Comparison of Artificial Neural Networks and Fuzzy Logic Approaches for Crack Detection in a Beam Like Structure

Abstract

This paper proposes two algorithms of crack detection one using fuzzy logic (FL) and the other artificial neural networks (ANN). Since modal parameters are very sensitive to damages, the first three relative natural frequencies are used as three inputs and the corresponding relative crack location, relative crack depth are used as the two outputs in the algorithms. The three natural frequencies for an undamaged beam and different cases of damaged beam (Single crack at various locations with varying depths) were obtained by modelling and simulating the beams using a finite element based (FEM) software. Results concluded that both the approaches can be successfully employed in crack detection in a beam like structure but FL approach performed better in determining relative crack depth whereas ANN approach performed better in determining relative crack location. All the comparisons made in the study are based on the R 2 values.