Estimation of infrastructure distress initiation and progression models

Infrastructure distress models predict the initiation and progression of distress on a facility over time as a function of age, design characteristics, environmental factors, and so on. Examples of facility distress included cracking, potholing, and rutting. Facility condition survey data sets typically include a large number of structural zeros indicating absence of distress at the time of observation. Most distress progression models in the literature are simple regression models that are estimated using the sample of observations for which distress has been initiated. These models are statistically erroneous because they suffer from selectivity bias due to the nonrandom nature of the estimation sample used. In this paper, we apply two econometric methods to estimate joint discrete-continuous models of infrastructure distress initiation and progression while correcting for selectivity bias. These methods are Heckman’s procedure and the full information maximum likelihood method. An empirical case study demonstrates these methods for the case of highway-pavement-cracking models. It is shown that selectivity bias can be a very serious problem in such models.