Assessing life-cycle seismic fragility of corroding reinforced concrete bridges through dynamic Bayesian networks

Bridge structures are exposed to several chronic and abrupt stressors, among which the combined effects of corrosion and earthquakes pose a major threat to their long-term safety. Probabilistic risk assessment frameworks that quantify and propagate uncertainties inherent to these phenomena are necessary to mitigate this threat. This paper proposes a dynamic Bayesian network for state-dependent seismic fragility functions, capturing corrosion and seismic effects over time. Markovian transitions among deterioration states for different bridge components are developed, combining chloride diffusion and corrosion propagation models with non-stationary Gamma processes. State-dependent fragility curves are derived based on non-linear dynamic time-history analyses given possible degradation configurations of the structure, accounting for uncertainties in material, geometry, and deterioration parameters. Record-to-record variability is captured using synthetic ground motions. Results on a 4-span Gerber bridge showcase the suitability of the framework for describing life-cycle fragility, and its capacity for embedding in advanced algorithmic decision-making workflows is discussed.