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2016 | OriginalPaper | Chapter

17. Inelastic Base Shear Reconstruction from Sparse Acceleration Measurements of Buildings

Authors : Boya Yin, Henri Gavin

Published in: Dynamics of Civil Structures, Volume 2

Publisher: Springer International Publishing

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Abstract

This paper presents a novel method for recovering base shear forces of building structures with unknown nonlinearities from sparse seismic-response measurements of floor accelerations. The method requires only direct matrix calculations (factorizations and multiplications); no iterative trial-and-error methods are required. The method requires a mass matrix, or at least an estimate of the floor masses. A stiffness matrix may be used, but is not necessary. Essentially, the method operates on a matrix of incomplete measurements of floor accelerations. In the special case of complete floor measurements of systems with linear dynamics and real modes, the principal components of this matrix are the modal responses. In the more general case of partial measurements and nonlinear dynamics, the method extracts a number of linearly-dependent components from Hankel matrices of measured horizontal response accelerations, assembles these components row-wise and extracts principal components from the singular value decomposition of this large matrix of linearly-dependent components. These principal components are then interpolated between floors in a way that minimizes the curvature energy of the interpolation. This interpolation step can make use of a reduced-order stiffness matrix, a backward difference matrix or a central difference matrix. The measured and interpolated floor acceleration components at all floors are then assembled and multiplied by a mass matrix. A sum (or weighted sum) of the resulting vector of inertial forces gives the base shear. The proposed algorithm is suitable for linear and nonlinear hysteretic structural systems.

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Metadata
Title
Inelastic Base Shear Reconstruction from Sparse Acceleration Measurements of Buildings
Authors
Boya Yin
Henri Gavin
Copyright Year
2016
DOI
https://doi.org/10.1007/978-3-319-29751-4_17