Announcing the Final Examination of Bryan Paul for the degree of Master of Science Time & Location: March 20, 2013 at 1:00 PM in ENG II 426 Title: ANALYTICAL AND EXPERIMENTAL STUDY OF MONITORING FOR CHAIN-LIKE NONLINEAR DYNAMIC SYSTEMS

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چکیده

Inverse analysis of nonlinear dynamic systems is an important area of research in the field of structural health monitoring for civil engineering structures. Structural damage usually involves localized nonlinear behaviors of dynamic systems that evolve into different classes of nonlinearity as well as change system parameter values. Numerous parametric modal analysis techniques (e.g., eigensystem realization algorithm and subspace identification method) have been developed for system identification of multi-degree-of-freedom dynamic systems. However, those methods are usually limited to linear systems and known for poor sensitivity to localized damage. On the other hand, non-parametric identification methods (e.g., artificial neural networks) are advantageous to identify time-varying nonlinear systems due to unpredictable damage. However, physical interpretation of non-parametric identification results is not as straightforward as those of the parametric methods. In this study, the Multidegree-of-Freedom Restoring Force Method (MRFM) is employed as a semi-parametric nonlinear identification method to take the advantages of both the parametric and non-parametric identification methods.

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Announcing the Final Examination of Bryan Paul for the degree of Master of Science Time & Location: March 20, 2013 at 1:00 PM in ENG II 426 Title: ANALYTICAL AND EXPERIMENTAL STUDY OF MONITORING FOR CHAIN-LIKE NONLINEAR DYNAMIC SYSTEMS

Inverse analysis of nonlinear dynamic systems is an important area of research in the field of structural health monitoring for civil engineering structures. Structural damage usually involves localized nonlinear behaviors of dynamic systems that evolve into different classes of nonlinearity as well as change system parameter values. Numerous parametric modal analysis techniques (e.g., eigensys...

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تاریخ انتشار 2017