A model for efficient risk allocation in privately financed public infrastructure projects using neuro-fuzzy techniques

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 mainly 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 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. It was found that 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, which 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 and thus has made it possible to accurately predict efficient risk allocation strategies in an ever?changing business environment, which had not been achieved in previous studies.