Abstract
There are multitudinous parameters during the processing of monitoring the bridge, which leads to difficulty in predicting the state of bridges. Thus, it is difficult to acquire the bridge health state accurately according to traditional methods. In order to coalesce different types of monitoring parameters or the asynchronous data monitored by the same type parameters, and obtain the concordance evaluation of the bridge health state, this paper presents a fusion assessment method for the bridge health state based on two step neural networks ensemble. The method reduces the complexity of process on the multisource fusion of the monitoring data and improves the accuracy of the bridge health state assessment.
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Shan, L., Hui, Z., Hong, R. (2012). Fusion Assessment Methods for Bridge Health State Based on Two Step Neural Networks Ensemble. In: Liu, C., Wang, L., Yang, A. (eds) Information Computing and Applications. ICICA 2012. Communications in Computer and Information Science, vol 307. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34038-3_14
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DOI: https://doi.org/10.1007/978-3-642-34038-3_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34037-6
Online ISBN: 978-3-642-34038-3
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