Journals Proceedings

Abstract

Online structural health monitoring becomes as a promising tool to ensure of the safety of the structure with low cost, short time and high effectiveness. Existing of crack in the structure makes the structure weaker and unsafe and it may propagate to complete fracture and catastrophic failure. Different methods are developed to predict the location and depth of a crack. In this paper, Artificial Neural Network (ANN) theory is used to predict the generated data for different crack locations and crack depth based on changes in natural frequencies and mode shapes of a healthy structure. The ANSYS software based on finite element (FE) principles is used to generate data for solid or fibre reinforced composite cantilever (and simply supported) beams. Natural frequencies for different important vibration modes are obtained based on linear vibration analysis. The effects of ply orientation, crack location, crack depth and end supports on the natural frequencies and corresponding mode shapes are investigated. Results of ANSYS software was first compared with some well-known theoretical cases for verification purpose and then results of artificial neural network (ANN) are compared with ANSYS software generated data. The results indicate high accuracy of ANN on predicting size and location of cracks in the studied structures.