Bridge Maintenance Management Based on Routine Inspection Data: A Quantitative Approach

Abstract

Mainstream bridge management systems use Markov chains of bridge condition ratings (CRs) to simulate temporal bridge health degradations and apply Markov decision processes to find optimal repair schemes. However, CR-based Markov models fail to consider the effect of structural age. Ongoing research aims to develop alternative models that incorporate the time factor, including multidimensional Markov models. In this study, bridge health degradation is described using a three-dimensional (3D) Markov model, and a corresponding condition-based bridge maintenance management framework is proposed. The proportional hazard model with a Weibull baseline hazard function translates the state of the 3D Markov model into a single hazard indicator for decision making. To demonstrate the proposed framework, concrete bridge decks were taken as the element of interest. The proposed management framework can provide time-variant decision thresholds, which outperform the CR-based Markov models in terms of information richness and total life-cycle costs. This paper also briefly discusses how to apply the proposed method to element-level inspection data.