Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Artificial Neural Networks for Civil Engineers: Fundamentals and Applications
Artificial Neural Networks for Civil Engineers: Fundamentals and Applications
Finite element analysis for fracture behavior of cracked beam-columns
Finite Elements in Analysis and Design
Concrete breakout strength of single anchors in tension using neural networks
Advances in Engineering Software
Modeling and control of non-linear systems using soft computing techniques
Applied Soft Computing
Comparison of critical column buckling load in regression, fuzzy logic and ANN based estimations
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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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.