An Input/Output-Based Procedure for Fully Evaluating and Monitoring Dynamic Properties of Structural Systems via a Subspace Identification Method

Xiao, He; Bruhns, Otto T.; Waller, Heinz; Meyers, A.

doi:10.1006/jsvi.2001.3650

Cited by 16 publications

(17 citation statements)

References 36 publications

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“…suggested by Xiao et al [34]. It should be emphasized that the transformation is validated here for the first time in the context of damage experiments.…”

Section: Postprocessingmentioning

confidence: 92%

“…It should be emphasized that the transformation is validated here for the first time in the context of damage experiments. The examples in Xiao et al [34] are purely numerical. Hung et al [18] who derived the same transformation worked with experimental modal data but did not take into account any damage effects.…”

Section: Postprocessingmentioning

confidence: 98%

“…In detail, we apply the data-driven stochastic subspace identification algorithm to obtain a purely mathematical description of the system in form of a system and output matrix. To identify characteristic information like modal data we apply the state-space transformation proposed in the purely theoretical work of Xiao et al [34]. A similar transformation has been derived by Hung et al [18] who validated their method by means of experimental modal data of an undamaged structure.…”

Section: Introductionmentioning

confidence: 98%

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Experimental investigation of damage evolution by data-driven stochastic subspace identification and iterative finite element model updating

2007

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In civil and mechanical engineering, the monitoring of structures is still a frequently investigated research topic. The basis of the present monitoring concept is the analysis of non-destructive measurements taken at regular time intervals to provide information about the current state of damage in a structure. The approach includes two steps: First, a data-driven stochastic subspace identification is applied to obtain a mathematical description of the system. Using a special state-space transformation, we transfer this result into physically meaningful system properties. Thereby the modal information can be separated into a part which describes the undamped system response and a second contribution which represents the influence of the damping. In the second step, we discretise the experimental setup by means of finite elements and apply an iterative finite element model updating algorithm to localise and to determine the damage. One important innovative aspect of the contribution lies in the updating of an entire damage evolution in order to estimate the lifetime of the structure. The experimental laboratory setup is based on a cantilever beam which is damaged locally and progressively with the help of a special device.

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“…suggested by Xiao et al [34]. It should be emphasized that the transformation is validated here for the first time in the context of damage experiments.…”

Section: Postprocessingmentioning

confidence: 92%

Section: Postprocessingmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 98%

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Experimental investigation of damage evolution by data-driven stochastic subspace identification and iterative finite element model updating

2007

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“…Finite element model updating [15, 16] and data-driven structural parameter estimation [17, 18] can be applied to identify structures. However, these approaches are deemed to be rather challenging for its direct application to this study, since the tested structure is a complex timber frame with numerous structural elements.…”

Section: Model-based Story Stiffness Estimationmentioning

confidence: 99%

Ambient Vibration Testing for Story Stiffness Estimation of a Heritage Timber Building

Kang

Kim

Park

et al. 2013

The Scientific World Journal

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This paper investigates dynamic characteristics of a historic wooden structure by ambient vibration testing, presenting a novel estimation methodology of story stiffness for the purpose of vibration-based structural health monitoring. As for the ambient vibration testing, measured structural responses are analyzed by two output-only system identification methods (i.e., frequency domain decomposition and stochastic subspace identification) to estimate modal parameters. The proposed methodology of story stiffness is estimation based on an eigenvalue problem derived from a vibratory rigid body model. Using the identified natural frequencies, the eigenvalue problem is efficiently solved and uniquely yields story stiffness. It is noteworthy that application of the proposed methodology is not necessarily confined to the wooden structure exampled in the paper.

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“…This can be achieved by using the subspace identification technique to evaluate the system matrix with acceptable accuracy. Once the system parameter matrix A and output matrix C have been obtained from the subspace identification algorithm with output-only data or input-output data, the system damping and stiffness matrices can be obtained for acceleration sensing as [18] [ ]…”

mentioning

confidence: 99%

A damage detection algorithm integrated with a wireless sensing system

Hsu

Huang

et al. 2011

J. Phys.: Conf. Ser.

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An Input/Output-Based Procedure for Fully Evaluating and Monitoring Dynamic Properties of Structural Systems via a Subspace Identification Method

Cited by 16 publications

References 36 publications

Experimental investigation of damage evolution by data-driven stochastic subspace identification and iterative finite element model updating

Experimental investigation of damage evolution by data-driven stochastic subspace identification and iterative finite element model updating

Ambient Vibration Testing for Story Stiffness Estimation of a Heritage Timber Building

A damage detection algorithm integrated with a wireless sensing system

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