Risk Assessment of Dropped Objects on Corroded Submarine Pipelines Using Machine Learning Algorithms

This paper proposes a probabilistic model based on machine learning algorithms to estimate the risk associated with different levels of damage (per DNV-RP-F۱۰۱) due to a dropped-object impact on subsea pipelines. The model is generalized by considering a wide range of pipeline geometric and mechanical specifications, corrosion conditions, and various possible impact scenarios. Multiple machine learning algorithms—including Linear Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, and Gradient Boosting—were evaluated, with Random Forest demonstrating the highest accuracy. The analysis of how pipeline characteristics influence the probability of different damage levels provides a basis for decision-making on implementing preventive measures to reduce damage probability during the pipeline design stage

کلیدواژه ها:

Submarine pipeline ، dropped object ، Machine Learning (ML) ، Monte Carlo Simulation (MCS) ، Risk assessment ، pitting corrosion