The main aim of this study is to develop different hybrid artificial intelligence (AI) approaches, such as an adaptive neuro-fuzzy inference system (ANFIS) and two ANFISs optimized by metaheuristic techniques, namely simulated annealing (SA) and biogeography-based optimization (BBO) for predicting the critical buckling load of structural members under compression, taking into account the influence of initial geometric imperfections. With this aim, the existing results of compression tests on steel columns were collected and used as a dataset. Eleven input parameters, representing the slenderness ratios and initial geometric imperfections, were considered. The predicted target was the critical buckling load of columns. Statistical criteria, namely the correlation coefficient (R), the root mean squared error (RMSE), and the mean absolute error (MAE) were used to evaluate and validate the three proposed AI models. The results showed that SA and BBO were able to improve the prediction performance of the original ANFIS. Excellent results using the BBO optimization technique were achieved (i.e., an increase in R by 7.15%, RMSE by 40.48%, and MAE by 38.45%), and those using the SA technique were not much different (i.e., an increase in R by 5.03%, RMSE by 26.68%, and MAE by 20.40%). Finally, sensitivity analysis was performed, and the most important imperfections affecting column buckling capacity was found to be the initial in-plane loading eccentricity at the top and bottom ends of the columns. The methodology and the developed AI models herein could pave the way to establishing an advanced approach to forecasting damages of columns under compression.

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The main purpose of this article is to present analytical solutions for bending, buckling and free vibration analysis of cylindrical panel, which are composed of functionally graded materials (FGMs). Equations of motion are derived using Hamilton's principle. The first-order shear deformation theory is used for developing Navier's solutions of simply supported cylindrical panel. Comparison studies are presented to verify the validity of present solution. It is found that the presented results are close to those existing. The effect of volume fraction distributions, panel aspect ratio, and side-to-thickness ratio on the deflections, buckling loads and natural frequencies is also investigated.

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This paper presents free vibration analysis of functionally graded materials (FGMs) shell panels with various geometric shapes in thermal environments. The shell panels are made from a mixture of metal and ceramic. Material properties are assumed to be temperature-dependent and graded in the thickness direction according to a power law function. A formulation of eight-nodded middle surface shell elements based on Reissner-Mindlin assumptions is developed for modeling FGM shell panels under the effect of temperature, which changes nonlinearly across the thickness. Numerical results obtained by the proposed model are in good agreement with those available in the literature. The effects of geometric properties, material composition, boundary conditions and temperature on the natural frequencies are investigated.