Holistic Benefit–Cost Analysis of Bridge Seismic Retrofitting Coupled with Artificial Intelligence-Based Decision Policy

Highway bridges are fundamental components of transportation networks. Although chronic deterioration of bridge conditions has been the primary focus of bridge asset management, the implications of seismic hazards, as well as their interaction with bridge maintenance and seismic retrofitting strategies has yet to be fully studied. This study aims to quantitatively assess the long-term performance and cost effectiveness of various seismic retrofitting options when coupled with artificial intelligence (AI)–based maintenance decision policies. To achieve this, bridge component deterioration is modeled using Markov processes. To account for seismic hazard threats, bridge system-level seismic fragility and risk quantification modules are introduced to estimate seismic damage probabilities. The analysis also includes a comprehensive evaluation of both direct and indirect costs associated with maintenance and retrofitting actions for life-cycle cost estimations. Next, the developed bridge performance and life-cycle cost models are coupled with a powerful AI approach known as deep reinforcement learning (DRL) to provide a comprehensive bridge management policy. Finally, the study presents an AI-based maintenance policy integrated with seismic retrofitting strategies and compared with condition-based maintenance policy. The findings underscore the benefits of a holistic AI-driven decision-making process that can manage both routine maintenance and seismic repair actions. Moreover, the evaluation of seismic retrofitting within an AI-driven maintenance framework highlights the long-term benefits of such integration, especially for high seismicity regions.