Investigating the Seepage Characteristics of an Open-Graded Friction Course Using Finite Element Modeling

Abstract

An open-graded friction course (OGFC) is an asphalt mixture designed with a large air void (AV) content that provides enhanced drainage capability at the surface. The main objectives of this study were to investigate the impacts of selected factors (i.e., the OGFC thickness and coefficient of permeability, the permeability of the underlying layer, and traffic loading) on the drainage characteristics of the OGFC, develop a quantitative tool to simulate the deterioration in the functional performance of the OGFC, and propose new guidelines for the AV content of OGFC for optimum functionality. To this end, a three-dimensional finite element (FE) model was developed to evaluate the impacts of OGFC permeability, OGFC layer thickness, underlying layer permeability, rain intensity, and traffic volume on the seepage characteristics of OGFC pavements. The impacts of these factors were evaluated by calculating the time at which the OGFC surface reaches overflow condition ( TC). Statistical analysis of the results showed that all considered factors had a significant impact on OGFC seepage characteristics, except OGFC permeability. In addition, an artificial neural network (ANN) model was developed to predict TC without the need for FE modeling. Results indicated that the ANN model predicted TC accurately with R2 values of 0.99 and 0.98 in the training and validation stages, respectively. The results also indicated that the model accurately predicted TC over time for OGFC pavements with a root-mean-square error of less than 5.0%. Simulation runs were conducted using the developed FE model under different OGFC AV content conditions and rain intensities. Results revealed that an OGFC layer with an AV content of 16% would provide adequate drainage performance while minimizing OGFC durability issues.