Genetic programming‐based model for estimating maximum pull load of fiber‐reinforced polymer‐to‐concrete bond interfaces with graphical user interface implementation
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".