Abstract
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.
Highlights
Initial imperfections of structural members could be classified into three main categories: geometric imperfections, fluctuations in mechanical properties of material, and residual stresses [1].These imperfections directly result from manufacturing processes [2] or after the assembly of parts [3].Appl
The adaptive neuro-fuzzy inference system (ANFIS) is an artificial intelligence-based algorithm developed by Jang [56] in the early 1990s
By combining features of artificial neural networks (ANN) [57,58,59] along with fuzzy logic (FL) principles, the method has a powerful potential for predicting complex non-linear processes [60,61]
Summary
Initial imperfections of structural members could be classified into three main categories: geometric imperfections, fluctuations in mechanical properties of material, and residual stresses [1].These imperfections directly result from manufacturing processes [2] or after the assembly of parts [3].Appl. Initial imperfections of structural members could be classified into three main categories: geometric imperfections, fluctuations in mechanical properties of material, and residual stresses [1]. These imperfections directly result from manufacturing processes [2] or after the assembly of parts [3]. As reported in many experimental studies, imperfections are crucial for structural elements regarding their initial design, especially for those under compression [4,5,6].
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