7. Predicting residual axial resistance of corroded steel offshore tubular members
DOI:
https://doi.org/10.61591/jslhu.26.1089Từ khóa:
Kết cấu giàn khoan ngoài khơi; sự xuống cấp do ăn mòn; Sức kháng trục; Học máy; Dữ liệu kiểm tra thực địaTóm tắt
Offshore jacket tubular members are continuously exposed to harsh marine environments, where corrosion significantly affects structural integrity. Therefore, accurate assessment of the residual axial remaining resistance of corroded steel members is crucial. 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 output variable is the calculated axial resistance 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 steel offshore structures.
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