Structural Engineering and Mechanics

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A multi-crack effects analysis and crack identification in functionally graded beams using particle swarm optimization algorithm and artificial neural network

  • Abolbashari, Mohammad Hossein (Department of Mechanical Engineering, Lean Production Engineering Research Center, Ferdowsi University of Mashhad) ;
  • Nazari, Foad (Department of Mechanical Engineering, Lean Production Engineering Research Center, Ferdowsi University of Mashhad) ;
  • Rad, Javad Soltani (Mechanical Engineering Department, Amirkabir University of Technology)
  • Received : 2013.02.17
  • Accepted : 2014.05.17
  • Published : 2014.07.25
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Abstract

In the first part of this paper, the influences of some of crack parameters on natural frequencies of a cracked cantilever Functionally Graded Beam (FGB) are studied. A cantilever beam is modeled using Finite Element Method (FEM) and its natural frequencies are obtained for different conditions of cracks. Then effect of variation of depth and location of cracks on natural frequencies of FGB with single and multiple cracks are investigated. In the second part, two Multi-Layer Feed Forward (MLFF) Artificial Neural Networks (ANNs) are designed for prediction of FGB's Cracks' location and depth. Particle Swarm Optimization (PSO) and Back-Error Propagation (BEP) algorithms are applied for training ANNs. The accuracy of two training methods' results are investigated.

Keywords

References

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