Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/103345
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dc.contributorDepartment of Building and Real Estate-
dc.creatorUtama, WPen_US
dc.creatorChan, APCen_US
dc.creatorZahoor, Hen_US
dc.creatorGao, Ren_US
dc.creatorJumas, DYen_US
dc.date.accessioned2023-12-11T00:33:18Z-
dc.date.available2023-12-11T00:33:18Z-
dc.identifier.issn0969-9988en_US
dc.identifier.urihttp://hdl.handle.net/10397/103345-
dc.language.isoenen_US
dc.publisherEmerald Publishing Limiteden_US
dc.rights© Emerald Publishing Limited. This AAM is provided for your own personal use only. It may not be used for resale, reprinting, systematic distribution, emailing, or for any other commercial purpose without the permission of the publisher.en_US
dc.rightsThe following publication Utama, W.P., Chan, A.P.C., Zahoor, H., Gao, R. and Jumas, D.Y. (2019), "Making decision toward overseas construction projects: An application based on adaptive neuro fuzzy system", Engineering, Construction and Architectural Management, Vol. 26 No. 2, pp. 285-302 is published by Emerald and is available at https://doi.org/10.1108/ECAM-01-2018-0016.en_US
dc.subjectInternational constructionen_US
dc.subjectSimulationen_US
dc.titleMaking decision toward overseas construction projects : an application based on adaptive neuro fuzzy systemen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage285en_US
dc.identifier.epage302en_US
dc.identifier.volume26en_US
dc.identifier.issue2en_US
dc.identifier.doi10.1108/ECAM-01-2018-0016en_US
dcterms.abstractPurpose: The purpose of this paper is to introduce a decision support aid for deciding an overseas construction project (OCP) using an adaptive neuro fuzzy inference system (ANFIS)-
dcterms.abstractDesign/methodology/approach: This study presents an ANFIS approach as a decision support aid for assessment of OCPs. The processing data were derived from 110 simulation cases of OCPs. In total, 21 international factors observed from a Delphi survey were determined as assessment variables to examine the cases. The experts were involved to evaluate and judge whether the company should Go or Not Go for an OCP, based on the different parameter scenarios given. To measure the performance of the ANFIS model, root mean square error (RMSE) and coefficient of correlation (R) were employed.-
dcterms.abstractFindings: The result shows that optimum ANFIS model indicating RMSE and R scores adequately near between 0 and 1, respectively, was obtained from parameter set of network algorithm with two input membership functions, Gaussian type of membership function and hybrid optimization method. When the model tested to nine real OCPs data, the result indicates 88.89 percent accurate.-
dcterms.abstractResearch limitations/implications: The use of simulation cases as data set in development the model has several advantages. This technique can be replicated to generate other case scenarios which are not available publicly or limited in terms of quantity.-
dcterms.abstractOriginality/value: This study evidences that the developed ANFIS model can predict the decision satisfactorily. Therefore, it can help companies’ management to make preliminary assessment of an OCP.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEngineering, construction and architectural management, 26 Mar. 2019, v. 26, no. 2, p. 285-302en_US
dcterms.isPartOfEngineering, construction and architectural managementen_US
dcterms.issued2019-03-26-
dc.identifier.scopus2-s2.0-85062284760-
dc.identifier.eissn1365-232Xen_US
dc.description.validate202312 bcch-
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberBRE-0621-
dc.description.fundingSourceRGCen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS24255802-
dc.description.oaCategoryGreen (AAM)en_US
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