Element Identification of Member Properties of Framed Structures

Oreta, Andres Winston C.; Tanabe, Tada-aki

doi:10.1061/(asce)0733-9445(1994)120:7(1961)

Cited by 32 publications

(13 citation statements)

References 4 publications

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“…The structure is divided into three substructures: S 1 = [7][8][9][10][11][12], i.e. 7th to 12th DOFs inclusive, and S 2 = [3][4][5][6][7][8] and S 3 = [1][2][3][4].…”

Section: Sti Ness Identiÿcation Of 12-dof Systemmentioning

confidence: 99%

“…7th to 12th DOFs inclusive, and S 2 = [3][4][5][6][7][8] and S 3 = [1][2][3][4]. Two random excitation forces act on the 3rd and 7th DOFs.…”

Section: Sti Ness Identiÿcation Of 12-dof Systemmentioning

confidence: 99%

“…The ÿrst substructure is S 1 = [7][8][9][10][11][12], for which no damping and elastic forces are used to form the interface forces. Accordingly, only accelerations (as opposed to displacements or velocities) at interface DOFs are required to compute the interface forces at S 1 .…”

Section: Sti Ness Identiÿcation Of 12-dof Systemmentioning

confidence: 99%

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Substructural first‐ and second‐order model identification for structural damage assessment

Tee

Koh

Quek

2005

Earthq Engng Struct Dyn

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SUMMARYThis paper presents two methods to perform system identiÿcation at the substructural level, taking advantage of reduction in the number of unknowns and degrees of freedom (DOFs) involved, for damage assessment of fairly large structures. The ÿrst method is based on ÿrst-order state space formulation of the substructure where the eigensystem realization algorithm (ERA) and the observer/Kalman ÿlter identiÿcation (OKID) are used. Identiÿcation at the global level is then performed to obtain the secondorder model parameters. In the second method, identiÿcation is performed at the substructural level in both the ÿrst-and second-order model identiÿcation. Both methods are illustrated using numerical simulation studies where results indicate their signiÿcantly better performance than identiÿcation using the global structure, in terms of e ciency and accuracy. A 12-DOF system and a fairly large structural system with 50 DOFs are used where the e ects of noisy data are considered. In addition to numerical simulation studies, laboratory experiments involving an eight-storey frame model are carried out to illustrate the performance of the proposed method. The identiÿcation results presented in terms of the sti ness integrity index show that the proposed methodology is able to locate and quantify damage fairly accurately.

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“…The structure is divided into three substructures: S 1 = [7][8][9][10][11][12], i.e. 7th to 12th DOFs inclusive, and S 2 = [3][4][5][6][7][8] and S 3 = [1][2][3][4].…”

Section: Sti Ness Identiÿcation Of 12-dof Systemmentioning

confidence: 99%

“…7th to 12th DOFs inclusive, and S 2 = [3][4][5][6][7][8] and S 3 = [1][2][3][4]. Two random excitation forces act on the 3rd and 7th DOFs.…”

Section: Sti Ness Identiÿcation Of 12-dof Systemmentioning

confidence: 99%

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Substructural first‐ and second‐order model identification for structural damage assessment

Tee

Koh

Quek

2005

Earthq Engng Struct Dyn

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“…In the works [1][2][3][4], modified structural dynamic models are used to predict the dynamic response of structures accurately and the structural damage detection and residual life evaluation are carried out by combining the measured results. In 2006, Zhuang [5] located the damage of self-reinforced polyethylene composites accurately by means of acoustic emission technology.…”

Section: Introductionmentioning

confidence: 99%

A High-Sensitivity Fusion Index for Delamination Identification of Composite Laminate Structures

Liu¹,

Wang²,

Shi³

et al. 2017

dtetr

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Abstract. A significant obstacle that hinders the extensive application of large composite structures in the aerospace industry is the lack of valid identification methods for composite damage, where the delamination damage is the most common one. In this paper, a high-sensitivity fusion index (HSFI) is proposed for detecting, locating, and quantifying damages in aerospace polymer composite structures. The proposed fusion index is developed based on modern information fusion of three vibration-based indexes: modal strain, modal flexibility and static strain indexes. And its advantage is high sensitivity and strong noise immunity. By detecting the delamination damage in a composite laminate plate, it is verified that this fusion index is highly sensitive for delamination position and size in both cases of little and large damage in the presence of 2% level noise.

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“…Performing identification on the whole structure is not computationally efficient and again could lead to a singular or ill-conditioned optimization problem. To overcome these difficulties, some research efforts have been geared toward performing identification regionally around the portion of structure which is more likely to damage [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Wavelet-based Identification of Time-Varying Shear-beam buildings using Incomplete and Noisy Measurement Data

Shi

Chang

2013

Nonlinear Engineering

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Quite a lot of structural identification techniques assume that the complete state of the structure can be measured which unfortunately cannot be easily met in practice. In addition, measurement data are normally contaminated with noise which unavoidably affects the performance of these techniques. In this paper, two waveletbased identification techniques are developed to identify time-varying parameters in a shear-beam building using incomplete and noisy measurement data. By approximating the unknown time-varying parameters using wavelet multiresolution expressions, a parametric identification problem is transformed into a nonparametric coefficient optimization. As the wavelet multi-resolution expressions can accurately represent arbitrary functions, these wavelet-based techniques can be used to identify various forms of timevarying behavior that can occur to the building's parameters. Depending on the availability of measurement data, two wavelet-based identification techniques, story-by-story and sub-structural, are proposed. To minimize the effect of measurement noise, a wavelet decomposition level selection scheme and a multi-rate Kalman data fusion technique are adopted. Numerical studies are conducted to illustrate the performance of the proposed wavelet-based timevarying structural identification techniques. Results show that the techniques can track the time-varying properties of shear-beam buildings quite accurately under incomplete and noisy measurement condition.

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Element Identification of Member Properties of Framed Structures

Cited by 32 publications

References 4 publications

Substructural first‐ and second‐order model identification for structural damage assessment

Substructural first‐ and second‐order model identification for structural damage assessment

A High-Sensitivity Fusion Index for Delamination Identification of Composite Laminate Structures

Wavelet-based Identification of Time-Varying Shear-beam buildings using Incomplete and Noisy Measurement Data

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