University of California Transportation Center

Predicting pavement performance under the combined action of traffic and the environment provides valuable information to a highway agency. The estimation of the time at which the pavement conditions will fall below an acceptable level (failure) is essential to program maintenance and rehabilitation works and for budgetary purposes. However, the failure time of a pavement is a highly variable event; terminal conditions will be reached at different times at various locations along a homogeneous pavement section. A common problem in modeling event duration is caused by unobserved failure events in a typical data set. Data collection surveys are usually of limited length. Thus, some pavement sections will have already failed by the day the survey starts, others will reach terminal conditions during the survey period, while others will only fail after the survey is concluded. If only the failure events observed during the survey are included in the statistical analysis (disregarding the information on the after and before events), the model developed will suffer from truncation bias. If the censoring of the failure events is not accounted for properly, the model may suffer from censoring bias.

In this paper, an analysis of the data collected during the AASHO Road Test is presented. The analysis is based on the use of probabilistic duration modeling techniques. Duration models enable the stochastic nature of pavement failure time to be evaluated as well as censored data to be incorporated in the statistical estimation of the model parameters. The results, based on sound statistical principles, show that the failure times predicted with the model match the observed pavement failure data better than the original AASHO equation.