What question did this study set out to answer?

This research aims to develop a predictive modeling and optimization framework for analyzing buckling in composite plates on elastic foundations.

Machine learning-assisted predictive modeling and hybrid optimization of buckling characteristics in porous laminated composite plates on nonlinear elastic foundations

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Predictive modeling improves buckling performance in porous laminated plates using machine learning techniques, indicating nonlinear effects matter.

This research aims to develop a predictive modeling and optimization framework for analyzing buckling in composite plates on elastic foundations.
Employs Higher-Order Shear Deformation Theory for accurate shear deformation analysis.
Investigates impacts of ply-angle, imperfection amplitudes, and porosity on buckling load.
Utilizes a Stacked Ensemble Surrogate Modelling approach for prediction efficiency.
Implements a hybrid optimization strategy using Teaching-Learning-Based Optimization and Sequential Quadratic Programming.
Machine learning-assisted optimization improves prediction accuracy and computational efficiency.
Identifies significant factors influencing buckling characteristics, including foundation stiffness and imperfections.
Establishes effective design strategies for composite structures under varying conditions.

Agrawal et al. (2026) studied this question.

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Predictive modeling improves buckling performance in porous laminated plates using machine learning techniques, indicating nonlinear effects matter.