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.

This work proposes a rapid and robust machine-learning model to predict the volumetric error of a five-axis machine tool. For this purpose, several machine learning models – which are MultiOutput regression, Chained MultiOutput regression, Linear regression, SVM regression, XGB regression and ANN regression – have been selected, and their performances were compared to other models in the literature and to one another to find the best model for the problem at hand. The robustness of each model is investigated using three statistical metrics: namely Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Coefficient of Determination (R²). The results show that the models proposed in this paper are more effective than those in the literature, using the same data. Amongst the proposed models, the SVR Regression model has proven to be the best, considering all statistical metrics. Compared to the polynomial method in the literature with varying order levels, the method proposed here improves accuracy and predictive performance by 27%, with an RMSE of 0.03246 mm for the SVR model. Finally, based on the best model, an explicit equation has been deduced for practical applications. That prediction equation has been implemented in Excel and included in this paper.

The purpose of the paper is to develop an analytical investigation on free vibration of a simply supported functionally graded (FG) doubly curved shell panels resting on elastic foundation in thermal environment. Heat conduction and temperature-dependent material properties are both taken into account. The temperature field considered is assumed to be a uniform distribution over the shell surface and varied in the thickness direction only. Material properties are assumed to be temperature dependent and graded in the thickness direction according to a simple power law distribution in terms of the volume fractions of the constituents. Based on the first-order shear deformation theory and applying the Hamilton’s principle, governing equations of motion are derived. The results of the study are compared with the available published literature. The numerical results obtained reveal that the material volume fraction index, geometrical parameters and temperature change have significant effects on natural frequencies of the FG doubly curved shell panels.