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.

Structural systems must satisfy multiple performance and functionality requirements during their life cycle, withstanding safety-reducing degradation mechanisms and hazards. Intervention strategies must be planned accordingly to maintain structural integrity and minimize total life-cycle costs and risks, posing a complex optimization problem. Recent advances in multi-agent deep reinforcement learning (DRL) in conjunction with partially observable Markov Decision Processes (POMDPs) have shown great potential for determining optimal structural integrity management policies for systems with large state and action spaces compared to traditional decision practices. This paper tackles the maintenance optimization problem of aging bridges in seismic-prone areas, creating an updatable environment that embeds chloride-induced corrosion and state-dependent seismic fragility throughout the bridge life-cycle. The evolution of the environment is captured by a dynamic Bayesian network, and it is further integrated with decentralized multi-agent DRL algorithms to identify near-optimal lifecycle decisions under risk constraints. Results on a multi-component bridge system show the suitability of the developed framework for minimizing expected life-cycle costs, and for providing detailed and adaptive policies that significantly outperform traditional condition- and time-based maintenance plans.