XIAO Shu-min, YAN Yun-ju, JIANG Bo-lan. Damage Identification for Bridge Structures Based on the Wavelet Neural Network Method[J]. Applied Mathematics and Mechanics, 2016, 37(2): 149-159. doi: 10.3879/j.issn.1000-0887.2016.02.004
Citation: XIAO Shu-min, YAN Yun-ju, JIANG Bo-lan. Damage Identification for Bridge Structures Based on the Wavelet Neural Network Method[J]. Applied Mathematics and Mechanics, 2016, 37(2): 149-159. doi: 10.3879/j.issn.1000-0887.2016.02.004

Damage Identification for Bridge Structures Based on the Wavelet Neural Network Method

doi: 10.3879/j.issn.1000-0887.2016.02.004
  • Received Date: 2015-07-20
  • Rev Recd Date: 2015-10-23
  • Publish Date: 2016-02-15
  • Bridge structures will suffer complex loading environment during their service, and inevitable damages of structures may occur in long term service. If a damage can’t be found in time and treated properly, it may cause serious accidents. Therefore, the local small damage identification for bridge structures is of great significance for the timely maintenance. Generally, the measured global dynamic properties of a damaged structure are not sensitive enough to the local structural damages, especially to small damages, thus, it is necessary to extract more sensitive feature information to structural damages from the structural dynamic response signals. The finite element model of a bridge structure was established, and the dynamic characteristics were analyzed. The wavelet packet analysis was used to process the structural dynamic response signals and the structural damage index was presented. Then the damages of the bridge structure were positioned according to the damage index with the artificial wavelet neural network method. The results show that the wavelet packet energy change ratio makes an effective damage index; the properly trained neural network can fairly precisely position the bridge structural damages in the numerical test; the higher the damage degree is, the lower the positioning error comes.
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