This study introduces a novel multivariate risk assessment method for cargo ship dynamics, developed by Oleg Gaidai and colleagues. The method is designed to efficiently assess risks in complex marine systems, particularly for large cargo ships. The research focuses on analyzing the dynamic behavior of cargo vessels, including structural stresses and accelerations, under various environmental conditions. The method is applied to a 4400 TEU Panamax container vessel, which has been equipped with sensors to monitor hull stresses and accelerations during trans-Atlantic voyages. The study highlights the importance of accurately predicting risks associated with cargo vessel operations, especially in extreme weather conditions. The Gaidai multivariate risk assessment method is compared with traditional bivariate Weibull methods, demonstrating its effectiveness in handling high-dimensional, nonlinear, and cross-correlated dynamic systems. The method is validated using synthetic and real-world data, showing improved accuracy in predicting extreme events such as hull girder overloading and structural failures. The study also discusses the application of the method to various marine and offshore engineering systems, emphasizing its potential for enhancing safety and reliability in maritime operations. Key findings include the method's ability to accurately assess risks in complex systems with limited data, making it a valuable tool for naval design and marine engineering. The study underscores the need for further research into stochastic structural models for wind-wave environments and the development of reliable algorithms for risk assessment in dynamic systems. Overall, the Gaidai multivariate risk assessment method offers a robust framework for evaluating and mitigating risks in cargo ship operations, contributing to safer and more efficient maritime transport.This study introduces a novel multivariate risk assessment method for cargo ship dynamics, developed by Oleg Gaidai and colleagues. The method is designed to efficiently assess risks in complex marine systems, particularly for large cargo ships. The research focuses on analyzing the dynamic behavior of cargo vessels, including structural stresses and accelerations, under various environmental conditions. The method is applied to a 4400 TEU Panamax container vessel, which has been equipped with sensors to monitor hull stresses and accelerations during trans-Atlantic voyages. The study highlights the importance of accurately predicting risks associated with cargo vessel operations, especially in extreme weather conditions. The Gaidai multivariate risk assessment method is compared with traditional bivariate Weibull methods, demonstrating its effectiveness in handling high-dimensional, nonlinear, and cross-correlated dynamic systems. The method is validated using synthetic and real-world data, showing improved accuracy in predicting extreme events such as hull girder overloading and structural failures. The study also discusses the application of the method to various marine and offshore engineering systems, emphasizing its potential for enhancing safety and reliability in maritime operations. Key findings include the method's ability to accurately assess risks in complex systems with limited data, making it a valuable tool for naval design and marine engineering. The study underscores the need for further research into stochastic structural models for wind-wave environments and the development of reliable algorithms for risk assessment in dynamic systems. Overall, the Gaidai multivariate risk assessment method offers a robust framework for evaluating and mitigating risks in cargo ship operations, contributing to safer and more efficient maritime transport.