Machine learning regression approaches for predicting the ultimate buckling load of variable-stiffness composite cylinders

• Published in: Acta mechanica Vol. 232; no. 3; pp. 921 - 931
• Main Authors: Kaveh, A., Dadras Eslamlou, A., Javadi, S. M., Geran Malek, N.
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.03.2021; Springer; Springer Nature B.V
• Subjects: Algorithms; Analysis; Aspect ratio; Buckling; Classical and Continuum Physics; Control; Cost analysis; Cylinders; Decision trees; Dynamical Systems; Engineering; Engineering Fluid Dynamics; Engineering Thermodynamics; Finite element method; Heat and Mass Transfer; Machine learning; Mathematical analysis; Metamodels; Methods; Nonlinearity; Optimization; Original Paper; Regression analysis; Regression models; Solid Mechanics; Steering; Stiffness; Theoretical and Applied Mechanics; Vibration

Due to the high computational cost of finite element analyses, particularly in optimization tasks, establishing a high-fidelity surrogate model is of immense importance to engineers. Analyses of fiber steering composite cylinders are costly and often involve high nonlinearity. Among different metamo...