A Study on the Long-Term Measurement Data Analysis of Existing Cable Stayed Bridge Using ARX Model

Yunwoo Lee, Minseo Jang, Seungjun Kim, Young Jong Kang

Research output: Contribution to journalArticle

Abstract

Various sensors have been installed in cable-stayed bridges to monitor the behavior of structures and external conditions. These sensors alert the administrator to take appropriate action when an abnormal signal is detected. Although inherent meaningful information about the history of structural responses in long-term accumulated measurement data are available, the methodology for utilizing such data in the long-term point of view has not yet been established. Structural response is determined by the mechanical principle of external loads and the structural system characteristics. Assuming that structural responses have a certain pattern in a constant condition, the state of the structure can be estimated to have changed or not through an analysis of the pattern variation of the measured data. This study utilizes the temperature and displacement data of a cable-stayed bridge to analyze the pattern variation of the measurement data. An autoregressive model is used to define the pattern of the time series data. A pattern model is then constructed with the data adopted as a reference for comparison. The compared data are applied to the pattern model to simulate the data reflecting the reference data pattern. Subsequently, the simulated data are compared with the actual data, and the pattern difference is computed through the error discriminant index.

Original languageEnglish
JournalInternational Journal of Steel Structures
DOIs
Publication statusAccepted/In press - 2020

Keywords

  • Autoregressive model
  • Cable-stayed bridge
  • Data analysis
  • Long term
  • Measurement data
  • Pattern analysis

ASJC Scopus subject areas

  • Civil and Structural Engineering

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