Abstract:
An artificial neural networks-based methodology for the identification of road surface condition was applied to two different vehicles
in their normal operating environments at two mining sites. An ultra-heavy haul truck used for hauling operations in surface mining and
a small utility underground mine vehicle were utilised in the current investigation. Unlike previous studies where numerical models were
available and road surfaces were accurately profiled with profilometers, in this study, that was not the case in order to replicate the real
mine road management situation. The results show that the methodology performed very well in reconstructing discrete faults such as
bumps, depressions or potholes but, owing to the inevitable randomness of the testing conditions, these conditions could not fit the fine
undulations present on the arbitrary random rough surface. These are better represented by the spectral displacement densities of the
road surfaces. Accordingly, the proposed methodology can be applied to road condition identification in two ways: firstly, by detecting,
locating and quantifying any existing discrete road faults/features, and secondly, by identifying the general level of the road’s surface
roughness.
