Ngwangwa, Harry MagadhlelaHeyns, P.S. (Philippus Stephanus)2015-07-072015-07-072014-06Ngwangwa, HM & Heyns, PS 2014, 'Application of an ANN-based methodology for road surface condition identification on mining vehicles and roads', Journal of Terramechanics, vol. 53, pp. 59-74.0022-4898 (print)1879-1204 (online)10.1016/j.jterra.2014.03.006http://hdl.handle.net/2263/46319An 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.en© 2014 ISTVS. Published by Elsevier Ltd. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Journal of Terramechanics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Terramechanics, vol. 53, pp. 59-74, 2014. date. doi : 10.1016/j.jterra.2014.03.006Mining haul roadsDisplacement spectral densityRoad roughness classificationArtificial neural networksRoad profile reconstructionRoad condition monitoringPostprint Article