The article introduces the Gaidai multivariate risk assessment method, a novel approach designed to efficiently analyze the structural dynamics of cargo ships, particularly in high-dimensional and cross-correlated systems. The method is benchmarked against a well-established bivariate risk assessment method using synthetic and real-world data from a 4400 TEU Panamax container vessel. The study highlights the method's ability to handle complex, non-stationary, and nonlinear dynamic systems, making it suitable for a wide range of marine and offshore engineering applications. The Gaidai method treats the ship's dynamic system as a black box, considering a large number of covariates and merging critical components into a single synthetic vector. The validation process involves comparing the method's predictions with those of the 4-parameter bivariate Weibull method, demonstrating that the Gaidai method can provide more accurate risk assessments, especially for real-world, non-Gaussian, and intercorrelated datasets. The article also discusses the method's potential for broader engineering domains and future research directions, emphasizing its resilience, ease of use, and general applicability.The article introduces the Gaidai multivariate risk assessment method, a novel approach designed to efficiently analyze the structural dynamics of cargo ships, particularly in high-dimensional and cross-correlated systems. The method is benchmarked against a well-established bivariate risk assessment method using synthetic and real-world data from a 4400 TEU Panamax container vessel. The study highlights the method's ability to handle complex, non-stationary, and nonlinear dynamic systems, making it suitable for a wide range of marine and offshore engineering applications. The Gaidai method treats the ship's dynamic system as a black box, considering a large number of covariates and merging critical components into a single synthetic vector. The validation process involves comparing the method's predictions with those of the 4-parameter bivariate Weibull method, demonstrating that the Gaidai method can provide more accurate risk assessments, especially for real-world, non-Gaussian, and intercorrelated datasets. The article also discusses the method's potential for broader engineering domains and future research directions, emphasizing its resilience, ease of use, and general applicability.