Abstract
Strain data of structural health monitoring is a prospective to be made full use of, because it reflects the stress peak and fatigue, especially sensitive to local stress redistribution, which is the probably damage in the vicinity of the sensor. For decoupling structural damage and masking effects caused by operational conditions to eliminate the adverse impacts on strain-based damage detection, small time-scale structural events, i.e., the short-term dynamic strain responses, are analyzed in this paper by employing unsupervised modeling. A two-step approach to successively processing the raw strain monitoring data in the sliding time window is presented, consisting of the wavelet-based initial feature extraction step and the decoupling step to draw damage indicators. The principal component analysis and a low-rank property-based subspace projection method are adopted as two alternative decoupling methodologies. The approach’s feasibility and robustness are substantiated by analyzing the strain monitoring data from a customized truss experiment to successfully remove the masking effects of operating loads and identify local damages even concerning accommodating situations of missing data and limited measuring points. This work also sheds light on the merit of a low-rank property to separate structural damages from masking effects by comparing the performances of the two optional decoupling methods of the distinct rationales.
Highlights
Structural health monitoring (SHM) that consists of multidisciplinary technologies such as sensor, data processing, computer modeling, and mechanics inverse analysis can be responsible for the aging of infrastructures with the advantage of reducing the cost of the visual-based inspection and raising the efficiency of safety assessments
There is a two-step approach brought forward in this work to processing raw strain monitoring data only under operating loads for damage detection: in the first step, since relatively high-frequency dynamic strains increase the amount of data, the wavelet analysis tool is first used to process the strain responses to achieve initial feature extraction; in the step, two data-driven methodologies, principal component analysis (PCA) and another one denominated as the low-rank subspace projection are presented and applied in this work, respectively, for decoupling the effects on the strain-based feature data of the operating loads and anomalies, thereby getting corresponding local and global damage indicator (DI) values
The results show that both decoupling methods are able to detect damage instantly, one marked observation to emerge from this experiment is that the better performance is achieved by means of the local DI based on the low-rank subspace projection (LSP) rationale rather than PCA
Summary
Structural health monitoring (SHM) that consists of multidisciplinary technologies such as sensor, data processing, computer modeling, and mechanics inverse analysis can be responsible for the aging of infrastructures with the advantage of reducing the cost of the visual-based inspection and raising the efficiency of safety assessments. Environmental information such as temperature may not participate in data interpretation in such time sequences In this context, there is a two-step approach brought forward in this work to processing raw strain monitoring data only under operating loads for damage detection: in the first step, since relatively high-frequency dynamic strains increase the amount of data, the wavelet analysis tool is first used to process the strain responses to achieve initial feature extraction; in the step, two data-driven methodologies, PCA and another one denominated as the low-rank subspace projection are presented and applied in this work, respectively, for decoupling the effects on the strain-based feature data of the operating loads and anomalies (probably structural damages), thereby getting corresponding local and global DI values. The performance of the damage detection strategy is further evaluated concerning the cases of missing data and limited sensor deployments
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