Due to the complexity of ocean environmental loading models and the nonlinearity and empirical parameters involved in hydrodynamic numerical modeling and model testing, many uncertainties still exist in the design and operation of floating platforms. On-site prototype measurements provide a valid strategy for obtaining accurate environmental loading parameters and floater motion responses. A prototype monitoring system was built as part of a joint industrial project in the South China Sea. Long-term ocean environmental loading parameter data and structural dynamic motion responses were collected from 2012 to the present. In this study, the dynamic motions of the platform structure were analyzed using an artificial neural network (ANN) and data obtained during a typhoon. Numerical modeling was performed to analyze the platform parameters using a radial basis function (RBF), and hydrodynamic modeling was conducted using ansys-aqwa. Five geometric parameters related to the platform design were selected for optimization and included the mass, moments of inertia of the three rotation degrees, and the position of the center of gravity (COG). The mean values of the surge and pitch and the standard deviations of the roll and pitch were used as the input parameters. The model validations showed that the proposed ANN-based method performed well for obtaining the optimal platform parameters. The maximum errors of the roll, pitch, surge, and sway motions were within 5%. The updated response amplitude operators (RAOs) and new design indices for a 100-year return period of a typhoon were determined to guide operations and evaluate platform designs.

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