A Novel Coupled State/Input/Parameter Identification Method for Linear Structural Systems

Wan, Zhenzhen; Wang, Ting; Li, Lin; Xu, Zhenghong

doi:10.1155/2018/7691721

Cited by 13 publications

(21 citation statements)

References 35 publications

(42 reference statements)

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“…These vectors are all assumed mutually uncorrelated, zero-mean and with covariance relations: This is essentially a non-linear problem since the system matrices depends on the parameters in the augmented state. We apply a Kalman-type algorithm from [8], which is termed extended joint input-state (EJIS) estimation. This is an extension of previous algorithms [19,20] that considers minimum-variance unbiased estimation in systems with unknown inputs.…”

Section: Equations For the Identification Problemmentioning

confidence: 99%

“…Herein, the excitation forces are considered unknown and are estimated from limited output response measurements, typically accelerations. Although many techniques have recently been proposed in the literature [8][9][10][11][12][13][14][15][16][17][18], the application of these inverse methods are not well-explored for long-span bridges. There is therefore a need to test the available methods to get experience on the actual performance under realistic conditions.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, the apply the input and state estimator in [8] to study the estimation of wind loads. This Kalman-type algorithm, which is an extended version of earlier algorithms [19,20], allows for an extension to estimation of unknown system parameters.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Inverse Identification of Buffeting and Self-Excited Wind Loads on the Hardanger Bridge From Acceleration Data

Petersen¹,

Øiseth²,

Nord³

et al. 2019

Proceedings of the 7th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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The traditional wind load assessment for long-span bridges rely on assumed models for the wind field and aerodynamic coefficients from wind tunnel tests, which usually introduces some uncertainties. It is therefore desired to develop tools that can utilize full-scale vibration response data from existing bridges in order to study the wind loading in detail for in-situ conditions. This paper presents a novel case study of inverse identification of dynamic wind loads on the 1310 m long Hardanger bridge, a suspension bridge equipped with a network of accelerometers. The identification method used is an extented Kalman-type filter for joint input, state, and parameter estimation. A system model considering the still-air modes in addition to a quasi-steady submodel for the self-excited forces of the bridge is presente. The coefficients for self-excited lift and pitching moment are considered unknown and are jointly estimated with the buffeting forces.

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Section: Equations For the Identification Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Inverse Identification of Buffeting and Self-Excited Wind Loads on the Hardanger Bridge From Acceleration Data

Petersen¹,

Øiseth²,

Nord³

et al. 2019

Proceedings of the 7th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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show abstract

“…To solve this problem, Wan et al proposed a method called EGDF which is an extension of the unbiased minimum variance estimation for coupled state/input/parameter identification for nonlinear systems in state space [23]. Song developed the joint input-state estimation technique for joint input-state-parameter estimation based on the unscented minimum variance unbiased (UMVU) estimation method [24].…”

Section: Introductionmentioning

confidence: 99%

Wind Load and Structural Parameters Estimation from Incomplete Measurements

Xue

Liu

Peng

et al. 2019

Shock and Vibration

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The extended minimum variance unbiased estimation approach can be used for joint state/parameter/input estimation based on the measured structural responses. However, it is necessary to measure the structural displacement and acceleration responses at each story for the simultaneous identification of structural parameters and unknown wind load. A novel method of identifying structural state, parameters, and unknown wind load from incomplete measurements is proposed. The estimation is performed in a modal extended minimum variance unbiased manner, based on incomplete measurements of wind-induced structural displacement and acceleration responses. The feasibility and accuracy of the proposed method are numerically validated by identifying the wind load and structural parameters on a ten-story shear building structure with incomplete measurements. The effects of crucial factors, including sampling duration and the number of measurements, are discussed. Furthermore, the practical application of the developed inverse method is evaluated based on wind tunnel testing results of a 234 m tall building structure. The results indicate that the structural state, parameters, and unknown wind load can be identified accurately using the proposed approach.

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“…Several extensions of these algorithms for joint input-state-parameter estimation have recently been developed, allowing for the tracking of uncertain parameters in addition the estimation of the inputs and system states. The AKF was extended by Naets et al in [13], the GDF was extended by Wan et al in [16], the smoothing variant of the GDF presented in [9] was extended by Maes et al in [10], and the DKF algorithm was extended by Azam et al in [1].…”

Section: Introductionmentioning

confidence: 99%

Quasi-Static Correction of Modally Reduced Order Models for System Inversion in Structural Dynamics

Maes

Karlsson²,

Lombaert³

2019

Proceedings of the 7th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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A wide variety of system inversion algorithms has been proposed in the literature, tackling the problem of force identification, parameter estimation, and state/response estimation. Recently, much focus has gone to recursive joint input-state estimation and joint inputstate-parameter estimation, where the forces applied to the structure, the corresponding system states, and its parameters are simultaneously estimated in a recursive fashion. In order to reduce the computational load, these techniques are frequently used in combination with modally reduced order models. This paper shows that a model order reduction can lead to large estimation errors in the system inversion caused by disregarding the contribution of the so-called out-of-band modes. A recently developed computationally efficient quasi-static correction technique, which can be used in state-space modeling, is implemented and evaluated for both joint input-state estimation and joint input-state-parameter estimation. The evaluation is based on numerical simulations. It is shown that the quasi-static correction significantly reduces the estimation errors introduced by the model order reduction. 4386 COMPDYN 2019 7 th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering M. Papadrakakis, M. Fragiadakis (eds.

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A Novel Coupled State/Input/Parameter Identification Method for Linear Structural Systems

Cited by 13 publications

References 35 publications

Inverse Identification of Buffeting and Self-Excited Wind Loads on the Hardanger Bridge From Acceleration Data

Inverse Identification of Buffeting and Self-Excited Wind Loads on the Hardanger Bridge From Acceleration Data

Wind Load and Structural Parameters Estimation from Incomplete Measurements

Quasi-Static Correction of Modally Reduced Order Models for System Inversion in Structural Dynamics

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