Bayesian approach for characterizing wind-induced displacement responses of bridge using structural health monitoring data

Yiqing Ni, Y. W. Wang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

This paper presents an approach for quantifying the uncertainty in assessing wind-induced displacement responses of bridge with the data acquired from a Structural Health Monitoring (SHM) system. Both the wind speed and wind direction are served as quantitative explanatory variables in the Bayesian regression model to account for the wind-induced displacement responses. The parameters of the model as well as the uncertainties of the parameters and the model are identified by using the Markov Chain Monte Carlo (MCMC) method. To validate the performance of the formulated regression model, a new set of data is fed into the model for testing. The proposed approach is illustrated by applying it to the monitoring data acquired from a long-span suspension bridge − the Tsing Ma Bridge. Through comparing the Root Mean Square Errors (RMSE) in training and testing phases, it is demonstrated that the Bayesian model performs comparably well in the case of unseen data.
Original languageEnglish
Title of host publicationMechanics of Structures and Materials
Subtitle of host publicationAdvancements and Challenges - Proceedings of the 24th Australasian Conference on the Mechanics of Structures and Materials, ACMSM24 2016
PublisherCRC Press/Balkem
Pages1395-1400
Number of pages6
ISBN (Print)9781138029934
Publication statusPublished - 1 Jan 2017
Event24th Australasian Conference on the Mechanics of Structures and Materials, ACMSM 2016 - Perth, Australia
Duration: 6 Dec 20169 Dec 2016

Conference

Conference24th Australasian Conference on the Mechanics of Structures and Materials, ACMSM 2016
Country/TerritoryAustralia
CityPerth
Period6/12/169/12/16

ASJC Scopus subject areas

  • Mechanics of Materials
  • Civil and Structural Engineering

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