Fuzzy-Fault Tree Model For Risk Analysis With Application To Rural BOT Projects

In nearly a decade, China’s urban-rural gap has become larger and larger. The gap between urban and rural areas is not only reflected on the income, but also in medical and health care, education, employment, consumption, infrastructure construction areas. Chinese government has put forward a series of policies to promoting the rural development, which encourages private capital to enter in many fields especially rural infrastructure construction, in order to reduce the government financial burden. So, the BOT application on rural construction field can solve the short of government investment and also disperse the project risk. However, as the BOT mode for rural infrastructure construction is just starting up and relevant policies, laws and regulations are not perfect, Project Company involved in BOT would take a lot of risks during the process of project construction, operation and transfer. Therefore, scientific and effective risk assessment of rural BOT project is very important, which provides the foundation for risk control of BOT project.This paper focuses on identifying and evaluating the risks of rural BOT projects from the perspective of the project company. Firstly, the research background, significance and the purpose of combining BOT mode with new countryside construction are introduced. Then, the BOT financing mode, the risk management process, risk identification and evaluation methods are described systematically. A review of the related literature research are made at last.In order to solve the problem that rural BOT project is lack of history data of risk events, we construct the fuzzy fault tree model to analyze risk of rural BOT project. We introduce the establishment of the fault tree, analyze how the nature language be transformed into triangular fuzzy number in details. At last, we analyze the risk assessment process based on the fuzzy fault tree model.On the basis of the original fuzzy fault tree model, the expert confidence and conditional probability are further introduced to effectively solve the uncertainty for experts’evaluation of various risk events and the problem which makes the top event probability too small or big when it is connected by the single’OR’ or ’AND’ gate. The expert confidence and the conditional probability are discussed further to explain how the expert confidence modifies the fuzzy membership function and how conditional probability works to calculate the probability of upper bound. Finally, on the base of rural BOT project risk identification, we use fuzzy fault tree and its modified model to asses risks in specific instance. The results show that the modified fuzzy fault tree model has important practical significance and value in the calculation of top event probability and positioning risk factors.