A Multi-Output Regression-Based Method for Predicting Structural Responses of Deepwater Jacket Platforms

Abstract

In real-world engineering scenarios, deep-water jacket platforms are exposed to complex, varying loads— including tension and bending—resulting from the combined actions of waves, currents, and wind. These conditions can induce structural damage and potentially compromise the integrity of the platform. This study establishes a data foundation for further analysis by utilizing the dynamic response data collected from an actual platform and applies a multi-output regression modeling approach. Six representative environmental parameters—wind speed, wind direction, current speed, current direction, significant wave height, and peak period—are selected as input features. A K-Nearest Neighbors (KNN) algorithm is employed to construct the mapping relationship between environmental conditions and structural responses. Subsequently, a multi-output regression model is developed to predict the mechanical response of the platform based on marine environmental inputs. This method effectively addresses the high computational cost associated with traditional numerical analysis, overcomes the limitations of single-output prediction models, and enhances both prediction accuracy and efficiency, thereby contributing to the safe operation of offshore platforms.