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Risk Assessment of Dropped Objects on Corroded Submarine Pipelines Using Machine Learning Algorithms
Pedram Edalat *1 , Erfan Rezaei1 , Alireza Abyari Bidgoli1
1- Mechanical Engineering Department, Petroleum University of Technology, Abadan, Iran
Abstract:   (116 Views)
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
Keywords: Submarine pipeline, dropped object, Machine Learning (ML), Monte Carlo Simulation (MCS), Risk assessment, pitting corrosion
Full-Text [PDF 1444 kb]   (56 Downloads)    

Highlights:
  • A probability-based framework for evaluating the damage levels in submarine pipelines based on machine learning algorithms is presented.
  • The framework employs Monte Carlo Simulation (MCS) to generate a generalized data set based on uncertainties and a wide range of pipelines with their geometric and mechanical specifications, corrosion conditions, and various possible conditions of object impact.
  • Using Machine Learning (ML)-based error methods to study the efficiency of different ML algorithms in the predictive model of damage level probability assessment.
  • The study offers an actionable tool for implementing preventive measures in the pipeline design phase.

Type of Study: Research Paper | Subject: Offshore Structure
Received: 2025/08/23 | Accepted: 2026/04/11
Audio file of the article abstract [MP3 1692 KB]  (6 Download)
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