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Structural identification with incomplete output‐only data and independence of measured information for shear‐type systems
Author(s) -
Mukhopadhyay Suparno,
Luş Hilmi,
Betti Raimondo
Publication year - 2016
Publication title -
earthquake engineering and structural dynamics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.218
H-Index - 127
eISSN - 1096-9845
pISSN - 0098-8847
DOI - 10.1002/eqe.2627
Subject(s) - identifiability , modal , operational modal analysis , a priori and a posteriori , identification (biology) , inverse problem , algorithm , control theory (sociology) , system identification , complete information , inverse , vibration , normal mode , structural system , computer science , mathematics , mathematical optimization , modal analysis , engineering , data mining , finite element method , structural engineering , statistics , control (management) , mathematical analysis , measure (data warehouse) , artificial intelligence , geometry , philosophy , mathematical economics , chemistry , biology , epistemology , quantum mechanics , botany , physics , polymer chemistry
Summary Structural identification is the inverse problem of estimating physical parameters of a structural system from its vibration response measurements. Incomplete instrumentation and ambient vibration testing generally result in incomplete and arbitrarily normalized measured modal information, often leading to an ill‐conditioned inverse problem and non‐unique identification results. The identifiability of any parameter set of interest depends on the amount of independent available information. In this paper, we consider the identifiability of the mass and stiffness parameters of shear‐type systems in output‐only situations with incomplete instrumentation. A mode shape expansion‐cum‐mass normalization approach is presented to obtain the complete mass normalized mode shape matrix, starting from the incomplete non‐normalized modes identified using any operational modal analysis technique. An analysis is presented to determine the minimum independent information carried by any given sensor set‐up. This is used to determine the minimum necessary number and location of sensors from the point of view of minimum necessary information for identification. The different theoretical discussions are illustrated using numerical simulations and shake table experiments. It is shown that the proposed identification algorithm is able to obtain reliably accurate physical parameter estimates under the constraints of minimal instrumentation, minimal a priori information, and unmeasured input. The sensor placement rules can be used in experiment design to determine the necessary number and location of sensors on the monitored system. John Wiley & Sons, Ltd.