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

Mohammad Hossein Abolbashari, Foad Nazari and Javad Soltani Rad

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.

Key Words

multiple cracks; functionally graded beam; artificial neural network; particle swarm optimization; identification

Address

Mohammad Hossein Abolbashari, Foad Nazari : Department of Mechanical Engineering, Lean Production Engineering Research Center, Ferdowsi University of Mashhad, PO Box 91775-1111, Mashhad, Iran Javad Soltani Rad : Mechanical Engineering Department, Amirkabir University of Technology, Po Box 15875-4413, Tehran, Iran

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