Continuous-State Survival Functions for Reinforced Concrete Bridges Based on Physics-Based Degradation Models and Visual Inspection

This study presents an approach to evaluate the Continuous-State Survival Functions (CSSF) of structural systems, considering the degradation of individual components and their arrangement within the system.System reliability is quantified using the Diagonally Approximated Signature (DAS), a framework that separates the system's topological configuration from the probabilistic behavior of its components, enabling efficient reliability computation.Although traditional survival signature methods assume binary states, this work extends the concept to accommodate continuously degrading components.Component reliability is evaluated through a physics-based degradation model, integrated with results from visual inspections of the structure.The proposed approach is demonstrated on a reinforced concrete girder bridge structure affected by corrosion.The system components -namely the bridge girders -are characterized by different deterioration processes.The DAS acts as a surrogate modeling approach and provides an efficient alternative for costly Monte Carlo simulation.The proposed procedure includes deriving CSSFs for structural elements and then propagating these through the DAS to quantify the CSSFs of the bridge.Thus, this paper constitutes a further stepping stone for stochastically simulating large-scale systems for infrastructure network reliability analyses under various degradation dynamics.