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Bayesian prediction of bridge extreme stresses based on DLTM and monitoring coupled data

Auteur(s):

Médium: article de revue
Langue(s): anglais
Publié dans: Structural Health Monitoring, , n. 2, v. 19
Page(s): 454-462
DOI: 10.1177/1475921719853171
Abstrait:

For predicting dynamic coupled extreme stresses of bridges with monitoring coupled data, this article considers monitoring extreme stress data as a time series, and takes into account its coupling generated by the fusion of non-stationarity and randomness. First, the local polynomial theory is introduced, and the local polynomial order of monitoring coupled extreme stress data is estimated with time-series analysis method. Second, based on time-series analysis results, dynamic linear trend models (DLTM) and the corresponding Bayesian probability recursive processes are given to predict dynamic coupled extreme stresses. Finally, through the illustration of monitoring coupled extreme stress data from an actual bridge, the proposed method, which is compared with the traditional Bayesian dynamic linear models, is proved to be more effective for predicting dynamic coupled extreme stresses of bridges.

Structurae ne peut pas vous offrir cette publication en texte intégral pour l'instant. Le texte intégral est accessible chez l'éditeur. DOI: 10.1177/1475921719853171.
  • Informations
    sur cette fiche
  • Reference-ID
    10562304
  • Publié(e) le:
    11.02.2021
  • Modifié(e) le:
    19.02.2021
 
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