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Structural system identification by measurement error‐minimizing observability method
Author(s) -
Lei Jun,
LozanoGalant Jose Antonio,
Xu Dong,
Turmo Jose
Publication year - 2019
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2425
Subject(s) - observability , frame (networking) , rigid frame , rotation (mathematics) , beam (structure) , identification (biology) , finite element method , range (aeronautics) , matrix (chemical analysis) , structural engineering , system identification , structural system , control theory (sociology) , engineering , algorithm , computer science , mathematics , geometry , botany , control (management) , artificial intelligence , biology , telecommunications , materials science , software engineering , data modeling , composite material , aerospace engineering
Summary This paper proposes a method for the finite element model updating using static load tests under the framework of observability analysis. Previous works included measurement errors in the coefficient matrix of the observability equations. This impeded the obtainment of accurate estimations. To deal with this issue, the proposed method relocates the errors and incorporates an optimization procedure to minimize the square sum of these errors. This method is able to identify the structural parameters of complex structures where the axial and bending behaviors are coupled, such as inclined beams or frame structures. Its application is illustrated by three structures. First, the method was validated in a beam‐like structure by comparing it with other methods in the literature. Then the effects of different factors were investigated in a multistory frame and a rigid frame bridge with inclined piers. These factors include the curvatures, the inclusion of rotation measurements, and the constraints on the range of unidentifiable parameters. The importance of rotation measurements is demonstrated in static structural system identification.