• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 30 Issue 5
Sep.  2017
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Article Contents
ZHANG Yunshi, ZHANG Nan, FAN Hua, CAO Yanmei, XIA He. Method to Predict Vibrations Induced in Ancient Wooden Structures by Traffic Loads and its Validation[J]. Journal of Southwest Jiaotong University, 2017, 30(5): 902-909. doi: 10.3969/j.issn.0258-2724.2017.05.009
Citation: ZHANG Yunshi, ZHANG Nan, FAN Hua, CAO Yanmei, XIA He. Method to Predict Vibrations Induced in Ancient Wooden Structures by Traffic Loads and its Validation[J]. Journal of Southwest Jiaotong University, 2017, 30(5): 902-909. doi: 10.3969/j.issn.0258-2724.2017.05.009

Method to Predict Vibrations Induced in Ancient Wooden Structures by Traffic Loads and its Validation

doi: 10.3969/j.issn.0258-2724.2017.05.009
  • Received Date: 19 Jul 2016
  • Publish Date: 25 Oct 2017
  • The aim of this work is to establish a method for predicting vibrations induced in ancient wooden structures by traffic loads. First, a field test was performed at the Buddhist sutra depositary at Yangzhou Zhunti Temple, based on the prediction method. The modal parameters were identified from the experimental data, and the FE model was established using ANSYS. Next, the efficiency of the prediction method was validated by comparing the predicted values of the traffic load-induced vibrations of the Buddhist sutra depositary with the corresponding experimental data. Finally, the vibration response of the Buddhist sutra depositary under the influence of heavy traffic load moving at high speed was predicted. The comparison between experimental data and numerical calculations reveals that the relative error between the predicted value and the experimental data of the peak acceleration induced by a typical vehicle is as small as 1.47%. This indicates that the vibration response of a certain structure under the influence of traffic load can be effectively predicted by the proposed method.

     

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