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Low-cycle fatigue evaluation for girth-welded pipes based on the structural strain method considering cyclic material behavior

  • Lee, Jin-Ho (Department of Naval Architecture and Ocean Engineering, Pusan National University) ;
  • Dong, Pingsha (Department of Naval Architecture and Marine Engineering, University of Michigan) ;
  • Kim, Myung-Hyun (Department of Naval Architecture and Ocean Engineering, Pusan National University)
  • Received : 2020.03.12
  • Accepted : 2020.08.29
  • Published : 2020.12.31

Abstract

One of the main concerns in the structural integrity of offshore pipelines is mechanical damage from external loads. Pipelines are exposed to fatigue failure in welded joints due to geometric discontinuity. In addition, fatigue loads such as currents, waves, and platform motions may cause significant plastic deformation and fracture or leakage within a relatively low-cycle regime. The 2007 ASME Div. 2 Code adopts the master S―N curve for the fatigue evaluation of welded joints based on the mesh-insensitive structural stress. An extension to the master S―N curve was introduced to evaluate the low-cycle fatigue strength. This structural strain method uses the tensile properties of the material. However, the monotonic tensile properties have limitations in describing the material behavior above the elastic range because most engineering materials exhibit hardening or softening behavior under cyclic loads. The goal of this study is to extend the cyclic stress-strain behavior to the structural strain method. To this end, structural strain-based procedure was established while considering the cyclic stress-strain behavior and compared to the structural strain method with monotonic tensile properties. Finally, the improved prediction method was validated using fatigue test data from full-scale girth-welded pipes.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) through GCRC-SOP (No. 2011e0030013). This work was also supported by the National Research Foundation of Korea (NRF) grant, which is also funded by the Korea government (MOE) (NRF-2017R1D1A1B03035811).

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