Delamination and crack detection and localization of a laminated composite plate structure using regression and regression-based neural network methods.

Despite significant progress, a comprehensive analysis of Machine learning algorithms for detecting delamination and crack damage in composite laminate plates is noticeably absent in the literature. This study aims to bridge this gap by meticulously help spectrum of six regression algorithms. By utilizing simulated displacement-time responses from a 16-layer laminated composite plate structure subjected to a random force, a robust dataset is constructed to evaluate the efficacy of these machine learning paradigms. Performance assessments rely on critical metrics: coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). [ABSTRACT FROM AUTHOR]