In this study, an effective intelligent system based on artificial neural networks (ANN) and a new version of the sine cosine algorithm (SCA) is developed to evaluate and predict the FOS of homogenous slopes under static and dynamic loading. In the first step, an effective hybrid optimization algorithm based on the adaptive sine cosine algorithm (ASCA) and pattern search (PS), namely ASCPS, is proposed and verified using a set of benchmark test functions. Then, the new algorithm, along with the Morgenstern and Price method is applied for seismic slope stability evaluation. To provide a neural network training dataset, a set of 189 slopes with different values of slope height, slope angle, friction angle of soil, soil cohesion, and horizontal acceleration coefficient have been analyzed and their corresponding FOS have been recorded. In the next step, the proposed ASCPS algorithm is implemented for training the ANN model using the collected database. The performance and prediction capacity of the developed model are evaluated using root mean square error (RMSE) and correlation coefficient (R). According to the obtained results, the ANN model with the RMSE value of 0.023 and the R value of 0.984 is a reliable, simple, and valid computational model for estimating the FOS and evaluating the slope stability under static and earthquake loads. In addition, the developed ANN model is applied to a case study of slope stability from previous studies, and the results reveal that the proposed model may provide better optimal solutions and outperform existing methods.

This is the first research on the thermal buckling analysis of graphene nanoplatelets reinforced composite (GPLRC) doubly curved open cylindrical micropanel in the framework of numerical-based two-dimensional generalized differential quadrature method (2D-GDQM). Additionally, the small-scale effects are analyzed based on nonlocal strain gradient theory (NSGT). The stresses and strains are obtained using the high-order shear deformable theory (HOSDT). The rule of mixture is employed to obtain varying thermal expansion, and Poisson’s ratio, while module of elasticity is computed by modified Halpin–Tsai model. In addition, nonlinear temperature changes along the GPLRC micropanel’s thickness direction. Governing equations and boundary conditions of the GPLRC doubly curved open cylindrical micropanel are obtained by implementing the extended Hamilton’s principle. Besides, for the validation of the results, the results of current model are compared to the results acquired from analytical method. The results show that GPL weight function ([Formula: see text], the ratio of shell curvatures ([Formula: see text]/[Formula: see text], NSG parameters, and geometric parameters have a significant influence on the thermal buckling characteristics of the GPLRC doubly curved open cylindrical micropanel.

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