Experimental study on uplift mechanism of pipeline buried in sand using high-resolution fiber optic strain sensing nerves

Abstract

Reliable assessment of uplift capacity of buried pipelines against upheaval buckling requires a valid failure mechanism and a reliable real-time monitoring technique. This paper presents a sensing solution for evaluating uplift capacity of pipelines buried in sand using fiber optic strain sensing (FOSS) nerves. Upward pipe-soil interaction (PSI) was investigated through a series of scaled tests, in which the FOSS and image analysis techniques were used to capture the failure patterns. The published prediction models were evaluated and modified according to observations in the present study as well as a database of 41 pipe loading tests assembled from the literature. Axial strain measurements of FOSS nerves horizontally installed above the pipeline were correlated with the failure behavior of the overlying soil. The test results indicate that the previous analytical models could be further improved regarding their estimations in the failure geometry and mobilization distance at the peak uplift resistance. For typical slip plane failure forms, inclined shear bands star from the pipe shoulder, instead of the springline, and have not yet reached the ground surface at the peak resistance. The vertical inclination of curved shear bands decreases with increasing uplift displacements at the post-peak periods. At large displacements, the upward movement is confined to the deeper ground, and the slip plane failure progressively changes to the flow-around. The feasibility of FOSS in pipe uplift resistance prediction was validated through the comparison with image analyses. In addition, the shear band locations can be identified using fiber optic strain measurements. Finally, the advantages and limits of the FOSS system are discussed in terms of different levels in upward PSI assessment, including failure identification, location, and quantification.

Keywords

• Pipe-soil interaction (PSI)
• Upheaval buckling
• Distributed strain sensing
• Image analysis
• Uplift resistance prediction
• Interfacial behavior