Prediction and reliability analysis of rigid pipeline response in soft soil using improved particle swarm neural...

Pipelines in soft soil are prone to deformation and failure under traffic loads. Therefore, it is highly important to accurately characterize the dynamic response of pipelines under traffic loads and reasonably evaluate their operational status. First, for rigid pipelines, a Dload subroutine is written in the FORTRAN language to accurately characterize traffic loads, and a 3D numerical analysis model of the rigid pipe‒soil system is established using ABAQUS software to simulate the dynamic response of rigid pipelines in soft soil under traffic loads. The simulation results are validated against data from field tests. Second, an improved particle swarm optimization (PSO) algorithm is introduced to optimize the weights and thresholds of a backpropagation neural network (BPNN). An improved PSO-BPNN method for predicting the dynamic response of pipelines is proposed, and the accuracy and applicability of the method are verified. Finally, using the prediction model, a reliability analysis is conducted on the dynamic response of rigid pipelines in soft soil under traffic loads. The results show that compared with smaller-diameter pipelines, larger-diameter pipelines exhibit lower axial stress and vertical displacement, with more concentrated distributions. During pipeline construction, larger-diameter pipelines should be chosen whenever possible to reduce the adverse impact of factors such as traffic loads on the dynamic response of pipelines. These research results provide a new theoretical basis and technical support for enhancing the reliability of rigid pipelines in soft soil and conducting in-depth safety assessments of pipelines under traffic loads.