Output-only structural damage detection based on transmissibility measurements and kernel principal component analysis

In this paper, we introduce a set of techniques for time series analysis based on principal component analysis (PCA). Firstly, the autoregressive (AR) model is established using acceleration response data, and the root mean squared error (RMSE) of AR model is calculated based on PCA. Then a new damage sensitive feature (DSF) based on the AR coefficients is presented. To test the efficacy of the damage detection and localization methodologies, the algorithm has been tested on the analytical and experimental results of a three-story frame structure model of the Los Alamos National Laboratory. The result of the damage detection indicates that the algorithm is able to identify and localize minor to severe damage as defined for the structure. It shows that the suggested method can lead to less amount of computing time, high suitability and identification accuracy.

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Damage detection in nonlinear civil structures using kernel principal component analysis

Advances in Structural Engineering ◽

10.1177/1369433220913207 ◽

2020 ◽

Vol 23 (11) ◽

pp. 2414-2430

Author(s):

Khaoula Ghoulem ◽

Tarek Kormi ◽

Nizar Bel Hadj Ali

Keyword(s):

Principal Component Analysis ◽

Damage Detection ◽

Structural Model ◽

Principal Component ◽

Original Data ◽

Component Analysis ◽

Correlated Data ◽

Data Driven ◽

Kernel Principal Component Analysis ◽

Structural Systems

In the general framework of data-driven structural health monitoring, principal component analysis has been applied successfully in continuous monitoring of complex civil infrastructures. In the case of linear or polynomial relationship between monitored variables, principal component analysis allows generation of structured residuals from measurement outputs without a priori structural model. The principal component analysis has been widely used for system monitoring based on its ability to handle high-dimensional, noisy, and highly correlated data by projecting the data onto a lower dimensional subspace that contains most of the variance of the original data. However, for nonlinear systems, it could be easily demonstrated that linear principal component analysis is unable to disclose nonlinear relationships between variables. This has naturally motivated various developments of nonlinear principal component analysis to tackle damage diagnosis of complex structural systems, especially those characterized by a nonlinear behavior. In this article, a data-driven technique for damage detection in nonlinear structural systems is presented. The proposed method is based on kernel principal component analysis. Two case studies involving nonlinear cable structures are presented to show the effectiveness of the proposed methodology. The validity of the kernel principal component analysis–based monitoring technique is shown in terms of the ability to damage detection. Robustness to environmental effects and disturbances are also studied.

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Principal Component Analysis Method with Space and Time Windows for Damage Detection

Sensors ◽

10.3390/s19112521 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2521 ◽

Cited By ~ 3

Author(s):

Ge Zhang ◽

Liqun Tang ◽

Licheng Zhou ◽

Zejia Liu ◽

Yiping Liu ◽

...

Keyword(s):

Principal Component Analysis ◽

Damage Detection ◽

Structural Damage ◽

Time Windows ◽

Principal Component ◽

Element Model ◽

Component Analysis ◽

Previous Method ◽

Beam Model ◽

Space And Time

Long-term structural health monitoring (SHM) has become an important tool to ensure the safety of infrastructures. However, determining methods to extract valuable information from large amounts of data from SHM systems for effective identification of damage still remains a major challenge. This paper provides a novel effective method for structural damage detection by introduction of space and time windows in the traditional principal component analysis (PCA) technique. Numerical results with a planar beam model demonstrate that, due to the presence of space and time windows, the proposed double-window PCA method (DWPCA) has a higher sensitivity for damage identification than the previous method moving PCA (MPCA), which combines only time windows with PCA. Further studies indicate that the developed approach, as compared to the MPCA method, has a higher resolution in localizing damage by space windows and also in quantitative evaluation of damage severity. Finally, a finite-element model of a practical bridge is used to prove that the proposed DWPCA method has greater sensitivity for damage detection than traditional methods and potential for applications in practical engineering.

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