Probabilistic infrastructure deterioration models with panel data

Statistical models of infrastructure facility deterioration are typically estimated using panel data sets of in-service facilities. For example, biannual ratings of bridges have been used to develop discrete models of component deterioration by a number of researchers. Unfortunately, these models have not accounted for the presence of heterogeneity in the panel data, which may lead to biased coefficient estimates. Furthermore, researchers have usually imposed a Markovian specification in the development of such models, implying that the probabilistic deterioration in a given period is independent of history. This assumption may be unrealistic for some types of facilities in which early stress initiation leads to accelerated deterioration in later stages of their lives. In this paper, we adopt a random-effects specification to control for heterogeneity in a probit model of bridge-deck deterioration and extend the model to investigate the presence of state dependence. The proposed model yields improved results in comparison with a simple probit model and provides evidence that is inconsistent with the Markovian assumption in bridge-deck deterioration. An implication of this study is that both heterogeneity and state dependence may need to be accounted for in developing probabilistic infrastructure deterioration models.