Data-driven modeling of modal parameters of long-span bridges under environmental and operational variation

Sunjoong Kim, Billie F. Spencer, Ho Kyung Kim, Se Jin Kim, Doyun Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study develops the multivariate model of modal parameters under the high variability of structural responses and environmental conditions. The automated operational modal analysis procedure is implemented by synthesizing the algorithms of output-only system identification and density-based clustering algorithm. The Gaussian Process Regression is applied to accumulated modal estimates as well as corresponding environmental/operational conditions for examining the high degree of nonlinear variation in these monitoring data. The performance of the developed model is demonstrated for one-to-one regressions for multivariate structural health monitoring outputs in the presence of environmental and operational variation.

Original languageEnglish (US)
Title of host publicationIABSE Conference, Seoul 2020
Subtitle of host publicationRisk Intelligence of Infrastructures - Report
PublisherInternational Association for Bridge and Structural Engineering (IABSE)
Pages170-173
Number of pages4
ISBN (Electronic)9783857481758
StatePublished - 2021
EventIABSE Conference Seoul 2020: Risk Intelligence of Infrastructures - Seoul, Korea, Republic of
Duration: Nov 9 2020Nov 10 2020

Publication series

NameIABSE Conference, Seoul 2020: Risk Intelligence of Infrastructures - Report

Conference

ConferenceIABSE Conference Seoul 2020: Risk Intelligence of Infrastructures
CountryKorea, Republic of
CitySeoul
Period11/9/2011/10/20

Keywords

  • Gaussian process regression
  • Multivariate regression
  • Operational modal analysis
  • Structural health monitoring

ASJC Scopus subject areas

  • Building and Construction
  • Civil and Structural Engineering

Fingerprint Dive into the research topics of 'Data-driven modeling of modal parameters of long-span bridges under environmental and operational variation'. Together they form a unique fingerprint.

Cite this