Quantitative risk assessment due to external corrosion requires an estimation of corrosion rates which is a challenging task for pipeline engineers because of the uncertainty in data related to environmental and physical variables such as soil type, drainage, soil chemistry, CP effectiveness, coating type and coating properties. Unfortunately, the research into quantitative assessment of external corrosion rates and the probability of failure of a buried pipeline is limited and has not progressed significantly. The reason is the complex mechanism of external corrosion, numerous factors affecting it, and the uncertainty in the knowledge of the variables. There is the need of a probabilistic external corrosion methodology that compiles in one framework field data, multiple analytical methods (i.e. mechanistic models from various sources and multiple risk modelling methods are combined in one unified method) and expert knowledge. In this paper a novel model for quantitative assessment of corrosion rates using Bayesian network method is proposed. Bayesian Networks are graphical models based on cause-consequence relationships that are quantified through conditional probability tables based on a combination of information available from subject matter experts, mechanistic models, and field data. A case study is presented to assess the probability of failure due to external corrosion in a crude oil buried pipeline located in Eastern China. The model was validated using in-line inspection data.