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Smart Structures and Systems   Volume 15, Number 3, March 2015, pages 751-767
DOI: https://doi.org/10.12989/sss.2015.15.3.751
 
Probabilistic condition assessment of structures by multiple FE model identification considering measured data uncertainty
Hyun-Joong Kim and Hyun-Moo Koh

 
Abstract     [Buy Article]
    A new procedure is proposed for assessing probabilistic condition of structures considering effect of measured data uncertainty. In this procedure, multiple Finite Element (FE) models are identified by using weighting vectors that represent the uncertainty conditions of measured data. The distribution of structural parameters is analysed using a Principal Component Analysis (PCA) in relation to uncertainty conditions, and the identified models are classified into groups according to their similarity by using a K-means method. The condition of a structure is then assessed probabilistically using FE models in the classified groups, each of which represents specific uncertainty condition of measured data. Yeondae bridge, a steel-box girder expressway bridge in Korea, is used as an illustrative example. Probabilistic condition of the bridge is evaluated by the distribution of load rating factors obtained using multiple FE models. The numerical example shows that the proposed method can quantify uncertainty of measured data and subsequently evaluate efficiently the probabilistic condition of bridges.
 
Key Words
    condition assessment; FE Model update; measurement uncertainty; principal component analysis; K-means clustering; load rating
 
Address
Hyun-Joong Kim and Hyun-Moo Koh: Department of Civil and Environmental Engineering, Seoul National University,
1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea
 

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