Abstract:
The paved road network is a critical asset to any economy. South Africa has a paved road
network that has an estimated value above R2 trillion. This asset is however under threat
as there was a backlog in maintenance of more than R416.6 billion in 2018. Heavy
vehicles are primarily responsible for road wear, and overloaded vehicles can cause more
than 60% of road wear. Most road wear assessments use static axle loads that are
assumed to be symmetrical on either side to calculate the road wear caused by a heavy
vehicle. Previous work has shown that the effect of crossfall (CF) cannot be ignored when
considering the dynamic road wear of heavy vehicles. This paper expands on previous
work through the development of a novel Gaussian process machine learning (GPML)
model that can predict the dynamic road wear of a rigid heavy vehicle given 15 input
parameters. The road wear criteria considered are the first (1st) and fourth (4th) power
aggregate forces on the left and right sides using the 95th and 99th percentile conditions.
The results show that the model is very accurate and requires comparatively few inputs to
train an accurate model. For interpolated results, the average absolute error is less than
1% and for extrapolated results, the average absolute error is less than 3%. The results
also include the standard deviation associated with the result which is important for future
research to minimise training examples. Using machine learning models to predict
dynamic road wear allows for rapid calculation and testing and also does not require
expensive multibody dynamics software tools to calculate. This would be very
advantageous to the industry, especially when developed for the Smart Truck Pilot Project.
