Wavelet-Based Analysis for Detection of Isolation Bearing Malfunction in a Continuous Multi-Span Girder Bridge.

This paper describes techniques for detecting bearing malfunction directly from the seismic response of the bridge using wavelet transform time-varying identification. Instantaneous frequency of continuous wavelet and detail components of discrete wavelet transform are utilized as structural features, and they characterize isolation bearing condition via statistical clustering technique. Accuracy and efficacy of the techniques were verified in numerical simulations using analytical and finite element models. The techniques were implemented on seismic records from long-term monitoring of multi-span continuous-girder isolated bridge. Results demonstrate that wavelet-based features can effectively characterize the condition of the isolation bearing directly from seismic responses of girder and piers. [ABSTRACT FROM AUTHOR]