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Damage Identification of Multimember Structure using Improved Neural Networks

Author

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  • M. Rajendra

    (Mechanical Engineering Department, IIT Madras, Chennai, Tamil Nadu, India)

  • K. Shankar

    (Mechanical Engineering Department, IIT Madras, Chennai, Tamil Nadu, India)

Abstract

A novel two stage Improved Radial Basis Function (IRBF) neural network for the damage identification of a multimember structure in the frequency domain is presented. The improvement of the proposed IRBF network is carried out in two stages. Conventional RBF network is used in the first stage for preliminary damage prediction and in the second stage reduced search space moving technique is used to minimize the prediction error. The network is trained with fractional frequency change ratios (FFCs) and damage signature indices (DSIs) as effective input patterns and the corresponding damage severity values as output patterns. The patterns are searched at different damage levels by Latin hypercube sampling (LHS) technique. The performance of the novel IRBF method is compared with the conventional RBF and Genetic algorithm (GA) methods and it is found to be a good multiple member damage identification strategy in terms of accuracy and precision with less computational effort.

Suggested Citation

  • M. Rajendra & K. Shankar, 2013. "Damage Identification of Multimember Structure using Improved Neural Networks," International Journal of Manufacturing, Materials, and Mechanical Engineering (IJMMME), IGI Global, vol. 3(3), pages 57-75, July.
    • Handle: RePEc:igg:jmmme0:v:3:y:2013:i:3:p:57-75
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