Purpose: The purpose of this paper is to attempt the buckling analysis of a laminated composite skew plate using the C₀finite element (FE) model based on higher-order shear deformation theory (HSDT) in conjunction with minimax probability machine regression (MPMR) and multivariate adaptive regression spline (MARS).Design/methodology/approach: HSDT considers the third-order variation of in-plane displacements which eliminates the use of shear correction factor owing to realistic parabolic transverse shear stresses across the thickness coordinate. At the top and bottom of the plate, zero transverse shear stress condition is imposed. C₀FE model based on HSDT is developed and coded in formula translation (FORTRAN). FE model is validated and found efficient to create new results. MPMR and MARS models are coded in MATLAB. Using skew angle (α), stacking sequence (Ai) and buckling strength (Y) as input parameters, a regression problem is formulated using MPMR and MARS to predict the buckling strength of laminated composite skew plates.Findings: The results of the MPMR and MARS models are in good agreement with the FE model result. MPMR is a better tool than MARS to analyze the buckling problem.Research limitations/implications: The present work considers the linear behavior of the laminated composite skew plate.Originality/value: To the authors’ best of knowledge, there is no work in the literature on the buckling analysis of a laminated composite skew plate using C₀FE formulation based on third-order shear deformation theory in conjunction with MPMR and MARS. These machine-learning techniques increase efficiency, reduce the computational time and reduce the cost of analysis. Further, an equation is generated with the MARS model via which the buckling strength of the laminated composite skew plate can be predicted with ease and simplicity.

In the present work, for the first time, free vibration response of angle ply laminates with uncertainties is attempted using Multivariate Adaptive Regression Spline (MARS), Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), Gaussian Process Regression (GPR), and Adaptive Network Fuzzy Inference System (ANFIS). The present approach employed 2D C⁰ stochastic finite element (FE) model based on the Third Order Shear Deformation Theory (TSDT) in conjunction with MARS, ANN-PSO, GPR, and ANFIS. The TSDT model used eliminates the requirement of shear correction factor owing to the consideration of the actual parabolic distribution of transverse shear stress. Zero transverse shear stress at the top and bottom of the plate is enforced to compute higher-order unknowns. C⁰ FE model makes it commercially viable. Stochastic FE analysis done with Monte Carlo Simulation (MCS) FORTRAN inhouse code, selection of design points using a random variable framework, and soft computing with MARS, ANN-PSO, GPR, and ANFIS is implemented using MATLAB in-house code. Following the random variable frame, design points were selected from the input data generated through Monte Carlo Simulation. A total of four-mode shapes are analyzed in the present study. The comparison study was done to compare present work with results in the literature and they were found in good agreement. The stochastic parameters are Young’s elastic modulus, shear modulus, and the Poisson ratio. Lognormal distribution of properties is assumed in the present work. The current soft computation models shrink the number of trials and were found computationally efficient as the MCS-based FE modelling. The paper presents a comparison of MARS, ANN-PSO, GPR, and ANFIS algorithm performance with the stochastic FE model based on TSDT.

In the present work, for the first time, stochastic normal mode frequency analysis of hybrid angle ply laminated composite skew plate with an opening is attempted by a novel approach. The present novel approach employed 2 D C⁰ stochastic finite element (FE) model based on higher-order shear deformation theory (HSDT) in conjunction with artificial neural network (ANN) and artificial neural network-particle swarm optimization (ANN-PSO). HSDT model used eliminates the requirement of shear correction factor owing to consideration of the actual parabolic distribution of transverse shear stress. Zero transverse shear stress at the top and bottom of the plate is enforced to compute higher-order unknowns. C⁰ FE model makes it commercially viable. Stochastic vibration using FE analysis with Monte Carlo simulation, design of experiment (DOE), and soft computing with ANN, ANN-PSO is implemented. With the DOE method, a total of 128 design points are generated. Since there are no results for the present problem in the literature is available. Hence suitable comparisons have been made and found in the good agreement. The stochastic parameters are Young’s elastic modulus, shear modulus, and the Poisson ratio. Lognormal distribution of properties is assumed in the present work. The present soft computation models shrink the trial and are found computationally in agreement with the Monte Carlo simulation-based finite element modeling (MCS-FEM). Current work reveals that ANN-PSO is a fast and straightforward method with result accuracy close to MCS-FEM, and the future researcher in this area may use ANN-PSO.