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dc.contributor.author Ranasinghe, M
dc.contributor.author de Silva, CR
dc.contributor.author De Silva, N
dc.date.accessioned 2014-06-27T15:49:40Z
dc.date.available 2014-06-27T15:49:40Z
dc.date.issued 2014-06-27
dc.identifier.issn 2044-124X en_US
dc.identifier.uri http://dl.lib.mrt.ac.lk/handle/123/10128
dc.description.abstract Purpose: Artificial neural network (ANN) has been used for risk analysis in various applications such as engineering, financial and facilities management. However, use of a single network has become less accurate when the problem is complex with a large number of variables to be considered. Ensemble neural network (ENN) architecture has proposed to overcome these difficulties of solving a complex problem. ENN consists of many small "expert networks" that learn small parts of the complex problem, which are established by decomposing it into its sub levels. This paper seeks to address these issues. Design/methodology/approach: ENN model was developed to analyze risks in maintainability of buildings which is known as a complex problem with a large number of risk variables. The model comprised four expert networks to represent building components of roof, façade, internal areas and basement. The accuracy of the model was tested using two error terms such as network error and generalization error. Findings: The results showed that ENN performed well in solving complex problems by decomposing the problem into its sub levels. Originality/value: The application of ensemble network would create a new concept of analyzing complex risk analysis problems. The study also provides a useful tool for designers, clients, facilities managers/maintenance managers and users to analyze maintainability risks of buildings at early stages. © Emerald Group Publishing Limited. en_US
dc.language.iso en en_US
dc.source.uri http://www.researchgate.net/publication/259077379_Use_of_ANNs_in_Complex_Risk_Analysis_Applications en_US
dc.title Use of ANNs in complex risk analysis applications en_US
dc.identifier.year 2013 en_US
dc.identifier.journal Built Environment Project and Asset Management en_US
dc.identifier.issue 1 en_US
dc.identifier.volume 3 en_US
dc.identifier.pgnos pp. 123-140 en_US
dc.identifier.email endds@uom.lk en_US


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