Data-driven optimization of repair schemes and inspection intervals for highway bridges

Abstract

Routine bridge inspections enable timely maintenance plans based on potential risks. These inspections are performed at fixed time intervals, which may be unnecessarily short for bridges in excellent conditions and precariously long for those in poor conditions. Also, deterioration models used in current bridge management approaches fail to include the effect of age, causing inaccurate predictions. This paper develops a hazard-based bridge management approach where inspection frequencies and repair suggestions are optimized based on hazard levels. A two-dimensional Markov model describes the age-dependent bridge deterioration, and a semi-Markov decision process optimizes inspection intervals and repair decisions. The objective is to minimize the expected long-term total annual costs, including expenses associated with maintenance and traffic detours. The modeling process and maintenance management framework was demonstrated on an example bridge superstructure in New York. The results showed lower annual and total costs than the conventional management method. As expected, sensitivity analyses indicated that the pre-specified choices of inspection intervals for low and high hazard levels have a significant effect on the calculated hazard threshold levels. The proposed maintenance management method is adaptable and practical; bridge authorities can identify appropriate repair actions and make inspection decisions based on simple threshold comparisons.