  Rutting continues to be one of the principal distresses in asphalt pavements
worldwide. This type of distress is caused by permanent deformation and shear
failure of the asphalt mix under the repetition of heavy loads. The Hamburg
wheel tracking test (HWTT) is a widely used testing procedure designed to
accelerate, and to simulate the rutting phenomena in the laboratory. Rut depth,
as one of the outputs of the HWTT, is dependent on a number of parameters
related to mix design and testing conditions. This study introduces a new model
for predicting the rutting depth of asphalt mixtures using a deep learning
technique - the convolution neural network (CNN). A database containing a
comprehensive collection of HWTT results was used to develop a CNN-based
machine learning prediction model. The database includes 10,000 rutting depth
data points measured across a large variety of asphalt mixtures. The model has
been formulated in terms of known influencing mixture variables such as asphalt
binder high temperature performance grade, mixture type, aggregate size,
aggregate gradation, asphalt content, total asphalt binder recycling content,
and testing parameters, including testing temperature and number of wheel
passes. A rigorous validation process was used to assess the accuracy of the
model to predict total rut depth and the HWTT rutting curve. A sensitivity
analysis is presented, which evaluates the effect of the investigated variables
on rutting depth predictions by the CNN model. The model can be used as a tool
to estimate the rut depth in asphalt mixtures when laboratory testing is not
feasible, or for cost saving, pre-design trials.
