Bayesian based updating of hull and mooring structure parameters of semi-submersible platforms using monitoring data

Abstract

The parameters of hull structures and mooring systems of offshore platforms will change during their long service lives at sea. Therefore, if assessments of platforms are based on the original design parameters, they will become less accurate over time. As sensor technology and data analysis methods have advanced, it has become possible to update hull body and mooring parameters based on field monitoring data. However, simultaneously updating the many parameters of the hull and mooring system of semi-submersible platform is complex and computationally demanding, and underwater structure monitoring is difficult, therefore this paper proposes a parameter updating method based on floating platform response monitoring data. First, the method conducts a sensitivity analysis based on the hydrodynamic theory of semi-submersible platforms and the parameters to be updated and an objective function for the semi-submersible platform is determined. Then, updating methods for hull structure parameters and mooring parameters are established using Bayesian theorem and surrogate models. Finally, the parameters of hull and mooring structure of a semi-submersible platform in the South China Sea were updated using monitoring data and the results assessed. The proposed method effectively reduced the statistical error between measured and simulated 6-DOF motion values by more than 20% compared with the model before updating. The updated platform model was used to re-evaluate the hydrodynamic responses of the platform under multi-year return period environmental loads. The evaluation showed that during the multi-year service of the platform, the displacement of the platform had decreased by 10%, which was the main reason for the changes in wave frequency responses of the platform.