Bridge asset managers are tasked with developing effective maintenance strategies by the stakeholders of transportation networks. Any presentation of maintenance strategies requires an estimate of the consequence on the Whole Life Cycle Cost (WLCC), which is contingent on an accurate deterioration model. Bridge deterioration has previously been demonstrated to exhibit non-constant behaviour in literature. However, commonly industrial data constrains deterioration models to use exponential distributions. In this study, a Dynamic Bayesian Network (DBN) is proposed to model bridge deterioration, which considers the initiation of different defect mechanisms and the interactions between the mechanisms. The model is parameterised using an exponential distribution, however through the consideration of defect interactions, non-constant deterioration behaviour can still be incorporated in the model. The deterioration of pointing, displacement of block work alongside the presence of spalling, hollowness and masonry cracking are the defect mechanisms considered, with masonry railway bridges in the United Kingdom serving as a case study
