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The identification of high risk regions is an important aim of risk-based inspections (RBIs) in pipeline networks. As the most vital part of risk-based inspections, risk assessment makes a significant contribution to achieving this aim. Accurate assessment can target high risk inspected regions so that limited resources can mitigate considerable risks in the face of increased spatial distribution of a pipeline network. However, the existing approaches for risk assessment face grave challenges due to a lack of sufficient data and an assessment’s vulnerability to human biases and errors. This paper attempts to tackle those challenges through spatial statistics, which is used to estimate the uncertainty of risk based on a dataset of locations of pipeline network failure events without having to acquire additional data. The consequence of risk in each inspected region is measured by the total cost caused by the failure events that have occurred in the region, which is also calculated in the assessment. Then, the risks of the different inspected regions are obtained by integrating the uncertainty and consequences. Finally, the feasibility of our approach is validated in a case study. Our results in the case study demonstrate that uncertainty is less instructive for prioritizing pipeline inspections than the consequences of risk due to the low significant difference in risk uncertainty in different regions. Our results also have implications for understanding the correlation between the spatial location and consequences of risk.

A Spatial Statistic Based Risk Assessment Approach to Prioritize the Pipeline Inspection of the Pipeline Network