Abstract
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.
Highlights
The safety of infrastructures such as bridges and high-rise buildings is of the utmost concern to the public
It demonstrates that the component of the eigenvector variance (CEV) corresponding to Sensor 1 computed by double-window PCA method (DWPCA) is larger than that obtained by moving PCA (MPCA) in Scenario E
This paper provides a novel effective method for structural damage detection by introduction of space and time windows in the traditional principal component analysis method
Summary
The safety of infrastructures such as bridges and high-rise buildings is of the utmost concern to the public. PCA is another popular method used for damage identification in long-term SHM It exhibits reliable and effective performance in modal analysis, reduced-order modelling, feature extraction, and structural damage detection [28,29,30,31,32]. Posenato et al proposed the moving PCA (MPCA) method to enhance discrimination features between undamaged and damaged structural responses [13,27,38] This method essentially uses a sliding fixed-size time window for time-series data instead of handling the total historical dataset. Due to the moving temporal window, MPCA enhances the detection effectiveness compared to that of the traditional PCA method through monitoring the evolution of eigenvector components between undamaged and damage states.
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