Parameterized Logistic Models for Bridge Inspection and Maintenance Scheduling

Abstract

Proper inspection and maintenance schedules are integral to bridge functionality and safety; however, they also pose challenges in light of budget and resource limitations. As such, bridge management systems (BMSs) are always concerned with finding the best deterioration and maintenance models to optimize scheduling. The current work proposes parameterized logistic models that can capture bridge deterioration and the effect of maintenance interventions. Given a handful of easy-to-track bridge parameters, such as age, time since last major maintenance, and location, the proposed models predict the probability of a bridge (or group of bridges) to need repair throughout its service life. Combined with the appropriate probability threshold, obtained from life-cycle cost analysis, this allows for the optimization of inspection frequency and helps in maintenance planning. The results indicate that the proposed models predict the bridge condition more accurately compared to the Markov Chains models adopted by many North American BMSs. Finally, the application of the parameterized logistic models is demonstrated through a case study.