Comparison of ANFIS and NN models—With a study in critical buckling load estimation

The investigation of the effects of cracks or similar weaknesses on the load carrying capacity of structural elements such as columns, beams and shells is an important problem in civil, mechanical, earthquake and aerospace engineering. In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) model have been successfully used for the buckling analysis of slender prismatic columns with a single non-propagating open edge crack subjected to axial loads. The main focus of this work has been to study the feasibility of using ANFIS and neural network (NN) trained with the non-dimensional crack depth and the non-dimensional crack location parameters to predict the critical buckling load of fixed-free, pinned-pinned, fixed-pinned and fixed-fixed supported, axially loaded compression rods. A comparative study is made using the neural nets and neuro-fuzzy techniques. Statistic measures were used to evaluate the performance of the models. Based on comparison of the results it is found that the proposed ANFIS architecture with Gaussian membership function is found to perform better than the multilayer feed forward ANN learning by backpropagation algorithm. The final results show that especially the ANFIS modeling may constitute an efficient tool for elastic buckling analysis of edge cracked columns. Architectures of the ANFIS and NN established in the current study perform sufficiently in the estimation of critical buckling loads, and particularly the ANFIS estimates closely follow the actual values for the whole data sets. These model architectures can be used as a non-destructive procedure for health monitoring of structural elements.