Multi-objective optimization model for automated risk-based inspection planning for concrete bridges

This paper addresses the critical challenge of optimizing inspection planning for reinforced concrete bridges, considering budgetary and operational constraints. The developments presented here focus on bridge decks. This entails identifying which bridges require inspections, the optimal timing for these inspections, and the most effective non-destructive evaluation inspection methods to employ. A multi-objective optimization model is developed, leveraging the non-dominated sorting genetic algorithm-II and probabilistic modeling. The developed model strikes a balance between minimizing the structure risk of failure, maximizing inspection effectiveness, and optimizing direct costs and impact costs of inspections. The developed model is expected to provide transportation agencies and infrastructure managers with a robust decision-support tool for automated, efficient inspection planning for this class of bridges, that increases inspection effectiveness and enables condition- and risk-driven utilization of advanced non-destructive evaluation methods. The developments here lay the groundwork for integrating inspection outcomes from these methods in selecting subsequent intervention strategies.