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.

平台掉落物体在海上管道上的碰撞对环境和经济造成灾难性影响。基于有限元分析和机器学习算法，提出了一种定量分析模型来量化故障风险。为了考虑碰撞事件中的不确定性和非线性影响，将拉丁超立方体采样技术与有限元模拟相结合以绘制样本空间。然后开发了四个机器学习模型，并比较了管道响应中的预测能力。遗传规划显示出最佳性能，相对绝对误差为0.04-0.05，将其集成到Monte Carlo Simulation中以完成风险分析。该定量分析模型已通过一种方法进行了验证，并在考虑非线性效应和管土相互作用时具有良好的一致性和潜力。检查了相关因素对失效风险的影响，包括海床的柔韧性，埋深，可接受的标准以及基本变量的敏感性。与Det Norkske Veritas推荐的方法相比，该模型可以解释海床的柔韧性效应，失效风险降低了23.6％。埋葬深度的增加会显着降低风险，但在严格的标准下是有限的。为安全设计提出了埋深和破坏概率的拟合方程，以及不同的可接受标准。