AI-driven residual strength diagnostics of composites using their electrical behavior under low-stress cyclic loading
Why this work is in the frame
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Bibliographic record
Abstract
A novel non-destructive testing (NDT) method was developed to predict the residual strength of composites, with unknown histories of fatigue damage, using their electrical behavior during a low stress cyclic loading test. Ninety-five samples, representing a wide range of fatigue damage levels, were prepared and subjected to a low-stress cyclic loading test, as a diagnostic test, while their electrical behavior was monitored. The samples then underwent quasi-static loading until failure to measure their corresponding residual strengths. To establish a relationship between the electrical behavior of samples during the diagnostic test and their corresponding residual strengths, various machine learning techniques were implemented. K-nearest neighbor (KNN), Decision Tree (DT), Random Forest, Extreme Gradient Boosting, Support Vector Regressor (SVR), and Feedforward Artificial Neural Networks were employed in two different approaches: as standalone predictors, and in an ensemble learning approach. The analysis demonstrated that a KNN meta-model, incorporating DT, SVR, and KNN as base models, in an ensemble framework, achieved the best performance, with a mean absolute percentage error (MAPE) of 5.7% in predicting residual strength. This significant performance underscores the potential of our low-stress diagnostic test for predicting the residual strength of composites, even when the fatigue damage history is unknown.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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