A New Avrami-Based Exponential Model for Predicting Fiber-Reinforced Polymer Bar Service Life: A Comparison with Existing Models Using a Large Database
Bibliographic record
Abstract
The fiber-reinforced polymers (FRP) bar is a promising solution to problems caused by steel rebar corrosion in concrete. To assess the service life of the FRP bar based on accelerated test results, it is crucial to have a reliable model. Here, a modified exponential (MEP) model is proposed based on the Avrami equation. The Avrami equation provides a theoretical foundation for the empirical exponential (EP) model and does not a priori fix the power of the exposure time to one. A database containing 903 data points from 74 groups of test specimens is assembled to compare the reliability of the MEP model vis-a-vis the EP, single logarithmic, double logarithmic, and power function models. The combination of Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), and the coefficient of determination (R2) criteria is proposed for assessing model reliability. It is shown that in certain cases the combined criteria, versus R2 alone, significantly increase the number of test groups meeting the acceptable performance limit. Observed test data aberrations are found to have minor influence on the results of the EP model, but they significantly influence the results of the other four models. The EP model generally predicts the lowest activation energy and the smallest strength retention for similar groups of bars, while the predicted values of the other four models exhibit a relatively small difference. The difference between the predicted strength retention values of the EP and MEP models shows an increasing trend with the increase of the absolute value of (1 − n), where n is the power of the exposure time in the MEP model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".