Real time structural modal identification using recursive canonical correlation analysis and application towards online structural damage detection

Bhowmik, Basuraj; Tripura, Tapas; Hazra, Budhaditya; Pakrashi, Vikram

doi:10.1016/j.jsv.2019.115101

Cited by 72 publications

(38 citation statements)

References 35 publications

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“…3 The total damage indicator displayed robustness against different levels of noise with SNRs of 1 dB, 5 dB, and 10 dB inputted, but attention still needs to paid to avoid the significant environmental noise that was present during the experiment to obtain good identification results. 4 The proposed method has the limitations that the damage indicator was calculated based on two working conditions, where the structural design and boundary conditions were the same. At the same time, the result could only show the damage difference between the two conditions and one control working case needed to be chosen.…”

Section: Discussionmentioning

confidence: 99%

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Numerical and Experimental Verification of a Multiple-Variable Spatiotemporal Regression Model for Grout Defect Identification in a Precast Structure

Zhang

Tang

Zhou

et al. 2020

Sensors

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Due to the increased service life, environmental corrosion, unreasonable construction, and other issues, local defects inevitably exist in civil structures, which affect the structural performance and can lead to structural failure. However, research on grout defect identification of precast reinforced concrete frame structures with rebars spliced by sleeves faces great challenges owing to the complexity of the problem. This study presents a multiple-variable spatiotemporal regression model algorithm to identify local defects based on structural vibration responses collected using a sensor network. First, numerical simulations were carried out on precast beam–column connection models by comparing the identification results based on a single-variable regression model, two-variable spatial regression model, and two-variable spatiotemporal regression model; furthermore, a multiple-variable spatiotemporal regression model was proposed and robustness analysis of the damage indicator was carried out. Then, to explore the validity of the proposed method, a nondestructive vibration experiment was considered on a half-scaled, two-floor, precast concrete frame structure with column rebars spliced by defective grout sleeves. The results show that local defects were successfully identified based on a multiple-variable spatiotemporal regression model.

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Section: Discussionmentioning

confidence: 99%

“…Due to overloading, environmental corrosion, material aging, operating loads, fatigue, and other unexpected events, engineering structures inevitably sustain different kinds of defects that can lead to structural failure. Therefore, it is necessary to develop a defect identification method to guarantee structural safety [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Numerical and Experimental Verification of a Multiple-Variable Spatiotemporal Regression Model for Grout Defect Identification in a Precast Structure

Zhang

Tang

Zhou

et al. 2020

Sensors

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“…Cluster analysis can be seen as an alternative approach because it does not require a prior baseline to perform feature discrimination, which is useful for the health assessment of aged structures, or for post-accident/post-retrofitting situations. Recent studies in damage detection have provided real-time solutions using recursive principal component analysis in conjunction with time varying auto-regressive modeling to successfully detect instantaneous structural damage [31][32][33]. As a recursive model updating approach, the Kalman filter (EKF) has been used in combination with various regularization methods to identify structural parameters and their changes in both vibration-and vision-based data.…”

Section: Damage Detection Methodsmentioning

confidence: 99%

Bridge Damage Detection Approach Using a Roving Camera Technique

Lydon

Kromanis

et al. 2021

Sensors

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Increasing extreme climate events, intensifying traffic patterns and long-term underinvestment have led to the escalated deterioration of bridges within our road and rail transport networks. Structural Health Monitoring (SHM) systems provide a means of objectively capturing and quantifying deterioration under operational conditions. Computer vision technology has gained considerable attention in the field of SHM due to its ability to obtain displacement data using non-contact methods at long distances. Additionally, it provides a low cost, rapid instrumentation solution with low interference to the normal operation of structures. However, even in the case of a medium span bridge, the need for many cameras to capture the global response can be cost-prohibitive. This research proposes a roving camera technique to capture a complete derivation of the response of a laboratory model bridge under live loading, in order to identify bridge damage. Displacement is identified as a suitable damage indicator, and two methods are used to assess the magnitude of the change in global displacement under changing boundary conditions in the laboratory bridge model. From this study, it is established that either approach could detect damage in the simulation model, providing an SHM solution that negates the requirement for complex sensor installations.

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“…However, the computational cost of these methods is still prohibitive for edge computing onboard low-power sensing nodes. Considering, e.g., the method based on recursive correlation analysis (RCCA) presented in [16], the most demanding part related to the eigenspace update has a computational complexity in the order of O d 3 per input sample, with d indicating the number of eigenvectors of the block covariance matrix of structural responses. Other recursive approaches are typically characterized by similar complexity [17].…”

Section: Introductionmentioning

confidence: 99%

Statistical Approach for Vibration-Based Damage Localization in Civil Infrastructures Using Smart Sensor Networks

2021

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One of the most discussed aspects of vibration-based structural health monitoring (SHM) is how to link identified parameters with structural health conditions. To this aim, several damage indexes have been proposed in the relevant literature based on typical assumptions of the operational modal analysis (OMA), such as stationary excitation and unlimited vibration record. Wireless smart sensor networks based on low-power electronic components are becoming increasingly popular among SHM specialists. However, such solutions are not able to deal with long data series due to energy and computational constraints. The decentralization of processing tasks has been shown to mitigate these issues. Nevertheless, traditional damage indicators might not be suitable for onboard computations. In this paper, a robust damage index is proposed based on a damage sensitive feature computed in a decentralized fashion, suitable for smart wireless sensing solutions. The proposed method is tested on a numerical benchmark and on a real case study, namely the S101 bridge in Austria, a prestressed concrete bridge that has been artificially damaged for research purposes. The results obtained show the potential of the proposed method to monitor the conditions of civil infrastructures.

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Real time structural modal identification using recursive canonical correlation analysis and application towards online structural damage detection

Cited by 72 publications

References 35 publications

Numerical and Experimental Verification of a Multiple-Variable Spatiotemporal Regression Model for Grout Defect Identification in a Precast Structure

Numerical and Experimental Verification of a Multiple-Variable Spatiotemporal Regression Model for Grout Defect Identification in a Precast Structure

Bridge Damage Detection Approach Using a Roving Camera Technique

Statistical Approach for Vibration-Based Damage Localization in Civil Infrastructures Using Smart Sensor Networks

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