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Bridge inspection standards in the United States require routine visual inspections to be conducted on most bridges at a maximum interval of two years regardless of the bridge condition. Limitations of this uniform calendar-based approach have been reported in the literature. Accordingly, the objective of this study is to provide a new systematic approach for inspection planning that integrates information from bridge condition prediction models, inspection data, and expert opinion using Bayesian analysis to enhance inspection efficiency and maintenance activities. The uncertainty-based inspection framework proposed in this study can help bridge owners avoid unnecessary or delayed inspections and repair actions, determine the inspection method, and consider more than one deterioration process or bridge component during the inspection planning process. The inspection time and method are determined based on the uncertainty and risks associated with the bridge condition. As uncertainty in the bridge condition reaches a defined threshold, an inspection is scheduled utilizing nondestructive techniques to reduce the uncertainty level. The framework is demonstrated on a new and on an existing reinforced concrete bridge deck impacted by corrosion deterioration. The results show that the framework can reduce the number of inspections by 50% compared to conventional scheduling methods, and the uncertainty regarding the bridge maintenance time is reduced by 16%.

A Comprehensive Uncertainty-Based Framework for Inspection Planning of Highway Bridges