关键词: 海上石油平台, 冰激振动, 风险等级预测, 贝叶斯网络

Abstract: Ice-induced vibration is a significant risk factor for the operational safety and structural stability of offshore oil platforms. If the hazards posed by sea ice, currents, and other environmental loads are not properly assessed, offshore platforms may suffer catastrophic consequences such as collapse, oil spills, and casualties. Due to the uncertainty of ice load and ice-induced vibration, traditional numerical analysis and statistical prediction methods struggle to simultaneously meet the requirements for high-precision early warning and timeliness in ice-induced vibration risk assessment. Therefore, based on the measured winter sea environment data of the JZ20-2MUQ platform in the Bohai Sea, this paper uses the interpretive structural model (ISM) and the expectation maximization (EM) algorithm to construct a Bayesian network model. Quantitative analysis indicates that the main risk factors affecting the vibration of the offshore oil platform in the Bayesian network are ice thickness, ice velocity, ice direction, wind speed, and tidal flow velocity and direction. Furthermore, the accuracy and validity of the model are verified. The average accuracy of the model is over 0.75, and the accuracy of the target node is above 0.9. Verification results confirm that the model has high accuracy and can provide reliable data support for risk prediction.

Key words: offshore oil platform, ice-induced vibration, risk level prediction, Bayesian network