Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155269
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dc.contributor.authorChen, Hongyuen_US
dc.contributor.authorZhang, Limaoen_US
dc.contributor.authorWu, Xianguoen_US
dc.date.accessioned2022-03-07T07:46:52Z-
dc.date.available2022-03-07T07:46:52Z-
dc.date.issued2020-
dc.identifier.citationChen, H., Zhang, L. & Wu, X. (2020). Performance risk assessment in public–private partnership projects based on adaptive fuzzy cognitive map. Applied Soft Computing Journal, 93, 106413-. https://dx.doi.org/10.1016/j.asoc.2020.106413en_US
dc.identifier.issn1568-4946en_US
dc.identifier.urihttps://hdl.handle.net/10356/155269-
dc.description.abstractHigh complexity exists underlying public–private partnership (PPP) projects due to their huge scale, large investment, and long-term relationships among various participants, leading to difficulty in managing PPP project performance risk. A robust model that integrates the structural equation model (SEM) and fuzzy cognitive map (FCM) is proposed to perceive and assess the performance risk in PPP projects. SEM is used to learn causal relationships among critical factors representing PPP project performance from the data given. Based on the well-verified SEM, an adaptive FCM model consisting of 14 observed variables and 5 latent variables is built. The proposed approach is capable of performing predictive, diagnostic, and hybrid analysis in various scenarios. Results indicate that variables, including project characteristics (A), project participants (B), project input (C), and project progress (D), all display positive correlations with the target performance (T). Particularly, variables C and D are identified to be more sensitive in ensuring the project satisfactory performance than variables A and B. The optimal risk mitigation strategy can be discovered when the project performance is under an unsatisfactory level. It is found that upgrading the variable with a higher priority would be more efficient to improve the target performance than the variable with a lower priority, which is helpful in both generic and specific situations. The novelty of this research lies in the development of an adaptive FCM model that is capable of learning casual relationships from observed data and assessing risk subjected to uncertainty, subjectivity, and interdependence. The developed model can be used to provide insights into a better understanding of risk mitigation strategies through what-if scenario analysis, enabling to enhance the likelihood of success in PPP projects.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.language.isoenen_US
dc.relationM4082160.030en_US
dc.relationM4011971.030en_US
dc.relation.ispartofApplied Soft Computing Journalen_US
dc.rights© 2020 Elsevier B.V. All rights reserved.en_US
dc.subjectEngineering::Civil engineeringen_US
dc.titlePerformance risk assessment in public–private partnership projects based on adaptive fuzzy cognitive mapen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.identifier.doi10.1016/j.asoc.2020.106413-
dc.identifier.scopus2-s2.0-85084939514-
dc.identifier.volume93en_US
dc.identifier.spage106413en_US
dc.subject.keywordsRisk Assessmenten_US
dc.subject.keywordsFuzzy Cognitive Mapen_US
dc.description.acknowledgementThe Start-Up Grant at Nanyang Technological University, Singapore (No. M4082160.030) and the Ministry of Education Tier 1 Grant, Singapore (No. M4011971.030) are acknowledged for their financial support of this research.en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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