Fatigue assessment of buried metal pipelines under traffic loads using video monitoring data

Abstract

Traffic loads have an important impact on long-term performance of buried urban pipelines due to generation of cyclic stress and fatigue damage. As a key task for pipeline fatigue assessment, the investigation of traffic-induced pipe stress and fatigue load spectrums is always a big challenge. Focusing on the metal pipelines buried under urban intersection, an integrated framework is proposed for fatigue assessment of the pipelines under traffic loads using video monitoring data acquired by the widespread surveillance cameras in cities. The computer vision algorithms and a quarter vehicle model are first employed to identify traffic loads. Analytical pipe-soil models are then developed to calculate pipeline stress and establish the traffic-induced fatigue load spectrums of buried pipelines, based on which the Monte-Carlo simulations are finally conducted to evaluate cumulative fatigue damage and fatigue reliability. A case study on a DN500 steel pipeline buried under an urban intersection demonstrates and validates the fatigue assessment procedure. The results can provide a basis for decision support regarding maintenance and replacement of urban pipelines.