Smart Structures and Systems

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Structural damage identification based on genetically trained ANNs in beams

  • Li, Peng-Hui (Hubei Key Laboratory of Control Structure, School of Civil Engineering and Mechanics, Huazhong University of Science and Technology) ;
  • Zhu, Hong-Ping (Hubei Key Laboratory of Control Structure, School of Civil Engineering and Mechanics, Huazhong University of Science and Technology) ;
  • Luo, Hui (Hubei Key Laboratory of Control Structure, School of Civil Engineering and Mechanics, Huazhong University of Science and Technology) ;
  • Weng, Shun (Hubei Key Laboratory of Control Structure, School of Civil Engineering and Mechanics, Huazhong University of Science and Technology)
  • Received : 2014.01.09
  • Accepted : 2014.05.25
  • Published : 2015.01.25
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Abstract

This study develops a two stage procedure to identify the structural damage based on the optimized artificial neural networks. Initially, the modal strain energy index (MSEI) is established to extract the damaged elements and to reduce the computational time. Then the genetic algorithm (GA) and artificial neural networks (ANNs) are combined to detect the damage severity. The input of the network is modal strain energy index and the output is the flexural stiffness of the beam elements. The principal component analysis (PCA) is utilized to reduce the input variants of the neural network. By using the genetic algorithm to optimize the parameters, the ANNs can significantly improve the accuracy and convergence of the damage identification. The influence of noise on damage identification results is also studied. The simulation and experiment on beam structures shows that the adaptive parameter selection neural network can identify the damage location and severity of beam structures with high accuracy.

Keywords

Acknowledgement

Supported by : National Natural Science Fund of China

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