This paper proposes online input, state, and response estimation based on Augmented Kalman filter for systems without direct feedthrough, such as earthquake-excited building structures with absolute floor acceleration measurements. Measurement noise, modelling error, and incomplete absolute acceleration measurement are considered. The system model in this case lacks direct feedthrough, resulting in weak observability of system input, for which a small uncertainty in the model and measurement data would lead to a drastic change in the estimation. The augmented state Kalman filter for system without direct feedthrough is proposed for earthquake-excited building structures, in which the input with known variance is augmented with states in order to estimate them together. Compared with unbiased minimum-variance input and state estimation methods that make no assumption of input, the proposed online approach is able to perform robust estimation of states, input, and responses at unmeasured locations successfully using only a limited number of absolute acceleration measurements.
Estimating both state and ground input for earthquake-excited building structures using a limited number of absolute acceleration measurements is critical to post-disaster damage assessment and structural evaluation. Input estimation in this case is particularly challenging due to the lack of direct feedthrough term, which renders the system weakly observable for its input. Hence, input estimation in this scenario is sensitive to modeling error and measurement noise. In this paper, a two-step strategy is proposed to estimate both state (displacement and velocity) and ground input using a limited number of absolute acceleration measurements for building structures. First, the ground input is estimated by solving a least squares problem with Tikhonov regularization and Bayesian inference. In the second step, floor states are estimated using Kalman filter with input obtained from the first step, the least squares with Tikhonov regularization and Bayesian inference. The proposed strategy was numerically evaluated based on a sheartype building structure.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.