Assessing life-cycle seismic safety of aging reinforced concrete bridges is a challenging engineering task. Deterioration phenomena reduce structural capacity, exacerbating poor design choices that are typical of old bridges, while also being characterized by major uncertainties. Management of engineering systems in highly uncertain environments can be efficiently addressed through Markov decision processes, which rely on dynamic Bayesian networks to model the deteriorating system’s life-cycle. However, there is still a gap in developing virtual environments that can seamlessly fit in such advanced algorithmic decision-making frameworks, especially under life-cycle seismic behavior considerations. In this study, we develop a dynamic Bayesian network capable of incorporating disparate uncertainties related to chloride-induced corrosion and seismic action, aiming at providing fragility curves over the bridge service life. The framework is applied to a prototype bridge encapsulating key risk-prone features. Using a multi-component approach, the developed network provides valuable insights into the fragility evaluation of both the system and individual components. Markovian transitions among component deterioration states are computed by combining corrosion initiation and propagation models with non-stationary Gamma processes. Subsequently, state-dependent fragilities are obtained through probabilistic seismic assessment based on non-linear dynamic analyses and multinomial logistic regression. Results show that the approach sheds light on the risk interplay mechanisms between components and the system, and on how different corrosion scenarios affect the system fragility. Discussion is finally provided on how these risk considerations can be interpreted for decision-making, allowing for better repair and retrofit strategies.@en

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.@en