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Bridge Damage Severity Quantification Using Multipoint Acceleration Measurement and Artificial Neural Networks

Author(s):


Medium: journal article
Language(s): English
Published in: Shock and Vibration, , v. 2015
Page(s): 1-11
DOI: 10.1155/2015/789384
Abstract:

The deterioration of bridges as a result of ageing is a serious problem in many countries. To prevent the failure of these deficient bridges, early damage detection which helps us to evaluate the safety of bridges is important. Therefore, the present research proposed a method to quantify damage severity by use of multipoint acceleration measurement and artificial neural networks. In addition to developing the method, we developed a cheap and easy-to-make measurement device which can be made by bridge owners at low cost and without the need for advance technical skills since the method is mainly intended to apply to small to midsized bridges. In addition, the paper gives an example application of the method to a weathering steel bridge in Japan. It can be shown from the analysis results that the method is accurate in its damage identification and mechanical behavior prediction ability.

Copyright: © 2015 Pang-jo Chun, Hiroaki Yamashita, Seiji Furukawa
License:

This creative work has been published under the Creative Commons Attribution 3.0 Unported (CC-BY 3.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10676355
  • Published on:
    28/05/2022
  • Last updated on:
    01/06/2022
 
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