عنوان مقاله [English]
Pavement deterioration models are the most important components of any Pavement Management System (PMS). These models could predict pavement condition at any time in its service life. Pavement deterioration is a very complicated and uncertain process. Probabilistic deterioration models in comparison with deterministic ones could take into account these uncertainties. One of the most important probabilistic pavement deterioration models, is the trend curve model that is based on pavement roughness. In this research, roughness data of GPS-1 and GPS-2 pavement sections, which are in-service asphalt pavements respectively with granular base and stabilized base layers, have been extracted from Long-Term Pavement Performance (LTPP) database. These data then were used for analyzing pavement deterioration uncertainties. For this purpose, Chi-square (χ2) and Kolmogorov-Smirnov (K-S) statistical tests were used to determine probability distribution of pavement future condition over its current condition ratio in different years. Results showed that lognormal distribution is more fitted with actual data in long-term pavement life. Having this distribution, pavement deterioration model was developed based on roughness index using trend curve model. Utilizing proposed model, the pavement management system could predict pavement future condition taking into account uncertainties of deterioration process and with optimal budget assignment, could maintain the network condition at a specified risk level. This could prevent of any future risk regarding the pavement deterioration uncertainties.