Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/50662
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dc.titleA model for construction project budget and schedule performances using fuzzy data
dc.contributor.authorChua, D.K.H.
dc.contributor.authorKog, Y.C.
dc.contributor.authorLoh, P.K.
dc.date.accessioned2014-04-23T07:07:13Z
dc.date.available2014-04-23T07:07:13Z
dc.date.issued2001
dc.identifier.citationChua, D.K.H.,Kog, Y.C.,Loh, P.K. (2001). A model for construction project budget and schedule performances using fuzzy data. Civil Engineering and Environmental Systems 18 (4) : 303-329. ScholarBank@NUS Repository.
dc.identifier.issn10286608
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/50662
dc.description.abstractEnsuring satisfactory budget and schedule performance are two major challenges for construction projects. On this issue, predictive models for budget and schedule performances can provide assistance in the appropriate allocation of project management resources. Two neural network models for construction budget and schedule performances using fuzzy data have been developed in the present study. These models consist of eight and five key determinants of project outcome, respectively. A combined fuzzy index (CFI) approach is introduced for data representation. The CFI for an input or output measurement can be derived using the fuzzy number membership degree concept. This approach permits a gradual change of scale value in the classification. Several definitions to the fuzzy numbers are experimented. The results reveal that this approach is a feasible alternative for neural network implementations dealing with quantitative measurements. Examples of using the models for guidance in project management are presented. These include the effect of amount of design completed before construction starts on budget performance, and the effect of amount of time devoted by project manager on schedule performance. The trade-off effect between two key determinants on project outcome can also be studied using the models.
dc.sourceScopus
dc.subjectBudget performance
dc.subjectFuzzy set
dc.subjectNeural network
dc.subjectProject management
dc.subjectSchedule performance
dc.typeArticle
dc.contributor.departmentCIVIL ENGINEERING
dc.contributor.departmentDEAN'S OFFICE (ENGINEERING)
dc.description.sourcetitleCivil Engineering and Environmental Systems
dc.description.volume18
dc.description.issue4
dc.description.page303-329
dc.description.codenCEESF
dc.identifier.isiutNOT_IN_WOS
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