Strain is sensitive to damage, especially in steel structures. But traditional strain gauge does not fit bridge damage identification because it only provides the strain information of the point where it is set up. While traditional strain gauges suffer from its drawbacks, long-gage FBG strain sensor is capable of providing the strain information of a certain range, which all the damage information within the sensing range can be reflected by the strain information provided by FBG sensors. The wavelet transform is a new way to analyze the signals, which is capable of providing multiple levels of details and approximations of the signal. In this paper, a wavelet packet transform-based damage identification is proposed for the steel bridge damage identifications numerically and with experimental experiment to validate the proposed method. The strain data obtained via long-gage FBG strain sensors are transformed into a modified wavelet packet energy rate index first to identify the location and severity of damage. The results of numerical simulations show that the proposed damage index is a good candidate which is capable of identifying both the location and severity of damage under noise effect.
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