Field experiment and numerical investigation on the mechanical response of buried pipeline under traffic load. (December 2022) - Zhang, Dong; Liu, Xiaoben; Yang, Yue; Shi, Ning; Jiang, Jinxu; Chen, Pengchao; Wu, Xuejian; Gao, Hui; Zhang, Hong
Highlights: A field experiment of full-size buried pipelines under traffic loads is carried out. A three-dimensional finite element model of traffic load considering pipe-soil nonlinearity is established. The effects of diameter, wall thickness, internal pressure, buried depth and vehicle mass on the mechanical response of buried pipelines are investigated. A PSO-SVR hybrid machine learning model is established to accurately predict the axial stress of buried pipelines. Abstract: The acceleration of global urbanization and an increasing intersection of roads and buried pipelines pose a certain threat to the operation safety of pipelines. In this paper, the mechanical response of the X65 pipeline under empty-load, half-load, and full-load traffic loads is investigated via field experiments. The numerical model considering the nonlinear interaction between the pipe and the soil is established. Moreover, an accurate simulation of dynamic vehicle load is achieved by Dload user-defined subroutine. The error between the numerical calculation results and the field experiment results is less than 5 %. On this basis, the influence of five important factors, such as vehicle mass and buried depth, on the axial stress of pipelines is further explored. The results show that the traffic load leads to vertical bending deformation of the pipeline and local deformation of the wheel rolling position. The axial tensile stress generated at the bottom of the pipe is superimposed on the axial tensile stress generated by the internal pressure Poisson effect. The vehicle mass most significantly affects the axial stress of the pipeline, which is generally less than 100 MPa. Furthermore, the operating conditions of the pipeline with large coverage in service are calculated based on Python and ABAQUS co-simulation calculation of 1280 working conditions. The particle swarm optimization-support vector regression (PSO-SVR) hybrid machine learning model is used to accurately predict the axial stress of the buried pipeline under traffic load, and the corresponding correlation coefficient reaches 99.77 %. The research results reveal the mechanical response law of buried pipeline under traffic load, which can provide a basis for the applicability evaluation of buried pipeline in service under traffic load, and accurately evaluate the safety state of buried pipeline.