Genetic programming‐based model for estimating maximum pull load of fiber‐reinforced polymer‐to‐concrete bond interfaces with graphical user interface implementation
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This study presents a novel, interpretable machine learning framework for predicting the maximum pull load of fiber‐reinforced polymer (FRP) bonded to concrete substrates. A comprehensive test database comprising 983 datasets was gathered from relevant existing studies. The datasets include key input parameters such as concrete compressive strength, bond length, width of FRP sheet, width of concrete block, FRP thickness, and elastic modulus of FRP sheets, with the maximum pull load as the output parameter. Utilizing this curated database, a symbolic regression model based on genetic programming (GP) was developed to uncover the nonlinear relationships among critical variables including axial stiffness of FRP, bond length, and concrete compressive strength. The model's predictive performance was evaluated using standard regression metrics, achieving mean absolute error (MAE) and root mean square error (RMSE) values below 5 kN, mean absolute percentage error (MAPE) slightly above 10%, and coefficient of determination ( R 2 ) exceeding 0.90 on both training and testing datasets. These results confirm the model's accuracy and generalizability. Unlike black‐box models, symbolic regression offers an explicit mathematical expression, ensuring transparency and interpretability for engineering applications. To facilitate practical deployment, a user‐friendly graphical user interface (GUI) named MaxPLoad‐FRP‐Concrete‐GPaided‐PredictionModel was developed, enabling practitioners to input key design parameters and obtain immediate, interpretable predictions. This tool serves as a valuable decision‐support system in the structural design and quality control for FRP‐strengthened concrete structures.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it