Multi crack detection in structures using artificial neural network

Abstract

Cracks are one of the main causes of structural failure. The detection of cracks is broadly carried out using NDT methods, vibration-based methods, and various mathematical models. The detection of single crack has been widely and importantly studied in recent years. However, the diagnosis of multiple cracks is minimal. In this paper, an alternative way for detecting multiple cracks used is Artificial Neural Networks (ANN) based modeling which is a subfield of artificial intelligence. The evolutions in the ANN have brought up various new potential in the arena of complex problems. In ANN modeling, networks can be built directly from experimental data using its self-organizing capabilities which is the main advantage of using ANN. This paper tries to predict multiple cracks in cantilever beam using soft computing technique. The crack location and crack depth of two cracks are the output parameters and the first three relative natural frequencies are the input parameters to the neural network. The result sets obtained from the finite element analysis (FEA) are used to train the network and the simulated results are obtained. It has been found that the maximum error percentage between the analytical and the ANN outputs is very less which shows that the ANN can well build to predict the characteristics of the multiple crack. This paper proposes a good approach for multiple damage detection in cantilever beam.