Date of Award

Spring 2017

Degree Name

Master of Science in Engineering

Department

Civil and Construction Engineering

First Advisor

Dr. Xiaoyun Shao

Second Advisor

Dr. Osama Abudayyeh

Third Advisor

Dr. Jiansong Zhang

Keywords

Implementing online updating, complex hysteresis models, real-time hybrid simulation, CUKF (Constrained Unscented Kalman Filter)

Access Setting

Masters Thesis-Open Access

Abstract

Although conventional experimental methods, such as quasi-static testing, shake table testing, etc., perform well to evaluate structural seismic performance necessary for modern seismic design, the quasi-static loading and/or reduced scale specimen used in shake table testing may not provide true dynamic responses of structural systems. To overcome these limitations, real-time hybrid simulation (RTHS) was developed, which is a combination of physical testing of the experimental substructure and analytical simulation of the numerical substructure. With the advancement of hybrid simulation, it is now possible to economically test large to full-scale specimens in laboratories at the real earthquake rate. Online model updating of the numerical substructure model was recently adopted in RTHS for enhanced reliability and accuracy. Unscented Kalman Filter (UKF) and Constrained UKF (CUKF), that can estimate unknown variables using measurements, have been utilized to identify parameters of numerical models in RTHS. This thesis starts with the study of individual parameter’s effect on the Bouc-Wen (BW) and Bouc-Wen-Baber-Noori (BWBN) models hysteresis behavior. To facilitate implementation of RTHS with online Model Updating (RTHSMU), numerical simulations that compare the performance of UKF and CUKF in identifying parameters is conducted. Finally, RTHSMU experiments using UKF and CUKF were carried out for a three-story shear building modeled with the BW model. It is demonstrated from these experiments that adopting online model updating can improve the accuracy of the simulated response of the numerical substructure so an enhanced RTHSMU method is achieved.

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