Identification of bridges' damage by dynamic fingerprints and bayes data fusion

Using a single index to evaluate bridge safety is likely to draw a false conclusion due to the effect of measurement noise. Thus, an identification method of bridge damage based on the Bayes theory was proposed, which includes location identification and degree identification. First, the Bayes formula was used to fuse the normalized dynamic fingerprints and identify the damage locations. Then, only the fingerprints of damaged locations were extracted to construct the Bayesian network and the conditional probability of every node was calculated in order to identify the damage degrees. By simulating a simple beam, the result shows that the proposed method has a good anti-noise performance, especially can accurately locate tiny damages and distinguish the damages with slight differences.