Abstract
This paper proposes a revised counter-propagation network (CPN) model by integrating rough set in structural damage detection, applicable for processing redundant and uncertain information as well as assessing structural health states. Firstly, rough set is used in the model to deal with a large volume of data; secondly, a revised training algorithm is developed to improve the capabilities of the CPN model; and lastly, the least input vectors are input to the revised CPN (RCPN) model, hence the rough set-based RCPN is proposed in the paper. To validate the model proposed, numerical experiments are conducted, and, as a result, six damage patterns have been successfully identified in a steel frame. The influence of measurement noise, the network models adopted, and the data preprocessing methods on damage identification is also discussed in the paper. The results show that the proposed model not only has good damage detection capability and noise tolerance, but also significantly reduces the data storage requirement and saves computing time.
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