Model for Efficient Risk Allocation in Privately Financed Public Infrastructure Projects Using Neuro-Fuzzy Techniques
Publication: Journal of Construction Engineering and Management
Volume 137, Issue 11
Abstract
Risk allocation plays a critical role in privately financed public infrastructure projects. Project performance is contingent on whether the adopted risk-allocation strategy can lead to efficient risk management. Founded primarily on the transaction cost economics, a theoretical framework was recently developed to model the risk allocation decision-making process in privately financed public infrastructure projects. In this paper, a neuro-fuzzy model adapted from an adaptive neuro-fuzzy inference system was further designed based on the framework by combining fuzzy logic and artificial neural network techniques. Real project data were used to train and validate the neuro-fuzzy models. To evaluate the neuro-fuzzy models, multiple linear regression models and fuzzy inference systems established in previous studies were used for a systematic comparison. The neuro-fuzzy models can serve the purpose of forecasting efficient risk-allocation strategies for privately financed public infrastructure projects at a highly accurate level that multiple linear regression models and fuzzy inference systems could not achieve. This paper presents a significant contribution to the body of knowledge because the established neuro-fuzzy model for efficient risk allocation represents an innovative and successful application of neuro-fuzzy techniques. It is thus possible to accurately predict efficient risk-allocation strategies in an ever-changing business environment, which had not been achieved in previous studies.
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Acknowledgments
The author would like to extend his gratitude to all the respondents and interviewees for their valuable contributions to this research project and to the anonymous reviewers for their insightful comments on the current paper.
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Published In
Journal of Construction Engineering and Management
Volume 137 • Issue 11 • November 2011
Pages: 1003 - 1014
Copyright
© 2011 American Society of Civil Engineers.
History
Received: Dec 14, 2009
Accepted: Feb 2, 2011
Published online: Feb 3, 2011
Published in print: Nov 1, 2011
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