Abstract
In this paper, a loop substructure identification method is proposed to estimate the parameters of any story in a shear structure with measurements of only limited number of acceleration floors and unknown structural mass. A shear structure is divided into substructures consisting of a series of similar two-story standard substructures; two identification problems are formulated for the standard substructure using the cross power spectral densities (CPSD) of structural responses, each of which identifies the parameters of one story given that the parameters of the other are known. A loop identification scheme is proposed by connecting the two identification problems in a loop manner, forming a sequence of estimation problems to directly identify both story parameters of the standard substructure. If the structural masses are unknown, this loop identification method can still be applied to estimate mass normalized structural parameters as well as the relative mass distribution of the structure. The convergence condition is derived for the loop substructure identification, showing that the loop identification sequence is conditionally converged and some structural responses play a crucial role in determining the convergence. To achieve convergent identification results, a reference selection method is proposed, which uses a synthesized response, formed by a linear combination of the measured structural responses, as the reference response to calculate the CPSD and perform the loop substructure identification. A 20-story shear building is used to verify the convergence condition and to demonstrate that the proposed reference selection method does provide the converged and accurate estimation results.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.