Artificial neural network approach to estimating stiffness behavior of rubberized asphalt concrete containing reclaimed asphalt pavement

作者：Xiao Feipeng^*; Amirkhanian Serji N

来源：Journal of Transportation Engineering-ASCE, 2009, 135(8): 580-589.

DOI：10.1061/(ASCE)TE.1943-5436.0000014

• Artificial Neural Network
• Artificial neural network approach
• Asphalt mixture
• Complex behavior
• Dependent variables
• Environmental conditions
• Fatigue life
• Independent variables
• Input variables
• Material engineering
• Pavement structures
• Prediction model
• Reclaimed asphalt pavements
• Rubber content
• Rubberized asphalt
• Stiffness values
• Testing temperature

Accurately predicting the stiffness of asphalt pavements is difficult due to the complex behavior of materials under various loading, pavement structure, and environmental conditions. This study explores the utilization of the artificial neural network (ANN) in predicting the stiffness behavior of rubberized asphalt concrete mixtures with reclaimed asphalt pavement (RAP). A total of 296 asphalt mixture beams were constructed from two different rubber types (ambient and cryogenic), two different RAP sources, and four rubber contents (0, 5, 10, and 15%). All samples were tested at two different testing temperatures of 5 and 20°C. The regression statistical method was used to predict the stiffness behavior of these mixtures via the 7 input variables covering the material engineering properties of the asphalt beams. In addition, the data were organized into 5 independent variables and one dependent variable (the stiffness values of the modified mixture beams) in ANN models. Results showed the ANN techniques to be more effective in predicting the fatigue life of the modified mixture than traditional regression-based prediction models.

• 出版日期2009