Abstract
This paper presents different artificial intelligence (AI) techniques for crack identification in curvilinear beams based on changes in vibration characteristics. Vibration analysis has been performed by applying the finite element method (FEM) to compute natural frequencies and frequency response functions (FRFs) for intact and damaged beams. The analysis reveals the changes in natural frequencies and amplitudes of FRFs of the beams for cracks of different sizes at different locations. These changes are used as input data for single and multiple artificial neural networks (ANN) and multiple adaptive neuro-fuzzy inference systems (ANFIS) in order to predict the size of the crack and its location. To avoid large models, the principal component analysis (PCA) approach has been carried out to reduce the computed FRFs data. The analysis of different techniques shows that the average prediction errors in the multiple ANN models is less than those in the single ANN model and in the multiple ANFIS. It is shown that the cracks longer than 5 mm can be located with satisfactory accuracy, even if the input data are corrupted with various level of noise. Multiple ANFIS is adopted to construct a more reliable and less sensitive model for noise excitation.