Research on delamination monitoring for composite structures based on HHGA-WNN

  • Authors:

  • Shi-jie Zheng;Zheng-qiang Li;Hong-tao Wang

  • Affiliations:

  • Aeronautical Science Key Lab of Smart Materials & Structures, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, PR China;Aeronautical Science Key Lab of Smart Materials & Structures, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, PR China;Aeronautical Science Key Lab of Smart Materials & Structures, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, PR China

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

Due to the deficiencies of the training algorithms for available wavelet neural network used for structural health monitoring, a new hybrid hierarchy genetic algorithm was introduced by combining hierarchy genetic algorithm and least-square method to improve the learning procedure of wavelet neural network. The hybrid algorithm was able to determine the structure and parameters of the wavelet neural network simultaneously. In this algorithm, adaptive crossover and mutation probability were used to accelerate the genetic speed and avoid the occurrence of prematurity. The modal frequencies of a glass/epoxy laminates beam with varying assumed delamination sizes and locations were computed using finite element method and fed into the wavelet neural network to predict the delamination location and its extent. The simulation demonstrates that the wavelet neural network based on hybrid hierarchy genetic algorithm is robust, promising and converges very fast.