Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80450
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dc.contributorDepartment of Building and Real Estate-
dc.creatorXiao, X-
dc.creatorWang, F-
dc.creatorLi, H-
dc.creatorSkitmore, M-
dc.date.accessioned2019-03-26T09:17:15Z-
dc.date.available2019-03-26T09:17:15Z-
dc.identifier.issn1392-3730-
dc.identifier.urihttp://hdl.handle.net/10397/80450-
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.rights© 2018 The Author(s). Published by VGTU Pressen_US
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.rightsThe following publication Xiao, X., Wang, F., Li, H., & Skitmore, M. (2018). Modelling the stochastic dependence underlying construction cost and duration. Journal of Civil Engineering and Management, 24(6), 444-456 is available at https://dx.doi.org/10.3846/jcem.2018.5712en_US
dc.subjectProbabilistic modellingen_US
dc.subjectCost-durationen_US
dc.subjectStochastic dependenceen_US
dc.subjectNataf distributionen_US
dc.titleModelling the stochastic dependence underlying construction cost and durationen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage444-
dc.identifier.epage456-
dc.identifier.volume24-
dc.identifier.issue6-
dc.identifier.doi10.3846/jcem.2018.5712-
dcterms.abstractConstruction cost and duration are two critical project indicators. It is acknowledged that these two indicators are closely dependent and highly uncertain due to various common factors and limited data for explanatory model calibration. However, the stochastic dependence underlying construction cost and duration is usually ignored and the subsequent probabilistic analysis can be misleading. In response, this study develops a Nataf distribution model of building cost and duration, in which the uncertainties of total cost, unit cost, and duration are respectively quantified by univariate distribution fitting, while their stochastic dependence is inferred by maximum likelihood estimation. 'this method is applied to the costs and durations of 77 China residential building projects completed between 2011 and 2016. The goodness of fit test illustrates that the data conform well to the developed model. 'I he conditional distributions of cost and duration are then derived and the corresponding conditional expectations and variances are given. The results provide the distribution of building costs for a desired duration and the expected duration given a budget. This, together with the ability to update probabilities when new project information is available, confirms the potential of the proposed model to benefit precontract decision making from a risk perspective.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of civil engineering and management, 2018, v. 24, no. 6, p. 444-456-
dcterms.isPartOfJournal of civil engineering and management-
dcterms.issued2018-
dc.identifier.isiWOS:000451298800002-
dc.identifier.eissn1822-3605-
dc.description.validate201903 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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