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Asmar, M E, Hanna, A S and Whited, G C (2011) New Approach to Developing Conceptual Cost Estimates for Highway Projects. Journal of Construction Engineering and Management, 137(11), 942–9.
Bogus, S M, Diekmann, J E, Molenaar, K R, Harper, C, Patil, S and Lee, J S (2011) Simulation of Overlapping Design Activities in Concurrent Engineering. Journal of Construction Engineering and Management, 137(11), 950–7.
Cass, D and Mukherjee, A (2011) Calculation of Greenhouse Gas Emissions for Highway Construction Operations by Using a Hybrid Life-Cycle Assessment Approach: Case Study for Pavement Operations. Journal of Construction Engineering and Management, 137(11), 1015–25.
Cheng, Y, Yu, C and Wang, H (2011) . Journal of Construction Engineering and Management, 137(11), 933–41.
Hallowell, M R and Calhoun, M E (2011) Interrelationships among Highly Effective Construction Injury Prevention Strategies. Journal of Construction Engineering and Management, 137(11), 985–93.
Jin, X (2011) Model for Efficient Risk Allocation in Privately Financed Public Infrastructure Projects Using Neuro-Fuzzy Techniques. Journal of Construction Engineering and Management, 137(11), 1003–14.
- Type: Journal Article
- Keywords: Risk management; Costs; Fuzzy sets; Neural networks; Infrastructure; Financial factors; Risk allocation; Transaction cost economics; Organizational capability; Fuzzy logic; Artificial neural networks; Public-private partnership (PPP);
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000365
- 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.
Kim, B and Reinschmidt, K F (2011) Combination of Project Cost Forecasts in Earned Value Management. Journal of Construction Engineering and Management, 137(11), 958–66.
Marques, R C and Berg, S (2011) Risks, Contracts, and Private-Sector Participation in Infrastructure. Journal of Construction Engineering and Management, 137(11), 925–32.
Song, Y and Chua, D K H (2011) Requirement and Availability Time-Window Analysis of Intermediate Function. Journal of Construction Engineering and Management, 137(11), 967–75.
Unsal, H I and Taylor, J E (2011) Absorptive Capacity of Project Networks. Journal of Construction Engineering and Management, 137(11), 994–1002.
Young, D A, Haas, C T, Goodrum, P and Caldas, C (2011) Improving Construction Supply Network Visibility by Using Automated Materials Locating and Tracking Technology. Journal of Construction Engineering and Management, 137(11), 976–84.