Title: A stacking ensemble learning model for buckling response of the functionally graded carbon nanotube reinforced composite plates

Authors: Lalit Kumar Sharma; Jyotindra Narayan; Naveen Sharma

Addresses: Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India ' Department of Mechanical Engineering, Indian Institute of Technology Patna, Patna, India ' Department of Computer Science and Engineering, HMR Institute of Technology and Management, Delhi, India

Abstract: This study proposed a stacking ensemble learning model for the buckling response of functionally graded carbon nanotube-reinforced composite (FG-CNTRC) plates using an inverse hyperbolic shear deformation theory (IHSDT). Firstly, the mathematical for the FG-CNTRC plate has been developed in the framework of IHSDT, then a stacking ensemble learning model is employed to estimate critical buckling loads under various conditions, including different CNT distributions, span-to-thickness ratios, volume fractions, and temperature environments and compared with the performance index of random forest (RF) and support vector regression (SVR). Input datasets were generated using MATLAB, with an 85:15 train-test split and five-fold validation to ensure robustness. Numerical results validate the predictive accuracy of ML models, with stacking ensemble learning outperforming RF and SVR in terms of mean squared error (MSE), and the coefficient of determination (R²). This work demonstrates the effectiveness of machine learning techniques for predicting the mechanical behaviour of FG-CNT materials.

Keywords: functionally graded materials; buckling; machine learning; stacking ensemble; random forest; support vector regression; SVR; shear deformation theory.

DOI: 10.1504/IJSTRUCTE.2025.150124

International Journal of Structural Engineering, 2025 Vol.15 No.4, pp.388 - 409

Received: 09 Jun 2025
Accepted: 19 Aug 2025
Published online: 01 Dec 2025 *

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