Large-scale evaluation of pavement performance models utilizing automated pavement condition survey data

Abstract

Pavement performance models are an essential component of a pavement management system (PMS) and have a direct impact on future pavement condition prediction, selection of pavement maintenance and rehabilitation (M&R) methods, and budget planning and allocations. Therefore, it is critical to develop and maintain pavement performance models as accurate as possible. The California Department of Transportation (Caltrans) has implemented a modern pavement management system called PaveM. To maintain the intended functions of PaveM, regular updates of its databases and key components are necessary, including pavement performance models. This paper aims to evaluate the network-level pavement performance models in PaveM by utilizing the concept of deterioration rate and the most recent automated pavement condition survey (APCS) data. First, the concept of pavement deterioration rate was defined. The actual deterioration rates were calculated using two latest cycles of APCS data and then compared to the predicted deterioration rates obtained using APCS data and the current configurations of PaveM. Two typical pavement distresses (International Roughness Index and Caltrans’ asphalt pavement alligator B cracking) for one selected pavement treatment (thin overlay) were predicted using PaveM and compared to the actual APCS measurements. The results from this study show that the concept of deterioration rate was effective in evaluating the overall quality of performance models in PaveM as well as identifying the time spans in which the models may over- or under-predict pavement performance.