Background: Due to the complexity of ocean environmental loading models, together with the nonlinearity and empirical parameters involved in hydrodynamic numerical modeling and model testing, many uncertainties and challenges still exist in the design and operation of platforms built to float at sea. On-site prototype measurements carried out on actual floating structures provide a valid strategy for obtaining accurate environmental loading parameters and floater motion responses.

Problem definition: A prototype monitoring system has been built as part of a joint industrial project in the South China Sea. A complete set of long-term ocean environment loading parameters and structural dynamic motion responses has been gathered for the period from 2012 to the present. Several advanced techniques, such as the independent remote monitoring technique (IRMT), the integrated continuous measurement technique and the standalone underwater measurement technique were established to enhance the reliability of data collection even during extreme typhoon conditions when there was no power.

Solution approach and findings: this paper analyzes the dynamic motion characteristics of the platform structure based on the monitoring data. The relationship between the measured wave spectrum and the JONSWAP spectrum is discussed. The spectral shape parameters of the JONSWAP spectrum for the South China Sea, as derived from the monitoring data, are discussed. The dynamic motions of the platform structure are analyzed based on artificial neural networks (ANN) using data from a typical monitored typhoon. The numerical modeling used in this research is constructed to perform the identification analysis of the platform parameters using radius basic function (RBF) and hydrodynamic results produced by ANSYS-AQWA. This research selects five main geometric parameters related to the platform design. Mass, moments of inertia of three rotation degrees, and the position of the center of gravity (COG) are selected as the optimization objectives. The mean values of surge and pitch and standard deviations of roll and pitch are treated as the input parameters. Modeling verifications show that the present ANN-based method performs well in obtaining the optimal platform parameters. The maximum error between the simulated and monitored results in terms of the measurement of the roll, pitch, surge and sway motions fall within 5%. The model of the monitored platform could be further updated; it could be made capable of performing the performance assessments of the dynamic characteristics in extreme and/or harsh environmental conditions.

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