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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/5352

Title: Reconstruction of road defects and road roughness classification using vehicle responses with artificial neural networks simulation
Authors: Ngwangwa, HM
Heyns, PS
Labuschagne, FJJ
Kululanga, GK
Keywords: Road surface profiles
Road roughness classes
Artificial neural network
Vehicle responses
Road damage
Terramechanics
Issue Date: Apr-2010
Publisher: Elsevier
Citation: Ngwangwa, HM, Heyns, PS et al. 2010. Reconstruction of road defects and road roughness classification using vehicle responses with artificial neural networks simulation. Journal of Terramechanics, Vol 47(2), pp 97-111
Series/Report no.: Workflow request;3640
Abstract: This paper proposes a procedure for utilizing measured responses on a vehicle to reconstruct road profiles and their attendant defects. The study seeks to capitalize on the popularization of vehicle information systems, where sensors are increasingly being mounted on vehicles for assessing vehicle performance and the structural integrity of suspensions. The paper numerically demonstrates the capabilities of road damage assessment 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: Copyright: 2010 Elsevier. This is an ABSTRACT ONLY
URI: http://www.sciencedirect.com/science/article/pii/S0022489809001141
http://hdl.handle.net/10204/5352
ISSN: 0022-4898
Appears in Collections:Infrastructure systems and operations
General science, engineering & technology

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