Development of a semi-parametric stochastic model of asphalt pavement crack initiation

Predicting pavement performance under the combined action of traffic and the environment provides valuable information to a highway agency. The prediction of the time at which the pavement starts to crack is an essential component of pavement management. A common problem in modeling crack initiation is censoring, caused by unobserved initiation times in a typical data set. Data collection surveys are usually of limited length. Thus, some pavement sections will have already cracked by the day the survey starts; others will crack during the survey period, while others will only do so after the survey is concluded. If the œnsoring of the crack initiation times is not accounted for properly, the model may suffer from statistical biases. In this paper, an analysis of the pavement cracking data collected during the AASHO Road Test is presented. The analysis is based on the use of semi-parametric stochastic duration modeling techniques. Duration models enable the stochastic nature of pavement crack initiation to be represented as well as censored data to be incorporated in the statistical estimation of the model parameters. The results show that the crack initiation model provides very good fit to the data, and that the parameter estimates obtained have the correct signs and relative magnitudes.