Field Aging Characterization of Asphalt Pavement Based on the Artificial Neural Networks and Gray Relational Analysis

Abstract

Accurate characterization of field aging of asphalt pavement is critical to precisely assessing its in-service performance. However, most of the traditional test/predictive methods either cannot fully capture the field aging characteristics or involve costly testing/computational efforts to ensure satisfactory prediction accuracy. To alleviate these problems, this study developed a new field aging predictive model based on artificial neural networks (ANNs) and gray relational analysis (GRA), which takes the field-aged viscosity of asphalt binder as the target predictive property. A series of influencing factors that may affect the field-aged viscosity were systematically investigated, among which the eight most significant ones were screened out for the ANN modeling through the GRA. A total of 479 data extracted from long-term pavement performance (LTPP) database were used for the training, validation, and testing of the ANN model. The calculation results showed that the predictive model developed using the ANN approach provided a high prediction accuracy with R2 value greater than 0.90. Furthermore, the falling-weight deflectometer (FWD) data collected from the database were utilized to evaluate the predictive performance of the well-trained ANN model. Consistent results were obtained between the viscosity values predicted from the ANN model and those back-calculated from the FWD data, indicating that the newly developed field aging model has the capability to accurately characterize the field aging evolution of asphalt pavement.