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Self-regularized pseudo time-marching schemes for structural system identification with static measurements

Banerjee, B and Roy, D and Vasu, RM (2010) Self-regularized pseudo time-marching schemes for structural system identification with static measurements. In: International Journal for Numerical Methods in Engineering, 82 (7). pp. 896-916.

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Official URL: http://www3.interscience.wiley.com/journal/1231897...


We explore the application of pseudo time marching schemes, involving either deterministic integration or stochastic filtering, to solve the inverse problem of parameter identification of large dimensional structural systems from partial and noisy measurements of strictly static response. Solutions of such non-linear inverse problems could provide useful local stiffness variations and do not have to confront modeling uncertainties in damping, an important, yet inadequately understood, aspect in dynamic system identification problems. The usual method of least-square solution is through a regularized Gauss-Newton method (GNM) whose results are known to be sensitively dependent on the regularization parameter and data noise intensity. Finite time, recursive integration of the pseudo-dynamical GNM (PD-GNM) update equation addresses the major numerical difficulty associated with the near-zero singular values of the linearized operator and gives results that are not sensitive to the time step of integration. Therefore, we also propose a pseudo-dynamic stochastic filtering approach for the same problem using a parsimonious representation of states and specifically solve the linearized filtering equations through apseudo-dynamic ensemble Kalman filter (PD-EnKF). For multiple sets ofmeasurements involving various load cases, we expedite the speed of the PD-EnKF by proposing an inner iteration within every time step. Results using the pseudo-dynamic strategy obtained through PD-EnKF and recursive integration are compared with those from the conventional GNM, which prove that the PD-EnKF is the best performer showing little sensitivity to process noise covariance and yielding reconstructions with less artifacts even when the ensemble size is small. Copyright (C) 2009 John Wiley & Sons, Ltd.

Item Type: Journal Article
Publication: International Journal for Numerical Methods in Engineering
Publisher: John Wiley and Sons
Additional Information: Copyright of this article belongs to John Wiley and Sons.
Keywords: structural system identification; inverse problems; Gauss-Newton method; pseudo-dynamical systems; ensemble Kalman filter
Department/Centre: Division of Physical & Mathematical Sciences > Instrumentation Appiled Physics
Division of Mechanical Sciences > Civil Engineering
Date Deposited: 26 May 2010 07:02
Last Modified: 19 Sep 2010 06:07
URI: http://eprints.iisc.ac.in/id/eprint/28056

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