Predicting asphalt pavement crack initiation following rehabilitation treatments

Abstract

Abstract Prolongation of the service life of pavements requires efficient prediction of the performance of their structural condition and particularly the occurrence and propagation of cracking of the asphalt layer. Although pavement performance prediction has been extensively investigated in the past, models for predicting the cracking probability and for quantifying impacts of associated explanatory factors following pavement treatment, have not been adequately investigated in the past. In this paper the probability of alligator crack initiation following pavement treatments is modeled with the use of genetically optimized Neural Networks, The proposed methodological approach represents the actual (observed) relationships between of probability of crack initiation and the various design, traffic and weather factors as well as the different rehabilitation strategies. Data from the Long Term Pavement Performance (LTPP) Data Base and the Specific Pavement Study 5 (SPS-5) are used for model development. Results indicate that the proposed approach results in accurately predicting the probability of crack initiation following treatment; furthermore it provided information on the relationship between external factors and cracking probability that can help pavement managers in developing appropriate rehabilitation strategies.