Infrastructure state transition probability computation using duration models

Sound infrastructure deterioration models are essential for accurately predicting future conditions which, in turn, are key inputs to effective maintenance and rehabilitation decision-making. The challenge central to developing accurate deterioration models is that condition is often measured on a discrete scale, such as inspectors' ratings. Furthermore, deterioration is a stochastic process that varies widely with several factors, many of which are generally not captured by available data. Therefore, probabilistic discrete state models are often used to characterize deterioration. Such models are based on transition probabilities which capture the nature of the evolution of condition states from one time point to the next. However, current methods for determining such probabilities suffer from several serious limitations. An alternative approach addressing these limitations is presented in this paper. A probabilistic model of the time spent in a state is derived and the approach used for estimating its parameters is described. Furthermore, a methodology for determining the corresponding state transition probabilities from the developed duration model is presented. Finally, the overall methodology is demonstrated using a data set of reinforced concrete bridge deck observations.