Perspectives of Second-Order Blind Identification for Operational Modal Analysis of Civil Structures

Rainieri, Carlo

doi:10.1155/2014/845106

Cited by 18 publications

(12 citation statements)

References 29 publications

(66 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The dam health diagnosis result based on these data is shown in Figures 16 and 17. UCL is calculated by setting the test level = 0.01 using (26). From the two figures, we can see that the health state of the dam is normal at the 16 different times, which is consistent with the diagnosis result using the static monitoring data of displacement and flow seepage.…”

Section: Numerical Verificationsupporting

confidence: 76%

“…If the BSS problem can be solved, the modal parameters can be extracted from the estimated mixing matrix Θ and modal responseq ( ). To solve the BSS problem shown in (1), the SOBI algorithm [25,26] is adopted and a modal identification is proposed in our previous work.…”

Section: Modal Identification Of Concrete Damsmentioning

confidence: 99%

“…The SPE metrics are calculated using (22). Given a testing level , (26) determines the UCL of SPE for a dam without damage.…”

Section: The Flowchart Of Ica-based Dam Health Monitoringmentioning

confidence: 99%

“…For each newly obtained AVT data, the above mentioned Steps 1∼3 are repeated and the SPE index is calculated. These newly calculated SPE indices are compared with the UCL calculated using (26) to determine whether the structure is normal. If the new SPE metrics exceed the control limit, the dam is abnormal; otherwise, it is still in the normal state.…”

Section: The Flowchart Of Ica-based Dam Health Monitoringmentioning

confidence: 99%

See 3 more Smart Citations

Application of Blind Source Separation Algorithms and Ambient Vibration Testing to the Health Monitoring of Concrete Dams

Cheng

Tong

2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In this work, using AVT data, a health monitoring method for concrete dams based on two different blind source separation (BSS) methods, that is, second-order blind identification (SOBI) and independent component analysis (ICA), is proposed. A modal identification procedure, which integrates the SOBI algorithm and modal contribution, is first adopted to extract structural modal features using AVT data. The method to calculate the modal contribution index for SOBI-based modal identification methods is studied, and the calculated modal contribution index is used to determine the system order. The selected modes are then used to calculate modal features and are analysed using ICA to extract some independent components (ICs). The square prediction error (SPE) index and its control limits are then calculated to construct a control chart for the structural dynamic features. For new AVT data of a dam in an unknown health state, the newly calculated SPE is compared with the control limits to judge whether the dam is normal. With the simulated AVT data of the numerical model for a concrete gravity dam and the measured AVT data of a practical engineering project, the performance of the dam health monitoring method proposed in this paper is validated.

show abstract

Section: Numerical Verificationsupporting

confidence: 76%

Section: Modal Identification Of Concrete Damsmentioning

confidence: 99%

“…The SPE metrics are calculated using (22). Given a testing level , (26) determines the UCL of SPE for a dam without damage.…”

Section: The Flowchart Of Ica-based Dam Health Monitoringmentioning

confidence: 99%

Section: The Flowchart Of Ica-based Dam Health Monitoringmentioning

confidence: 99%

See 2 more Smart Citations

Application of Blind Source Separation Algorithms and Ambient Vibration Testing to the Health Monitoring of Concrete Dams

Cheng

Tong

2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Döhler et al [21] proposed a memory efficient algorithm for uncertainty quantification of modal parameters estimated from multisetup SSI. As for the computational time, Rainieri [22] investigated the effects of analysis parameters on the computational time and the accuracy of modal parameter estimates, providing some useful hints for reduction of computational time and control of accuracy of estimates.…”

Section: Introductionmentioning

confidence: 99%

Operational Modal Analysis Based on Subspace Algorithm with an Improved Stabilization Diagram Method

Qin

Kang

Wang

2016

Shock and Vibration

View full text Add to dashboard Cite

Subspace-based algorithms for operational modal analysis have been extensively studied in the past decades. In the postprocessing of subspace-based algorithms, the stabilization diagram is often used to determine modal parameters. In this paper, an improved stabilization diagram is proposed for stochastic subspace identification. Specifically, first, a model order selection method based on singular entropy theory is proposed. The singular entropy increment is calculated from nonzero singular values of the output covariance matrix. The corresponding model order can be selected when the variation of singular entropy increment approaches to zero. Then, the stabilization diagram with confidence intervals which is established using the uncertainty of modal parameter is presented. Finally, a simulation example of a four-story structure and a full-scale cable-stayed footbridge application is employed to illustrate the improved stabilization diagram method. The study demonstrates that the model order can be reasonably determined by the proposed method. The stabilization diagram with confidence intervals can effectively remove the spurious modes.

show abstract

Increasing the efficiency and efficacy of second-order blind identification (SOBI) methods

Brewick

Smyth

2016

Struct. Control Health Monit.

View full text Add to dashboard Cite

SUMMARYThis paper proposes a new technique to increase the efficiency and effectiveness of second-order blind identification (SOBI) methods by reducing the number of time-lagged covariance matrices required to produce highly accurate mixing matrix estimates. The technique is based on randomly selecting the time-lagged covariance matrices as opposed to choosing them sequentially, which takes advantage of a property of independence with regard to the selection of time-lagged covariance matrices, while simultaneously reducing the correlation between selected covariance matrices. The proposed randomized approach is first applied to undamped and damped sinusoids to demonstrate its effectiveness. The randomized method is then applied to a three-degree-of-freedom linear structure subjected to random excitation. Finally, the proposed method is incorporated into a modified version of the SOBI method and applied to perform identification of the UCLA Factor building from recorded earthquake responses. In each case, the performance of the randomized approach is compared with the traditional sequential selection of time-lagged covariance matrices, and the randomized approach consistently demonstrates superior performance both in terms of accuracy and efficiency. Additionally, the proposed randomized SOBI approach was easily inserted into the modified SOBI method demonstrating its malleability for SOBI variants and other blind source separation methods.

show abstract

Perspectives of Second-Order Blind Identification for Operational Modal Analysis of Civil Structures

Cited by 18 publications

References 29 publications

Application of Blind Source Separation Algorithms and Ambient Vibration Testing to the Health Monitoring of Concrete Dams

Application of Blind Source Separation Algorithms and Ambient Vibration Testing to the Health Monitoring of Concrete Dams

Operational Modal Analysis Based on Subspace Algorithm with an Improved Stabilization Diagram Method

Increasing the efficiency and efficacy of second-order blind identification (SOBI) methods

Contact Info

Product

Resources

About