Alireza Ghavidel

This study develops a novel artificial intelligence (AI)-based methodology to enable risk-based, long-term-focused decision support for bridge asset management. This methodology features three key modules: (1) a bridge condition simulation module integrating Markovian state transition dynamics, (2) a lifecycle cost analysis module to holistically quantify and aggregate the direct and indirect costs incurred due to bridge condition deterioration and intervention actions over a prolonged planning horizon, and (3) an AI-based decision-support module for optimal sequential maintenance actions at individual bridge level. The proposed methodology can flexibly accommodate the decision needs of different individual bridges within a bridge portfolio under various sources of uncertainty in near real time. Compared with traditional reactive, condition-based decision policies, the proposed AI-based policies are found to offer more proactive and adaptive decisions and to demonstrate superior performance in minimizing the life cycle costs. To further enhance risk-averse stakeholders’ trustworthiness in the AI-based decision policy, practical action constraints and decision reasoning are introduced, and the effect of action override is also investigated.

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.