Bayesian updating method for quay mooring system simulation model

ObjectivesEstablishing a robust quay mooring simulation model for ships is of great significance for ensuring the safety of ship quay mooring operations.Methods This paper proposes an updating method for a quay mooring simulation model based on Bayesian updating and Bayesian Model Averaging (BMA). By integrating measured data under multiple operating conditions, this method updates and constructs a robust high-precision quay mooring simulation model. First, an initial quay mooring simulation model is constructed. Subsequently, simulation surrogate models for parameters-response characteristics of the quay mooring simulation model are developed under multiple operating conditions. Then, under each operating condition, response characteristics are extracted from measured data, and Bayesian updating is performed using the Markov Chain Monte Carlo (MCMC) method to obtain the posterior distributions of the simulation model parameters under each condition. Finally, based on the BMA method, the parameter updating results under all operating conditions are integrated to construct a high-precision quay mooring simulation model.Results Taking the quay mooring of a specific ship as an example, the proposed method is validated. The updated simulation model achieves prediction errors for hull motion strength of no more than 5% under multiple typical operating conditions. Conclusions This example validates the effectiveness of the proposed method and provides guidance for the construction of high-precision quay mooring simulation models.