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Bayesian Nonparametric Modeling of Structural Health Indicators under Severe Typhoons and Its Application to Modeling Modal Frequency
Sin-Chi Kuok1,2; Ka-Veng Yuen1
2019-04-02
Source PublicationJournal of Aerospace Engineering
ISSN0893-1321
Volume32Issue:4Pages:04019036
Abstract

Structural health indicators, such as modal frequencies, have been commonly utilized to interpret the health condition of monitored structures. This study modeled the relationship between structural health indicators and ambient conditions under severe typhoons. For this purpose, a two-stage Bayesian probabilistic procedure was established. In the first stage, the Bayesian spectral density approach (BSDA) is applied to identify the structural health indicators, namely the modal frequencies in this study, using the measured structural response. In the second stage, the Bayesian nonparametric general regression (BNGR) is introduced to model the relationship between the identified structural health indicators and some selected typhoon-induced ambient conditions. By using Bayesian model selection in conjunction with general regression, BNGR is able to select the most appropriate set of influencing/input variables for the prediction of the structural health indicators without prescribing any functional form. Full-scale measurements of a 22-story reinforced concrete (RC) building were used to demonstrate the efficacy of the procedure. The measurements consisted of over 280 h of structural response and the corresponding ambient conditions captured under the five most severe tropical cyclones that affected the region from 2011 to 2013. This study provides a promising framework for reliable interpretation of the variation of structural health indicators. Although the modal frequencies were considered in this study, the proposed two-stage procedure is applicable for other structural health indicators.

KeywordBayesian Inference Structural Health Monitoring Model Selection Nonparametric Modeling Severe Typhoons
DOI10.1061/(ASCE)AS.1943-5525.0001023
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Aerospace ; Engineering, Civil
WOS IDWOS:000482166000013
PublisherASCE-AMER SOC CIVIL ENGINEERS, 1801 ALEXANDER BELL DR, RESTON, VA 20191-4400
Scopus ID2-s2.0-85063766653
Fulltext Access
FWCI0.96798986
Citation statistics
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorKa-Veng Yuen
Affiliation1.Faculty of Science and Technology,State Key Laboratory of Internet of Things for Smart City,Univ. of Macau,999078,Macao
2.Dept. of Engineering Science,Univ. of Oxford,Oxford,OX1 2JD,United Kingdom
First Author AffilicationFaculty of Science and Technology
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Sin-Chi Kuok,Ka-Veng Yuen. Bayesian Nonparametric Modeling of Structural Health Indicators under Severe Typhoons and Its Application to Modeling Modal Frequency[J]. Journal of Aerospace Engineering,2019,32(4):04019036.
APA Sin-Chi Kuok,&Ka-Veng Yuen.(2019).Bayesian Nonparametric Modeling of Structural Health Indicators under Severe Typhoons and Its Application to Modeling Modal Frequency.Journal of Aerospace Engineering,32(4),04019036.
MLA Sin-Chi Kuok,et al."Bayesian Nonparametric Modeling of Structural Health Indicators under Severe Typhoons and Its Application to Modeling Modal Frequency".Journal of Aerospace Engineering 32.4(2019):04019036.
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