Reliability Analysis and Neural-network Modeling of Ship Radar Mast Structure by Adopting Quasi-random Sampling

Outfitting structures, such as ship radar masts, require a design that considers the vibration and structural strength performance to ensure safe navigation. Nevertheless, reliability analysis considering design uncertainties is required because of the lack of classification rules for such structure design. This study examined the quasi-random sampling process suitable for the design reliability evaluation of radar masts. Neural network modeling was performed for the approximation using quasi-random sampling data regarding the vibration and structural strength performance of radar masts. For statistical reliability analysis, the Sobol sequence method was applied to the sampling process, and the reliability probability with the variation of the sampling number was evaluated. The approximate accuracy of neural network modeling was analyzed according to the variation of sampling number. In the context of research results, setting the quasi-random sampling number to approximately 0.4% of the Monte Carlo Simulation (MCS) sampling number allowed for a practical design reliability evaluation. A sampling number of approximately 0.2% of the MCS sampling number was found to be sufficient to approximate. Through this study, a quasi-random sampling process suitable for statistical reliability analysis of radar mast structures was determined, and a neural network model with high approximate accuracy was generated.