Machine Learning-Based Resilience Modeling and Assessment of High Consequence Systems Under Uncertainty

Abstract

This study proposes a theoretical model and assessment method for the resilience of High Consequence System (HCS), addressing the risk assessment and decision-making needs in critical system engineering activities. By analyzing various resilience theories in different domains and considering the characteristics of risk decision-making for HCS, a comprehensive theoretical model for the resilience of HCS is developed. This model considers the operational capability under normal environment (consisting of reliability and maintainability) and the safety capability under abnormal environment (consisting of resistance and emergence response ability). A case study is conducted on a spent fuel transportation packaging system, where the sealing performance after sealing ring aging is regarded as the reliability of the system and calculated using reliability methods, and impact resistance after impact is regard as resistance the impact safety of the packaging system is assessed using finite element analysis and surrogate modelling methods. The surrogate model fits the deformation output results of finite elements. Maintainability and emergency response ability are also essential elements of the resilience model for HCS facing exceptional events. The resilience variation of the spent fuel transportation packaging system is computed under the uncertainty of yielding stress of buffer material. The resilience of the packaging system is evaluated for different buffer thicknesses. The system's resilience decreases with higher uncertainty in the yielding stress of the buffer material, while it increases with thicker buffer materials. The improvement of emergency rescue ability will also lead to the improvement of system resilience.