Structural Integrity and Reliability Analysis
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Abstract
Abstract
Offshore jacket tubular members are continuously exposed to harsh marine environments, where corrosion significantly affects structural integrity. Therefore, accurate assessment of the residual axial resistance of corroded members is essential for ensuring safe operation and effective maintenance planning of offshore structures. This study proposes a data-driven analytical framework to examine the relationship between corrosion parameters obtained from ultrasonic thickness gauge (UTG) inspections and the residual axial resistance of offshore tubular members. Corrosion-related geometric descriptors extracted from inspection data are used as input features for several machine learning models, including Linear Regression, Random Forest, Support Vector Regression, and XGBoost. The axial resistance is calculated using the ISO 19902 based on the measured geometric properties. The results demonstrate strong predictive performance across the models (R² > 0.99) and confirm that effective thickness and geometric parameters dominate the residual load-carrying capacity. This study highlights the potential of machine learning as a complementary tool for interpreting field inspection data in the structural integrity assessment of offshore structures
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@article{Dat2026DATA,
title = {DATA-DRIVEN SURROGATE MODEL FOR ESTIMATING RESIDUAL AXIAL RESISTANCE OF CORRODED OFFSHORE TUBULAR MEMBERS FROM INSPECTION DATA},
author = {Le Tran Minh Dat},
journal = {Tạp chí Khoa học Lạc Hồng},
year = {2026},
doi = {10.61591/jslhu.26.1089},
url = {https://doi.org/10.61591/jslhu.26.1089}
}FAQ
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