Identification Problem of Some Seismic Systems by Extended Kalman Filter

Hoshiya, Masaru; Saito, Etsuro

doi:10.2208/jscej1969.1983.339_59

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…Previous research has broadly classified time‐domain physical parameter identification considering the nonlinear characteristics of structures into two methods: an identification method based on a sequential linear approximation 16–20 and a direct identification method based on a nonlinear model 21–31 . The former method has the advantage of being relatively simple and easy to implement.…”

Section: Introductionmentioning

confidence: 99%

“…Previous research has broadly classified time-domain physical parameter identification considering the nonlinear characteristics of structures into two methods: an identification method based on a sequential linear approximation [16][17][18][19][20] and a direct identification method based on a nonlinear model. [21][22][23][24][25][26][27][28][29][30][31] The former method has the advantage of being relatively simple and easy to implement. However, the degree and type of nonlinearity, and the resolution of the time domain, significantly impact its accuracy and reliability.…”

Section: Introductionmentioning

confidence: 99%

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Damage detection based on stiffness identification in time domain for poly‐linear hysteresis using sparse modeling

Nabeshima

2023

Earthq Engng Struct Dyn

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The physical parameter identification of the dynamic mechanical model, such as stiffness identification, provides some valuable information for detecting post‐earthquake damage to building structures. During an earthquake, tracking changes in stiffness on the skeleton curve can be effective. It would also be helpful as an approximate evaluation if the method could be applied to structures with arbitrary poly‐linear hysteresis characteristics. However, previous research has not identified any poly‐linear hysteresis characteristics in a unified formulation. This paper proposes a new stiffness identification method in the time domain for building structures with any poly‐linear hysteresis characteristics using sparse modeling. The validity of the proposed method was investigated through numerical simulations and a full‐scale shaking table test.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Damage detection based on stiffness identification in time domain for poly‐linear hysteresis using sparse modeling

Nabeshima

2023

Earthq Engng Struct Dyn

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“…Loh and Chung 9 developed a nonlinear parameter identification method for the Baber–Wen model 10 . For an identification of extended Kalman filter, Hoshiya and Saito 11 proposed a stability evaluation approach for extended Kalman filter identification accuracy, and the reliability was numerically investigated for some seismic systems including an S‐DOF (single‐degree of freedom) model with bilinear hysteresis. Then, Hoshiya and Maruyama 12 proposed the identification method for the S‐DOF model with the simplified versatile‐hysteresis model, including the bilinear hysteresis.…”

Section: Introductionmentioning

confidence: 99%

Physical parameters identification for full‐scale ten‐story reinforced concrete building with degrading tri‐linear model by modal iterative error correction method

Uesaka

Nabeshima

Nakamura

et al. 2021

Earthq Engng Struct Dyn

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To assess the post‐earthquake seismic safety of buildings, it is crucial to predict seismic response, and it is necessary to set the appropriate physical parameters of the response analysis model. Numerous methods have been proposed to identify physical parameters. However, most of them are limited to linear systems, and previous researches on nonlinear systems have difficulties in practical applications. In this paper, a nonlinear response analysis model is identified for a full‐scale ten‐story reinforced concrete building with the degrading tri‐linear stiffness model by the modal iterative error correction (MIEC) method, and the accuracy of this technique is discussed by comparing with the shaking table test.

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“…Noteworthy contributions have been made by Masri and Caughey (1979); Beck and Jennings (1980); Hoshiya and Saito (1983); Masri et al (1987a, b); Ghanem and Shinozuka (1995); Shinozuka and Ghanem (1995); Qi and Sato (1999); Smyth et al (1999);and Sano et al (1999). Furukawa et al (2000) proposed a prediction error method (PEM) with a nonlin ear state-space model and carried out system identification of a base-isolated structure using a one-directional MDOF model in which the base isolation system was assumed to have a piecewise linear restoring force displacement relation.…”

Section: Introductionmentioning

confidence: 99%

System Identification of Base-Isolated Building using Seismic Response Data

et al. 2005

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Due to the complex nature of the excitation, and the inherent dynamics characteristics of restoring force of the base isolation systems, the response of base-isolated structures subject to strong earthquakes often experiences excursion into the inelastic range. Therefore, in designing base-isolated structures, the nonlinear hysteretic restoring force model of the base isolation system is frequently used to predict structural response and to evaluate structural safety. In this paper, the prediction error method system identification technique is used in conjunction with nonlinear state-space models for identification of a base-isolated structure. Using a variety of nonlinear restoring force models and bidirectional recorded seismic responses, several identification runs are conducted to evaluate the accuracy of the selected models. Several nonlinear restoring force models are utilized for the base-isolation system, including a multiple shear spring (MSS) model. Among all models used, results indicate that the trilinear hysteretic MSS model closely matches the actual hysteretic restoring force profile and time histories obtained directly from the observed data. Introd uctionThe behavior of base-isolated structures during an earthquake is highly affected by the characteristics of the base isolation system. The base isolation system separates the structure from its founda tion and primarily moves the natural frequency of the structure away from the dominant frequency range of the excitation via its low stiffness relative to that of the upper structure.Construction of base-isolated structures has increased, espe cially after the recent strong earthquakes in the United States and Japan. Despite the limited number of recorded seismic response data, vigorous studies to evaluate the actual behavior of base isolated structures during strong earthquakes have been con ducted. Nonlinearity in structural response is often due to the restoring force characteristics of the base isolation system, i.e., variations in structural stiffness and damping during strong earth quakes. Stewart et al. (1999) identified several base-isolated 1Associate Professor,

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Identification Problem of Some Seismic Systems by Extended Kalman Filter

Cited by 8 publications

References 7 publications

Damage detection based on stiffness identification in time domain for poly‐linear hysteresis using sparse modeling

Damage detection based on stiffness identification in time domain for poly‐linear hysteresis using sparse modeling

Physical parameters identification for full‐scale ten‐story reinforced concrete building with degrading tri‐linear model by modal iterative error correction method

System Identification of Base-Isolated Building using Seismic Response Data

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