Sensor Placement for Structural Damage Detection considering Measurement Uncertainties

Zhou, Xiao Qing; Xia, Yong; Hao, Hong

doi:10.1260/1369-4332.16.5.899

Cited by 16 publications

(7 citation statements)

References 22 publications

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“…In this regard, proper selection of limited number of DOFs is a challenging issue. Many researchers developed methods for selection of measurement locations in the context of sensor placement . In this study, excitation loads are applied at several locations, and response measurements are conducted in the desired DOFs as expressed in Table .…”

Section: Numerical Examplementioning

confidence: 99%

Structural model updating using incomplete transfer function and modal data

Farshadi

Esfandiari

Vahedi

2016

Struct Control Health Monit

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Structural damage detection by non-destructive techniques has been investigated widely by researchers in recent years, and several response-based Structural Health monitoring techniques have been developed. In this paper, a vibration-based damage detection algorithm through model updating utilizing incomplete frequency response function and modal data is presented. First, a modified sensitivity equation is presented to relate the changes of mode shapes to the changes of unknown structural parameters. Then, the developed equations are used to correlate changes of frequency response functions to the changes of structural parameters. Parameter estimation is conducted through least square solution of sensitivity equations. In order to prove the capability of the proposed method for structural damage detection purpose, a numerical example using error contaminated data is presented. Results exhibit robustness of method against measurement and mass modeling errors. KEYWORDS damage detection, frequency response function, modified modal method, sensitivity equation | INTRODUCTIONEvery structure may experience changes in structural properties during its design life because of the occurrence of several conditions such as extreme loading, earthquake, strong wind, erosion, and fatigue. Even if a structure is managed to withstand such changes, it is crucial to investigate its integrity. To ensure this objective, many algorithms have recently been developed to detect the location and severity of structural damages and evaluation of its safety. The basic principle of damage detection algorithms is because damage can cause detectable changes in structural response.A general classification of damage identification techniques refers to types of data used to detect damage including static and dynamic data. Most common dynamic features are modal data including mode shapes, [1,2] natural frequencies, [3,4] strain energy, [5] transfer function parameters, [6] measured modal flexibility, [7,8] frequency response function (FRF), [9][10][11][12] time domain structural responses, [13][14][15] or a combination of these features. [16,17] Damage detection by model updating is the process of modification of structural parameters to minimize discrepancy between response of structure and numerical model. The response residual can be minimized by various approaches. [18][19][20] Because model updating is an inverse problem and the relationship between structural parameters and measured response is inherently nonlinear, methods known as sensitivity-based approaches are developed to simplify solving this problem through linearization of equations. [21] Model updating using FRF improves predictive capabilities of updating procedure through providing large amount of information and has been used by some researchers. [22][23][24] One of the main advantages of using FRFs data in sensitivity-based model updating algorithm is avoiding propagation of errors in parameter estimation. [25,26] By sensitivity-based approaches, FRF residual is minimized to eval...

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Section: Numerical Examplementioning

confidence: 99%

Structural model updating using incomplete transfer function and modal data

Farshadi

Esfandiari

Vahedi

2016

Struct Control Health Monit

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“…These criteria can be easily compounded to yield more thorough considerations, such as EI-MKE and EI-AAA (Liu et al, 2013). Some researchers maximized the damage sensitivity and minimized the noise sensitivity in the measurement selection to enhance damage identification performance (Xia and Hao, 2000; Zhou et al, 2013). Other optimization strategies tend to approach the optimal solution using numerical methods, such as genetic algorithms (Yao et al, 2012), neural networks, and others.…”

Section: Experimental Methodsmentioning

confidence: 99%

Modal testing and detection of pretension deviation in a cable dome structure

Liu

Dong

Liang

2018

Advances in Structural Engineering

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Pretension deviation may cause stiffness degradation and overstress that can compromise the safety of tensile structures, which can be diagnosed by modal identification. This article presents modal tests on a 1:10 scaled model of a herringbone-ribbed cable dome structure. An optimal sensor placement scheme is proposed to observe the geometric stiffness change induced by pretension deviation. Based on the tests, different output-only modal identification techniques were implemented. A substructure strategy was adopted to overcome the limited measurement quantity and provide localized diagnoses. The experimental results show that operational modal analysis methods based on output-only data can effectively identify major modes of massive structures. The sensitivity of modal characteristics to pretension deviation is also evaluated via experimental comparisons, and modifications are implemented in an analytical finite element model to approximate the test model. The identified modal information can help locate stiffness degradation and thereby pretension loss in tensile structures. A modified modal strain energy method is proposed to detect pretension loss from decentralized testing and is verified by the test results.

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“…Other studies proposed OSP schemes with the objectives of achieving the best global response reconstructions, which are then used to assess the story‐by‐story seismic performance of the building . Some scholars also investigated sensor placement strategies for structural damage detection . However, the aforementioned studies did not address the issue of seismic performance assessment of key structural components, which are damage sensitive and require the accurate and complete estimation of both global and local structural responses with high resolution .…”

Section: Introductionmentioning

confidence: 99%

Optimal multi‐type sensor placement for monitoring high‐rise buildings under bidirectional long‐period ground motions

Zhao

2020

Struct Control Health Monit

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Summary Towards a complete and accurate seismic assessment of a super high‐rise building under bidirectional long‐period ground motions, this study proposes an optimal multi‐type sensor placement framework for the best reconstruction of structural responses and ground motions based on a full‐scale 3D finite element (FE) model of the building. The selected locations and types of sensors are capable of not only capturing the coupled bending‐torsional vibration but also providing an unbiased estimation of the unmeasured responses of seismic‐vulnerable components and bidirectional ground motions. A Kalman filtering algorithm‐based data fusion technique is employed to deal with the measurement data from multi‐type sensors. With the target of achieving the best reconstruction, an optimal sensor placement (OSP) configuration, which minimizes the reconstruction error, is achieved through a sequential sensor placement algorithm. To assess the feasibility of the proposed OSP framework and the accuracy of reconstructed responses and ground motions, a numerical study is also performed on the full‐scale 3D FE model of a super high‐rise building. The torsional effects of the building under bidirectional long‐period ground motions are assessed by comparison with the results without considering the torsional effect. The numerical results show that the proposed framework is feasible and the reconstructed responses and ground motions are accurate and that the torsional effects cannot be neglected in the seismic assessment of the building.

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Sensor Placement for Structural Damage Detection considering Measurement Uncertainties

Cited by 16 publications

References 22 publications

Structural model updating using incomplete transfer function and modal data

Structural model updating using incomplete transfer function and modal data

Modal testing and detection of pretension deviation in a cable dome structure

Optimal multi‐type sensor placement for monitoring high‐rise buildings under bidirectional long‐period ground motions

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