A multivariate time-series analysis employing a state-space embedding strategy and singular value decomposition is presented in this article to detect infrastructure damage. After summarizing the current state-space reconstruction method, the univariate state-space reconstruction is extended to multivariate (or global) reconstruction for observed time series at multiple locations. Under the hypothesis that reconstructed phase state geometry will change with damage, a reduced feature based on Mahalanobis distance of the most significant singular value vector, which is calculated from the reconstructed trajectory, is proposed. Both the area under receiver operating characteristic curve and deflection coefficient are used as comparison metrics to illustrate the presence and severity of damage. The advantage of this proposed approach is computational efficiency and easy implementation using state-space methodology since it does not require high-dimensional neighbor searches, as previous methods have proposed. Validation of the approach is demonstrated using a 6-degree-of-freedom linear spring-mass system and the IASC-ASCE 4-story benchmark experimental structure. Results from both test beds show that damage occurrence and severity can be successfully identified.
The improved direct sti®ness calculation (DSC) is a simple and relatively practical technique to estimate the bending sti®ness along beam-type structures based on modal parameters and thus to assess damage in the structure. Application of this technique to a real continuous bridge, named Truckee River Bridge (TRB) in California, is presented in this paper. Comparing the sti®ness estimated from baseline condition with that obtained from di®erent damage scenarios in consideration of measurement errors, the damage location and its severity were reliably estimated. Moreover, the improved DSC technique is extended to detect the damage with material nonlinearity, which is simulated with pushover and time-history analyses using one earthquake event. The results of this case study show that the DSC is valid and e±cient for identifying the damage with material nonlinearity in beam-type bridges using low-order mode shapes, which is advantageous for in-suit structural health monitoring applications.
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