AI-driven residual strength diagnostics of composites using their electrical behavior under low-stress cyclic loading
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".