Collision failure risk analysis of falling object on subsea pipelines based on machine learning scheme

Abstract

Platform falling object collision on offshore pipelines are catastrophic to the environment and economy. Based on finite element analysis and machine learning algorithms, a quantitative analysis model is proposed to quantify failure risk. To consider the uncertainties and nonlinear effects in the collision events, the Latin Hypercube Sampling technique and the finite element simulation is coupled to draw the sample space. Then four machine learning models are developed and the prediction abilities in the pipeline response are compared. The genetic programming shows the best performance with the relative absolute error of 0.04–0.05, which is integrated into Monte Carlo Simulation to complete the risk analysis. This quantitative analysis model is verified with a method and indicates good consistency and potential in considering nonlinear effects and pipe–soil interactions. Effects of related factors on failure risk are examined, including seabed flexibility, burial depth, acceptable criterion, and sensibility of basic variables. Compared with the method recommended by the Det Norkske Veritas, the proposed model can account for the seabed flexibility effect, and the failure risk declined by 23.6%. The increase in burial depth affects risk reduction significantly but is limited under a strict criterion. The fitting equations of burial depth and failure probabilities as well as different acceptable criteria are proposed for safety design. Sensibility analysis of the basic variables reveals that the quality of wall thickness and pipeline diameter are important to failure risk.