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Abstract Bridge alarming system data sets are often interspersed with noise. If it does not consider noise, the data process algorithm in alarming system may not provide accurate results. A new hybrid approach comprising of neural rules, data classification and data evaluation was proposed to carry out data selection. Specifically, the neural rules remove noise data by applying the principles of an intelligent reasoning process. Data classification classifies the patterns in the reduced datasets based on the similarity index. The data evaluation applies curvature model to extract kernel information of bridge structure. The simulation results demonstrate that the proposed approach produces good classification accuracy and a higher level of consistency to bridge damage condition.
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Received: 10 June 2012
Published: 30 October 2012
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