Corrosion is a significant concern causing tremendous losses to all pipeline operators. To combat this operational issue, new methods and tools are needed to analyze and model degradation, to predict failure, and finally to develop strategies for prevention, control, and mitigation of corrosion in pipelines. A practical inspection and maintenance program is crucial to prevent pipeline failures due to corrosion. Risk-based inspection (RBI) is an increasingly popular and trusted method to assess and develop inspection plans. However, the determination of optimal inspection intervals is still challenging in RBI. The present study aims to develop a dynamic Bayesian network (DBN)-based approach for optimization of inspection intervals. Based on inline inspection data and analytical corrosion propagation models, DBN is applied for the estimation of both the internal and external corrosion damage as well as the probability of failure (PoF). The cost of failure (CoF) is estimated based on typical cost categories relevant to pipeline accidents. Risk is calculated as the product of PoF and CoF. A utility function to combine both the risk and the annual cost of the inspection program is also developed. The optimal interval can be found based on the curve of the utility function. The proposed approach is demonstrated through a real-world case study on an operating pipeline. (C) 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.