Kalman Filter—Finite Element Method in Identification

Hoshiya, Masaru; Sutoh, Atsushi

doi:10.1061/(asce)0733-9399(1993)119:2(197)

Cited by 52 publications

(26 citation statements)

References 17 publications

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“…In the method used here, the error covariance matrix of estimation P k is enlarged with a modification weight W in every global iteration to obtain fast convergence as proposed by Hoshiya et al [3]. The aim is to make the fictitious difference between the true state and the estimated state more significant as it can be seen from the function J in equation (8).…”

Section: Ki+1mentioning

confidence: 99%

“…The estimation of parameters presented in this paper is aimed at identification of geotechnical model parameters which do not change in time. Thus, a stationary transition of state estimates is suitable for our purpose [3].…”

Section: Introductionmentioning

confidence: 99%

“…as a very successful method for state estimation and parameter identification. In recent years, some applications of the KF method have also arisen in the field of geotechnical engineering for estimation of material parameters [3,4,6].…”

Section: Introductionmentioning

confidence: 99%

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Identification of parameters in nonlinear geotechnical models using extenden Kalman filter

Nestorović

Nguyễn

Trajkov

2014

MATEC Web of Conferences

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Abstract. Direct measurement of relevant system parameters often represents a problem due to different limitations. In geomechanics, measurement of geotechnical material constants which constitute a material model is usually a very difficult task even with modern test equipment. Back-analysis has proved to be a more efficient and more economic method for identifying material constants because it needs measurement data such as settlements, pore pressures, etc., which are directly measurable, as inputs. Among many model parameter identification methods, the Kalman filter method has been applied very effectively in recent years. In this paper, the extended Kalman filter -local iteration procedure incorporated with finite element analysis (FEA) software has been implemented. In order to prove the efficiency of the method, parameter identification has been performed for a nonlinear geotechnical model.

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Section: Ki+1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of parameters in nonlinear geotechnical models using extenden Kalman filter

Nestorović

Nguyễn

Trajkov

2014

MATEC Web of Conferences

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“…Extended Kalman filter (EKF) has been shown to be useful for structural identification with limited response outputs [11,12], but in the EKF approach, the extended state vector includes the augmented unknown structural parameters. Structural state vector and the parametric vector are estimated simultaneously [13,14].…”

Section: A Two-step Kalman Estimator For the Identification Ofmentioning

confidence: 99%

“…Extended Kalman filter (EKF) has been studied and shown to be a useful tool for structural identification with limited measurements of structural response [11,12], but EKF approach requires that excitation inputs are measured [5]. Also, in the extended state vector, unknown structural parameters are included as an augmented vector.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Monitoring of Multistory Buildings under Unknown Earthquake Excitation by a Wireless Sensor Network

Lei

Tang

Wang

et al. 2012

International Journal of Distributed Sensor Networks

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It is necessary to investigate techniques for monitoring structures under unknown earthquakes. For this purpose, an algorithm is proposed in this paper for the identification of structures and excitation of multi-story shear buildings with limited measurements of structural responses. The equation of motion of a multi-story shear building under ground motion is established in the absolute co-ordinate system, while the multi-story building is decomposed into substructures. A novel two-step Kalman estimator approach, which is not available in the previous literature, is proposed for the identification substructures and unknown ground motion with less computational effort. Then, for the purpose of intelligent structural monitoring, a new wireless sensor network is developed in this paper. The designed wireless sensor network has a two-level cluster-tree architecture. Hardware designs of the sensor unit and the cluster head are presented; especially the cluster head contains a low power digital signal processor with strong computing capacity. Thus, the wireless sensor network has the unique feature offers distributed computing at group level. Finally, the proposed algorithm is embedded into the wireless sensor network for intelligent structural monitoring and an experimental test shows the technique is effective for intelligent monitoring of multi-story buildings under unknown earthquakes.

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Identification of linear structural systems using earthquake-induced vibration data

Luş

Betti

Longman

1999

Earthquake Engng. Struct. Dyn.

132

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This paper addresses the issue of system identi"cation for linear structural systems using earthquake induced time histories of the structural response. The proposed methodology is based on the Eigensystem Realization Algorithm (ERA) and on the Observer/Kalman "lter IDenti"cation (OKID) approach to perform identi"cation of structural systems using general input}output data via Markov parameters. The e$ciency of the proposed technique is shown by numerical examples for the case of eight-storey building "nite element models subjected to earthquake excitation and by the analysis of the data from the dynamic response of the Vincent-Thomas cable suspension bridge (Long Beach, CA) recorded during the Whittier and the Northridge earthquakes. The e!ects of noise in the measurements and of inadequate instrumentation are investigated. It is shown that the identi"ed models show excellent agreement with the real systems in predicting the structural response time histories when subjected to earthquake-induced ground motion.

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Kalman Filter—Finite Element Method in Identification

Cited by 52 publications

References 17 publications

Identification of parameters in nonlinear geotechnical models using extenden Kalman filter

Identification of parameters in nonlinear geotechnical models using extenden Kalman filter

Intelligent Monitoring of Multistory Buildings under Unknown Earthquake Excitation by a Wireless Sensor Network

Identification of linear structural systems using earthquake-induced vibration data

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