Evolving reliability assessment of systems using active learning-based surrogate modelling

Risk tracking involves monitoring and assessing evolving event risks in complex infrastructure systems. As these systems change over time, so do the associated risk profiles, requiring reliability updating principles. Traditional methods rely on repetitive simulations of complex models, while recent surrogate-based techniques offer efficiency and accuracy without relying on observed data. However, these approaches inadequately address timedependent reliability updating and surrogate modeling integration. To overcome these limitations, this study presents the Time-Variant Reliability Updating method (TVRU), which seamlessly integrates Kriging-based timedependent reliability with parallel learning. TVRU estimates prior failure probability by leveraging the adaptive training capabilities of a time-variant surrogate Kriging model. This model incorporates random variables and time as inputs, using the performance function's responses as outputs. By classifying design samples, the prior failure probability is accurately computed, and a meticulous Kriging model estimates the posterior probability of failure. The TVRU method is demonstrated using four numeric models of varying complexities, showcasing exceptional efficiency and accuracy. This novel approach enhances risk assessment in complex systems, facilitating informed decision-making.