Input and state estimation for earthquake-excited building structures using acceleration measurements

Sdiq Taher; Jian Li; Huazhen Fang

doi:10.1117/12.2296881

27 March 2018 Input and state estimation for earthquake-excited building structures using acceleration measurements

Sdiq Taher, Jian Li, Huazhen Fang

Proceedings Volume 10598, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018; 1059827 (2018) https://doi.org/10.1117/12.2296881
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2018, Denver, Colorado, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Sdiq Taher, Jian Li, and Huazhen Fang "Input and state estimation for earthquake-excited building structures using acceleration measurements", Proc. SPIE 10598, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, 1059827 (27 March 2018); https://doi.org/10.1117/12.2296881

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