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Nonparametric Structural Mass and Hysteretic Behavior Simultaneous Identification Using Limited Acceleration Measurement

BIN XU, JING LI, BAI-CHUAN DENG

Abstract


Identifying damage initiation and development in engineering structures in the form of hysteretic behavior or nonlinear restoring force (NRF) after strong dynamic loading in a nonparametric way is attractive because it is challenging to assume a general parametric model describing the nonlinear behavior of various engineering structures in prior due to their individuality. Although traditional extended Kalman filter (EKF) is efficient in state vector estimation and structural parameter identification using partially available output measurements, a known structural mass is usually required. In this study, a simultaneous structural hysteretic behavior and mass identification approach is developed for multi-degree-of-freedom (MDOF) structures using the EKF with weighted global iteration (EKF-WGI) based on limited available acceleration response. The NRF is modeled with a power series polynomial model (PSPM) as a nonparametric function of unknown structural displacement and velocity responses to be estimated with EKF-WGI. Then, the performance of the approach is numerically illustrated with multi-story structures equipped with two magneto-rheological (MR) dampers using known applied excitations, partially available noise-contaminated acceleration measurements but unknown mass. The effect of different noise levels and structural mass initial estimation errors on both NRF and mass identification results as well as the convergence of the approach are investigated.


DOI
10.12783/shm2019/32507

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