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dc.contributor.authorPalaneeswaran, E.
dc.contributor.authorLove, Peter
dc.contributor.authorKumaraswamy, M.
dc.contributor.authorNg, T.
dc.date.accessioned2017-01-30T12:24:43Z
dc.date.available2017-01-30T12:24:43Z
dc.date.created2014-10-28T02:23:17Z
dc.date.issued2008
dc.identifier.citationPalaneeswaran, E. and Love, P. and Kumaraswamy, M. and Ng, T. 2008. Mapping rework causes and effects using artificial neural networks. Building Research and Information. 36 (5): pp. 450-465.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/21344
dc.identifier.doi10.1080/09613210802128269
dc.description.abstract

Rework can have adverse effects on the performance and productivity of construction projects. Techniques such as artificial neural networks (ANN) are widely used for prediction and classification problems and thus can be used to map the causes and effects of rework. The traditional back propagation neural network and general regression neural network data from 112 Hong Kong construction projects are used to examine the influence of rework causes on the various project performance indicators such as cost overrun, time overrun, and contractual claims. The results from this research could be used to develop forecasting systems and appropriate intelligent decision support frameworks for enhancing performance in construction projects. Furthermore, analysis of the neural network results indicates that the general regression neural network architecture is better suited for modelling rework causes and their impacts on project performance.

dc.publisherRoutledge
dc.titleMapping rework causes and effects using artificial neural networks
dc.typeJournal Article
dcterms.source.volume36
dcterms.source.startPage450
dcterms.source.endPage465
dcterms.source.issn0961-3218
dcterms.source.titleBuilding Research and Information
curtin.departmentSchool of Built Environment
curtin.accessStatusFulltext not available


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