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    Displacement Estimation of a Nonlinear SDOF System under Seismic Excitation Using an Adaptive Kalman Filter

    Source: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2021:;Volume ( 008 ):;issue: 001::page 04021084
    Author:
    Yaohua Yang
    ,
    Tomonori Nagayama
    ,
    Kai Xue
    ,
    Di Su
    DOI: 10.1061/AJRUA6.0001213
    Publisher: ASCE
    Abstract: A displacement estimation method for a nonlinear single-degree-of-freedom (SDOF) system under seismic excitation is proposed based on an extended Kalman filter (EKF). This method first identifies time intervals where a system experiences significant nonlinearity. For a time period when the system is in an elastic phase, available observations for EKF are acceleration, displacement from numerical integration, and residual displacement. During a time period with significant nonlinearity, acceleration and virtual displacement measurements are employed as observations. Two EKF schemes are applied in this part. In the first scheme, displacement is estimated along with time-varying stiffness using an augmented state vector. In the second scheme, a bilinear hysteresis model with optimized system parameters is employed. The results are further smoothed by extended Kalman smoother (EKS). The proposed displacement estimation method is numerically studied on a bilinear SDOF system and applied to various hysteresis models and earthquake excitations. Data obtained in shaking table experiments on a full-scale bridge pier and a 4-story building are analyzed to validate the method. The displacements are estimated with high accuracies.
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      Displacement Estimation of a Nonlinear SDOF System under Seismic Excitation Using an Adaptive Kalman Filter

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4282733
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    • ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

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    contributor authorYaohua Yang
    contributor authorTomonori Nagayama
    contributor authorKai Xue
    contributor authorDi Su
    date accessioned2022-05-07T20:40:06Z
    date available2022-05-07T20:40:06Z
    date issued2021-12-11
    identifier otherAJRUA6.0001213.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4282733
    description abstractA displacement estimation method for a nonlinear single-degree-of-freedom (SDOF) system under seismic excitation is proposed based on an extended Kalman filter (EKF). This method first identifies time intervals where a system experiences significant nonlinearity. For a time period when the system is in an elastic phase, available observations for EKF are acceleration, displacement from numerical integration, and residual displacement. During a time period with significant nonlinearity, acceleration and virtual displacement measurements are employed as observations. Two EKF schemes are applied in this part. In the first scheme, displacement is estimated along with time-varying stiffness using an augmented state vector. In the second scheme, a bilinear hysteresis model with optimized system parameters is employed. The results are further smoothed by extended Kalman smoother (EKS). The proposed displacement estimation method is numerically studied on a bilinear SDOF system and applied to various hysteresis models and earthquake excitations. Data obtained in shaking table experiments on a full-scale bridge pier and a 4-story building are analyzed to validate the method. The displacements are estimated with high accuracies.
    publisherASCE
    titleDisplacement Estimation of a Nonlinear SDOF System under Seismic Excitation Using an Adaptive Kalman Filter
    typeJournal Paper
    journal volume8
    journal issue1
    journal titleASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
    identifier doi10.1061/AJRUA6.0001213
    journal fristpage04021084
    journal lastpage04021084-20
    page20
    treeASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2021:;Volume ( 008 ):;issue: 001
    contenttypeFulltext
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