Structural Engineering and Mechanics

Volume 98, Number 4, 2026, pages 471-503

DOI: 10.12989/sem.2026.98.4.471

Buckling analysis of cosine functionally graded plates: Synergy between finite element and Gradient boosting machine learning algorithm

Tayeb Si Tayeb , Mohamed-Ouejdi Belarbi , Tan Ngoc Nguyen , Smain Bezzina , Mohammed Sid Ahmed Houari

Abstract

Cosine Functionally Graded (CFG) plates offer a smoother material transition and better stiffness-toweight ratios than traditional grading profiles. In this study, we investigate the buckling behavior of these plates using a recently developed Higher-Order Zigzag Theory (HOZT). The high-fidelity data generated from our HOZT model served as the training set for a Gradient Boosting Machine (GBM) algorithm. We used key parameters such as skew angle (Φ), boundary conditions, aspect ratio (a/b), side-to-thickness ratio (a/h) and gradient index (ℜ) as input features to predict the critical buckling load (𝑃̅𝑐𝑟 ). Our numerical results, show that the GBM model is remarkably accurate. A parametric dataset is generated considering key variables, including skew angle (0°-60°), aspect ratio (a/b), side-to-thickness ratio (a/h=5-100), boundary conditions, and material gradient index (ℜ). The results show that the non-dimensional critical buckling load significantly increases with skew angle (up to ~150% increase from 0° to 60°) and decreases with increasing a/h, with the most pronounced variation observed for a/h≤20. The GBM model demonstrates excellent predictive capability, with deviations from finite element results remaining negligible (typically less than 1% across all tested cases). Overall, this work demonstrates that coupling physics-based simulations with data-driven models is a highly effective way to reduce costs while maintaining the precision needed for the rapid stability assessment of advanced structures.

Key Words

axisymmetric p-version model; stress intensity factor; virtual crack extension method; robustness; error prediction; Poisson locking.

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