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In the bridge maintenance management decision, the health evaluation on bridge structure is far more than determination of abnormal index exceed fixed threshold, or evaluation on damage status and carrying capacity, the on-site human inspection should also be paid attention. In order to achieve more accurate and reliable health status evaluation result, it needs to take full advantage of various information and parameter. The D-S evidence information fusion was used in the paper to present comprehensive bridge health evaluation method. It fuses bridge operation status using abnormal status identification, bridge durability with residual life estimation, as well as human inspection rating, to arrive at health evaluation. The actual application examples to verify the method is also provided.

The bridge structure abnormality recognition is one of the key steps of its health assessment. Using novelty detection technique based on BP neural network, the method to identify and locate abnormal bridge status was presented. It uses non-training-data in the original sample data to generate novelty indicator and determines threshold. If the difference between detection status indicator and normal value is larger than the threshold, the structure status is determined changed. The method adapts stepwise partition identification method. The method firstly determines damage position within a small range and then analyzes sensor data in detail, so as to locate specific position. The measured data on T beam model verifies the method can accurately carry out status identification and locate cracking position under cracking load conditions.