A Mechanistic Design Approach for Novel Graphene Nanoplatelet (GNP) Reinforced Asphalt Pavements for Low Temperature Applications

Principal Investigator(s):

Jialiang Le, Professor, Civil, Environmental and Geo-Engineering


Project summary:

This report explores the application of a discrete computational model for predicting the fracture behavior of asphalt mixtures at low temperatures based on the results of simple laboratory experiments. In this discrete element model, coarse aggregates are explicitly represented by spheres, and these spheres are connected by bonds representing the fine aggregate mixture, a.k.a. FAM, (i.e., asphalt binder with the fine-size aggregates). A literature review examines various methods of computational modeling of asphalt materials as well as the application of nanomaterials to asphalt materials. Bending beam rheometer (BBR) tests are performed to obtain the mechanical properties of the fine aggregate mixture (FAM) at low temperatures. The computational model is then used to simulate the semi-circular bend (SCB) tests of the mixtures. This study considers both the conventional asphalt materials and graphite nanoplatelet (GNP)-reinforced asphalt materials. The comparison between the simulated and experimental results on SCB tests shows that by employing a softening constitutive model of the FAM, the discrete element model is capable of predicting the entire load-deflection curve of the SCB specimens. Based on the dimensional analysis, a parametric study is performed to understand the influence of properties of FAM on the predicted behavior of SCB specimens.

Project details:

  • Project number: 2016014
  • Start date: 05/2015
  • Project status: Completed
  • Research area: Infrastructure