Reconstruction of road defects and road roughness classification using vehicle responses with artificial neural networks simulation
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Date
Authors
Ngwangwa, Harry Magadhlela
Heyns, P.S. (Philippus Stephanus)
Labuschagne, F.J.J. (Kobus)
Kululanga, Grant K.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
The road damage assessment methodology in this paper utilizes an artificial neural network that reconstructs road surface profiles
from measured vehicle accelerations. The paper numerically demonstrates the capabilities of such a methodology in the presence of noise,
changing vehicle mass, changing vehicle speeds and road defects. In order to avoid crowding out understanding of the methodology, a
simple linear pitch-plane model is employed. Initially, road profiles from known roughness classes were applied to a physical model to
calculate vehicle responses. The calculated responses and road profiles were used to train an artificial neural network. In this way, the
network renders corresponding road profiles on the availability of fresh data on model responses. The results show that the road profiles
and associated defects can be reconstructed to within a 20% error at a minimum correlation value of 94%.
Description
Keywords
Reconstruction of road defects, Road roughness classification, Artificial neural networks simulation, Vehicles
Sustainable Development Goals
Citation
H.M. Ngwangaw, P.S. Heyns, F.J.J. Labuschagne & G.K. Kululanga, Reconstruction of road defects and road roughness clasification using vehicl responses with artificial neural networks simulation, Journal of Terramechanics, vol. 47, no. 2, pp. 97-111 (2012), doi: 10.1016/j.terra.2009.08.007.