Locating the damaged storey of a building using distance measures of low-order AR models

Xing, Zhenhua; Mita, Akira

doi:10.12989/sss.2010.6.9.991

Cited by 9 publications

(13 citation statements)

References 12 publications

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“…Martin [24] proposed cepstral metric of ARMA models. Xing et al [14] further developed the Martin's method and proposed cepstral metric of AR model. An autoregressive model can be written as:…”

Section: Cepstral Metric Of Ar Modelmentioning

confidence: 99%

“…where is the coefficient of AR model, is the order of AR model, and is a white noise time sequence. The cepstral metric of AR model is described as follows [14]:…”

Section: Cepstral Metric Of Ar Modelmentioning

confidence: 99%

“…Here, the acquired order of ARCH model is 5. For the CMC method, the Xing's method [14] is used to obtain the order of AR model. The literature [14] already described the order determination method of AR model in detail.…”

Section: Numerical Examplesmentioning

confidence: 99%

“…Zheng et al [13] proposed a damage indicator based on the distance between ARMA models to detect structural damage including its location and severity. Xing et al [14] utilized low-order AR models and cepstral metric to find damage locations. The principle of the method is that the distance between the models of damaged and undamaged states is correlated with the damage location information.…”

Section: Introductionmentioning

confidence: 99%

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Structural nonlinear damage detection using improved Dempster-Shafer theory and time domain model

Guo¹,

Zhou²,

Zhang³

2019

J. vibroeng.

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In the service period, a crack may appear in some engineering structures. The development of accurate and effective methods for crack damage detection has become a topic of great importance. In this paper, a nonlinear damage detection method based on the improved Dempster-Shafer (D-S) theory and time domain model is presented. First, acceleration responses in the undamaged and damaged states are measured by using accelerometers. Then, acceleration responses are utilized to establish an autoregressive (AR) model, and residual time series of acceleration responses are used to establish an autoregressive conditional heteroskedasticity (ARCH) model. A cepstral metric conversion (CMC) method based on the AR model is employed to obtain local damage solution and an autoregressive conditional heteroskedasticity conversion (ARCHC) method based on ARCH model is presented to acquire another local damage solution. Finally, the D-S theory is applied to detect damages by integrating these local damage solutions, and an improved D-S theory is further presented to enhance the detection accuracy. The numerical and experimental examples show that the improved D-S theory has high detection accuracy and good performance.

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Section: Cepstral Metric Of Ar Modelmentioning

confidence: 99%

“…where is the coefficient of AR model, is the order of AR model, and is a white noise time sequence. The cepstral metric of AR model is described as follows [14]:…”

Section: Cepstral Metric Of Ar Modelmentioning

confidence: 99%

Section: Numerical Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Structural nonlinear damage detection using improved Dempster-Shafer theory and time domain model

Guo¹,

Zhou²,

Zhang³

2019

J. vibroeng.

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“…Subsequently, when the structure is damaged, the ARMAX model fitted to the time history responses is called the current model. The distance between these two models (i.e., baseline and current) can be correlated with the position and severity of the damage . However, it is necessary to define an appropriate distance measure between the said two models in order to effectively distinguish the damaged one from the undamaged.…”

Section: Structural Damage Detection Combining Ssa and Armax Modelmentioning

confidence: 99%

Singular spectrum analysis combined with ARMAX model for structural damage detection

Lakshmi

Rao

Gopalakrishnan

2016

Struct. Control Health Monit.

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Time series analysis is being used popularly in structural health monitoring mainly because of its output-only and non-modal approach. Generally, the damage features are extracted either from the coefficients or the prediction errors of the time series models. However, when the incipient damage is small like minor cracks, the damage features of popularly used time series models, constructed using only the coefficients/prediction errors, are not sensitive. Therefore, identifying the presence or exact spatial damage location becomes difficult. In view of this, in this paper, we present an approach to enhance the sensitivity of the damage features by augmenting Singular Spectrum Analysis (SSA) to ARMAX model, enabling it to locate the smaller damage like cracks. The damage index is obtained from the Cepstral distance between any two ARMAX models. Numerical simulation studies have been carried out by considering an example of a simply supported beam girder with single and multiple cracks. Experimental studies on a simply supported RCC beam is conducted to demonstrate the effectiveness of the proposed algorithm. A benchmark problem associated with the bookshelf frame structure, proposed by Engineering Institute -Los Alamos National Laboratory, is used as another example for experimental verification of the proposed technique. SSA is found to improve the sensitivity of the damage features devised from the ARMAX models for detection of minor damage and damage localization on the structures.

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Experimental Research on Damage Detection Based on Time Domain Data and Bayesian Fusion

Guo

2022

J. Phys.: Conf. Ser.

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Damages such as cracks often occur in engineering structures, and such damages often have the characteristics of time-domain variable stiffness. To effectively identify this kind of damage, a damage detection method based on time domain data and Bayesian fusion is presented in this paper. First, a hybrid model of the AR/ARCH model is used to identify structural nonlinear damage, and a damage indicator is also used: the second-order-variance indicator (SOVI). Although most of the nonlinear damage information is extracted by the indicator SOVI and ARCH model, some nonlinear information is still filtered out by the AR model part. Therefore, a linear cepstral metric indicator (CMI) based on the AR model is introduced to extract the remained nonlinear damage information, and further, the Bayesian fusion theory is applied to combine the results of SOVI and CMI to obtain complete nonlinear damage information and achieve better identification results. Finally, a three-story frame experimental model is used to demonstrate the effectiveness of the Bayesian fusion. Experimental results show that the Bayesian fusion method is superior to SOVI and CMI.

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Locating the damaged storey of a building using distance measures of low-order AR models

Cited by 9 publications

References 12 publications

Structural nonlinear damage detection using improved Dempster-Shafer theory and time domain model

Structural nonlinear damage detection using improved Dempster-Shafer theory and time domain model

Singular spectrum analysis combined with ARMAX model for structural damage detection

Experimental Research on Damage Detection Based on Time Domain Data and Bayesian Fusion

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