Pavement deterioration models are important inputs for the pavement management systems (PMS). These models are based on the study of performance data. These models are based on the study of performance data, and they provide the evolution law of pavement deterioration. Performance data consist of observations of the condition of pavement sections, and are collected through several follow-up campaigns on road networks. In order to characterize the pavement deterioration process, several statistical methods have been developed at the LCPC (French Public Works Research Laboratory). However, these methods are suboptimal for modelling the evolution of pavement deterioration, as they ignore unitspecific random effects and potential correlation among repeated measurements. This paper presents a nonlinear mixed-effects model allowing to account for the correlation between observations on the same pavement section. Based on this nonlinear mixed-effects modelling, we investigate and identify structural and climatic factors that explain differences in the parameters between pavement sections, and quantify the impact of these factors on pavement evolution. The proposed model provides a good fit for describing the evolution law of different pavement sections. The performance of this model is assessed via simulated and real data.
