (TSR). In addition to environmental conditions such as the pH of the surrounding solution, temperature, and aging duration, the manufacturing parameters of GFRP rebars (i.e., diameter of rebar, db) and volume fraction (Vf) are vital in the degradation of GFRP rebars in alkaline environments as well as scarcely reported. To assess the effect of these variables on the degradation (i.e., TSR), shapely additive explanations (SHAP analysis) based on light gradient-boosting machine (Light GBM), and statistical analysis using response surface methodology (RSM) were used. The Light GBM and RSM models were developed using 715 experimental results of TSR obtained from the existing literature. The performance of both models was reliable in terms of correlation and error analysis. The interaction among the variables was further analyzed using detailed explanations of how each variable affected the prediction of TSR. The results revealed that the TSR generally increases at higher Vf and db of GFRP rebars; however, it decreases with increasing pH, temperature, and duration of exposure. Furthermore, maximum TSR was recorded for pH of 12.6 (Vf=0.62−0.70 and db=14–16 mm). Finally, severe degradation was observed for rebars having 0.55>Vf>0.70. The findings of this study suggest that the current practice of various structural codes using GFRP rebars having minimum mass fraction of 70% (≈0.48Vf) could be improved by using the range of Vf determined in this study to minimize the degradation in alkaline environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".