Please use this identifier to cite or link to this item: https://doi.org/10.1260/136943306778812787
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dc.titleStructural damage detection by integrating data fusion and probabilistic neural network
dc.contributor.authorJiang, S.-F.
dc.contributor.authorZhang, C.-M.
dc.contributor.authorKoh, C.G.
dc.date.accessioned2014-06-17T08:25:54Z
dc.date.available2014-06-17T08:25:54Z
dc.date.issued2006-08
dc.identifier.citationJiang, S.-F., Zhang, C.-M., Koh, C.G. (2006-08). Structural damage detection by integrating data fusion and probabilistic neural network. Advances in Structural Engineering 9 (4) : 445-457. ScholarBank@NUS Repository. https://doi.org/10.1260/136943306778812787
dc.identifier.issn13694332
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/66245
dc.description.abstractOver the past two decades, multi-sensor data fusion method has attracted increasing attention to structural health monitoring due to its inherent capabilities in extracting information from different sources and integrating them into a consistent, accurate and intelligible data set. Meanwhile, since the probabilistic neural network (PNN) describes measurement data in a Bayesian probabilistic approach, it has been successfully applied to structural damage detection (Jiang et al. 2004; Klenke et al. 1996; Ko et al. 1999; Ni et al. 2001). In order to make full use of multi-sensor data (or information) from multi-resource and to improve the diagnosis accuracy of the health conditions for complex structures, it is advisable to combine these methods and exploit their individual advantages. In this paper, a 5-phase complex structural damage detection method by integrating data fusion and PNN is developed and implemented. The proposed method is then applied to damage detection and identification of two simulation examples. The result shows that the proposed method is feasible and effective for damage identification.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1260/136943306778812787
dc.sourceScopus
dc.subjectDamage detection
dc.subjectData fusion
dc.subjectEnergy feature
dc.subjectFusion algorithms
dc.subjectProbabilistic neural network
dc.subjectWavelet analysis
dc.typeArticle
dc.contributor.departmentCIVIL ENGINEERING
dc.description.doi10.1260/136943306778812787
dc.description.sourcetitleAdvances in Structural Engineering
dc.description.volume9
dc.description.issue4
dc.description.page445-457
dc.description.codenASEDD
dc.identifier.isiut000239747700001
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