International Journal of Maritime Technology

en Risk Assessment of Dropped Objects on Corroded Submarine Pipelines Using Machine Learning Algorithms Offshore Structure مقاله پژوهشي Research Paper This paper proposes a probabilistic model based on machine learning algorithms to estimate the risk associated with different levels of damage (per DNV-RP-F101) 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 Submarine pipeline,dropped object,Machine Learning (ML),Monte Carlo Simulation (MCS),Risk assessment,pitting corrosion 12 28 http://ijmt.ir/browse.php?a_code=A-10-627-6&slc_lang=en&sid=1 Pedram Edalat edalat@put.ac.ir 10031947532846004498 10031947532846004498 Yes Mechanical Engineering Department, Petroleum University of Technology, Abadan, Iran Erfan Rezaei erfanrezaei1380@gmail.com 10031947532846004499 10031947532846004499 No Mechanical Engineering Department, Petroleum University of Technology, Abadan, Iran Alireza Abyari Bidgoli abyari1381@gmail.com 10031947532846004500 10031947532846004500 No Mechanical Engineering Department, Petroleum University of Technology, Abadan, Iran