Structural Identification by Extended Kalman Filter

Hoshiya, Masaru; Saito, Etsuro

doi:10.1061/(asce)0733-9399(1984)110:12(1757)

Cited by 484 publications

(200 citation statements)

References 4 publications

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“…In substructure 1, S 1 =[1-3], and substructure 2, S 2 = [3][4][5][6][7][8][9], no force is applied, whereas in substructure 3, S 3 =[9-10], a dynamic force is applied at level 10. In this example, the substructures have overlapping levels, providing an alternative to the non-overlap version of substructural identification used in the previous two examples.…”

Section: Identification With Complete Measurementsmentioning

confidence: 99%

“…While many structural identification methods in time domain and frequency domain have been proposed [4][5][6][7][8][9][10][11][12], most of these methods have been tested only on structures of limited unknowns. For large structural systems, modelling often involves a large number of degrees of freedom (DOFs) and also likely a large number of unknown parameters in the identification procedure.…”

Section: Introductionmentioning

confidence: 99%

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An improved substructural identification strategy for large structural systems

Trinh

Koh

2011

Struct. Control Health Monit.

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To identify physical parameters of a large structural system, the computational challenges in dealing with a large number of unknowns are formidable. A divide-and-conquer approach is often required to partition the structural system into many substructures, each with much lesser unknowns for more accurate and efficient identification. Furthermore, in view of the ill-conditioned nature of inverse analysis, it is highly beneficial to adopt non-gradient based search methods such as genetic algorithm (GA). To this end, this paper presents a GA-based substructural identification strategy for large structural systems. As compared to some recent work on substructural identification, the proposed strategy presents two significant improvements: (a) the use of acceleration measurements to directly account for interaction between substructures without approximation of interface force, and (b) the use of an improved identification method based on multi-feature GA. In numerical simulations, the mass, damping and stiffness parameters of a 100-storey shear building, involving 202 unknowns, are identified with very good accuracy (mean error of less than 3%) based on incomplete acceleration measurements with 10% noise. In addition, an experimental study on a 10-storey small-scale steel frame further validates the superior performance of the proposed strategy over complete structural identification.2

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Section: Identification With Complete Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An improved substructural identification strategy for large structural systems

Trinh

Koh

2011

Struct. Control Health Monit.

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“…(7) set k=k+1 and return to step (1). (3) STATE AND MEASUREMENT EQUA-TIONS To carry out the system identification, the state vector and measurement equations must be properly formulated.…”

Section: Modal Parameter Identifica-tion (1) Modal Analysismentioning

confidence: 99%

A Study on the Application of Modal Parameter Identification Using Kalman Filter to a Reinforced Concrete Bridge

Oreta

Tanabe

1992

Doboku Gakkai Ronbunshu

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System identification using Kalman filter was applied to an in-situ reinforced concrete bridge to identify its modal properties.A field vibration test was conducted to obtain vibration response data for the purpose of the identification. Using a step-by-step parameter identification technique based on modal analysis of a linear multi-degree of freedom system, the estimation of the modal parameters, which are the dominant natural frequencies and the corresponding damping ratios, was examined.The results from the system identification are presented and compared with those obtained from spectra analysis.

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“…On the other hand, the non-parametric identiÿcation procedure does not require a priori knowledge of system model, but instead yields the best representation of the system in the form of a multiple-indexed series of functions or functionals [6,7]. As a parametric identiÿcation procedure, the method based on extended Kalman ÿlter (EKF) has received much attention and has been used successfully in the parameter estimation problems over the past years [8,9]. In this procedure, the estimation problem is linearized about the predicted state so that the Kalman ÿlter can be applied.…”

Section: Introductionmentioning

confidence: 99%

Improvement of parameter estimation for non-linear hysteretic systems with slip by a fast Bayesian bootstrap filter

Suzuki

Noori

2004

International Journal of Non-Linear Mechanics

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Modeling and identiÿcation of non-linear hysteretic systems are widely encountered in the structural dynamics ÿeld, especially for the hysteresis with slip. A model, called SL model, which can describe the pinching of most practical hysteresis loops perfectly was proposed by Baber and Noori (J. Eng. Mech. 111 (1985) 1010). A method of estimating the parameters of SL model on the basis of input-output data based on bootstrap ÿlter was proposed by the writers. Bootstrap ÿlter is a ÿltering method based on Bayesian state estimation and Monte Carlo method, which has the great advantage of being able to handle any functional non-linearity and system and/or measurement noise of any distribution. The standard bootstrap ÿlter, however, is not time e cient, i.e., it is very time consuming and is not suitable for real-time applications. In this paper, previous work by the writers is extended to do the parameter estimation of SL model by a fast Bayesian bootstrap ÿltering technique. Simulation results are presented to demonstrate the performance of the algorithm.

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Structural Identification by Extended Kalman Filter

Cited by 484 publications

References 4 publications

An improved substructural identification strategy for large structural systems

An improved substructural identification strategy for large structural systems

A Study on the Application of Modal Parameter Identification Using Kalman Filter to a Reinforced Concrete Bridge

Improvement of parameter estimation for non-linear hysteretic systems with slip by a fast Bayesian bootstrap filter

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