dc.contributor.author |
Ngwangwa, HM
|
|
dc.contributor.author |
Heyns, PS
|
|
dc.contributor.author |
Labuschagne, FJJ
|
|
dc.contributor.author |
Kululanga, GK
|
|
dc.date.accessioned |
2011-11-30T10:29:18Z |
|
dc.date.available |
2011-11-30T10:29:18Z |
|
dc.date.issued |
2010-04 |
|
dc.identifier.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 |
en_US |
dc.identifier.issn |
0022-4898 |
|
dc.identifier.uri |
http://www.sciencedirect.com/science/article/pii/S0022489809001141
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/5352
|
|
dc.description |
Copyright: 2010 Elsevier. This is an ABSTRACT ONLY |
en_US |
dc.description.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%. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Elsevier |
en_US |
dc.relation.ispartofseries |
Workflow request;3640 |
|
dc.subject |
Road surface profiles |
en_US |
dc.subject |
Road roughness classes |
en_US |
dc.subject |
Artificial neural network |
en_US |
dc.subject |
Vehicle responses |
en_US |
dc.subject |
Road damage |
en_US |
dc.subject |
Terramechanics |
en_US |
dc.title |
Reconstruction of road defects and road roughness classification using vehicle responses with artificial neural networks simulation |
en_US |
dc.type |
Article |
en_US |
dc.identifier.apacitation |
Ngwangwa, 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/10204/5352 |
en_ZA |
dc.identifier.chicagocitation |
Ngwangwa, 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/10204/5352 |
en_ZA |
dc.identifier.vancouvercitation |
Ngwangwa 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. |
en_ZA |
dc.identifier.ris |
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 -
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en_ZA |