Table of Contents
Advances in Artificial Neural Systems
Volume 2013, Article ID 150209, 8 pages
http://dx.doi.org/10.1155/2013/150209
Research Article

Inverse Analysis of Crack in Fixed-Fixed Structure by Neural Network with the Aid of Modal Analysis

1Department of Mechanical Engineering, I.T.E.R, Bhubaneswar 751030, Odisha, India
2Department of Mechanical Engineering, Silicon Institute of Technology, Bhubaneswar 751024, Odisha, India

Received 6 December 2012; Accepted 11 February 2013

Academic Editor: Kyong Joo Oh

Copyright © 2013 Dhirendranath Thatoi and Prabir Kumar Jena. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In this research, dynamic response of a cracked shaft having transverse crack is analyzed using theoretical neural network and experimental analysis. Structural damage detection using frequency response functions (FRFs) as input data to the back-propagation neural network (BPNN) has been explored. For deriving the effect of crack depths and crack locations on FRF, theoretical expressions have been developed using strain energy release rate at the crack section of the shaft for the calculation of the local stiffnesses. Based on the flexibility, a new stiffness matrix is deduced that is subsequently used to calculate the natural frequencies and mode shapes of the cracked beam using the neural network method. The results of the numerical analysis and the neural network method are being validated with the result from the experimental method. The analysis results on a shaft show that the neural network can assess damage conditions with very good accuracy.