In-situ residual strength prediction of composites subjected to fatigue loading
Bibliographic record
Abstract
A novel approach is introduced for in-situ residual strength prediction of glass epoxy composites subjected to fatigue loading, by integrating piezo-resistivity-based structural health monitoring with machine learning techniques. In this process, composite samples made conductive with carbon nanotubes are subjected to fatigue loading while their electrical resistance (ER) is monitored. The ER features most closely related to the residual strength are identified and used to train various machine learning algorithms. Ridge regression, K-nearest neighbor (KNN), Decision Tree (DT), Random Forest, Extreme Gradient Boosting, and Support Vector Regressor (SVR) are implemented in two different approaches: as standalone predictors, and in an ensemble learning approach to predict the residual strength. The analysis shows that the KNN meta -model within an ensemble framework, integrating DT, SVR, and KNN as base models, demonstrates superior performance, with a mean absolute percentage error of 4.7% in predicting residual strength.
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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".