Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155269
Title: Performance risk assessment in public–private partnership projects based on adaptive fuzzy cognitive map
Authors: Chen, Hongyu
Zhang, Limao
Wu, Xianguo
Keywords: Engineering::Civil engineering
Issue Date: 2020
Source: Chen, 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.2020.106413
Project: M4082160.030
M4011971.030
Journal: Applied Soft Computing Journal
Abstract: High 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.
URI: https://hdl.handle.net/10356/155269
ISSN: 1568-4946
DOI: 10.1016/j.asoc.2020.106413
Rights: © 2020 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

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