Application of machine learning for predicting adhesive damage used for joining structural steel with GFRP under hygrothermal effect
Bibliographic record
Abstract
Adhesively bonded composite-steel structures are widely used in civil engineering due to their excellent mechanical properties, particularly for strengthening and repairing damaged steel structures. This study investigates the adhesive damage behavior of Araldite 2015 used to join cracked S235JR steel structures. Finite element analysis (FEM) and machine learning (ML) techniques were employed to predict adhesive damage. Two types of composites, graphite and boron, were used, and the adhesive was aged in deionized water over a period of 7.5 months. Damage was evaluated at four intervals: before immersion, and after 1, 3, and 7.5 months, under different applied loads of F = 100 MPa, F = 200 MPa, and F = 300 MPa, at a constant temperature of 25°C. The damage ratio (Dr) was calculated using SolidWorks based on damage zone theory. Three regression models, linear regression, polynomial regression, and support vector regression (SVR) were employed to predict adhesive damage. The results demonstrate that the adhesive maintained its integrity under prolonged immersion and high loads, even after 7.5 months of exposure and at a load of 300 MPa. Among the ML models, SVR provided the most accurate predictions, achieving a determination coefficient of R 2 = 0.999 and outperforming other models across all evaluation metrics (MAE, MSE, RMSE).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".