Ngwangwa, HMHeyns, PSLabuschagne, FJJKululanga, GK2011-11-302011-11-302010-04Ngwangwa, 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 97-1110022-4898http://www.sciencedirect.com/science/article/pii/S0022489809001141http://hdl.handle.net/10204/5352Copyright: 2010 Elsevier. This is an ABSTRACT ONLYThis 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%.enRoad surface profilesRoad roughness classesArtificial neural networkVehicle responsesRoad damageTerramechanicsArticleNgwangwa, H., Heyns, P., Labuschagne, F., & Kululanga, G. (2010). Reconstruction of road defects and road roughness classification using vehicle responses with artificial neural networks simulation. http://hdl.handle.net/10204/5352Ngwangwa, HM, PS Heyns, FJJ Labuschagne, and GK Kululanga "Reconstruction of road defects and road roughness classification using vehicle responses with artificial neural networks simulation." (2010) http://hdl.handle.net/10204/5352Ngwangwa H, Heyns P, Labuschagne F, Kululanga G. Reconstruction of road defects and road roughness classification using vehicle responses with artificial neural networks simulation. 2010; http://hdl.handle.net/10204/5352.TY - Article AU - Ngwangwa, HM AU - Heyns, PS AU - Labuschagne, FJJ AU - Kululanga, GK AB - 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%. DA - 2010-04 DB - ResearchSpace DP - CSIR KW - Road surface profiles KW - Road roughness classes KW - Artificial neural network KW - Vehicle responses KW - Road damage KW - Terramechanics LK - https://researchspace.csir.co.za PY - 2010 SM - 0022-4898 T1 - Reconstruction of road defects and road roughness classification using vehicle responses with artificial neural networks simulation TI - Reconstruction of road defects and road roughness classification using vehicle responses with artificial neural networks simulation UR - http://hdl.handle.net/10204/5352 ER -