Paper Title:
Prediction of Chaotic Time Series of Bridge Monitoring System Based on Multi-Step Recursive BP Neural Network
  Abstract

BHM is an important means to assess and predict the safety operation of large bridge in service around the world. Given the missing of real-time monitoring information for some time and the lack of effective theory and technique to capture the missing information and even to predict the evolution of structure, this paper made an attempt to predict the evolution of monitoring information using time series and chaotic theory. Firstly, maximum Lyapunov exponent of available monitoring information is calculated to assess the chaos of the bridge structure. The parameters of reconstructed phase space, correlation dimension and time delay, are calculated by C-C algorithm and G-P algorithm respectively. According to empirical formula, one 3-layer BP neural network is established Ten recursions are carried out. The results show that multi-layer recursive BP neural network is able to predict BHM information. Using chaotic time series to reconstruct phase space and applying multi-layer recursive BP neural network to predict BHM information facilitates further estimation and prediction of bridge safety condition by means of chaotic nonlinear characteristics.

  Info
Periodical
Edited by
Dehuai Zeng
Pages
138-143
DOI
10.4028/www.scientific.net/AMR.159.138
Citation
J. X. Yang, J. T. Zhou, "Prediction of Chaotic Time Series of Bridge Monitoring System Based on Multi-Step Recursive BP Neural Network", Advanced Materials Research, Vol. 159, pp. 138-143, 2011
Online since
December 2010
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