Profiling of rough terrain

Becker, Carl; Schalk, P.

doi:10.1504/ijvd.2014.058500

Cited by 28 publications

(31 citation statements)

References 6 publications

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“…This accuracy is very satisfactory for the purposes of this study where small bumps may be neglected due to the tire filtering effect [8,9]. The vehicle tires tend to cushion the vehicle from the impacts of surface undulations whose wavelengths are less than its contact patch width.…”

Section: Nominal Road Profilesmentioning

confidence: 99%

“…In this study, two kinds of road profiles are used: trapezoidal-shaped bump profiles and Belgian paving. The unique feature is that these profiles are based on real measured road surfaces [8,9]. The bump profiles were made of trapezoidally shaped steel blocks whose dimensions could be obtained using a one meter rule.…”

Section: Nominal Road Profilesmentioning

confidence: 99%

“…The profile measures were taken by Becker [8,9] using three different methods: 'can-can' machine, photogrammetry and laser scanner, which are shown in his work to be accurately correlated [8,9]. It is however reported that the 'can-can' machine provided a better profile measuring tool due to its ease in setting up, operation and data pre-processing.…”

Section: Nominal Road Profilesmentioning

confidence: 99%

“…It is reported that when a 'can-can' machine is properly used it is capable of achieving profiling errors of less than a few millimetres [8,9].…”

Section: Nominal Road Profilesmentioning

confidence: 99%

“…It has been adapted for suspension tests to allow for suspension settings to be safely switched between ride comfort and handling modes [5], a system that is known as four state semi-active suspension (4S 4 ) [4]. The vehicle response measurements used in this paper were carried out by Breytenbach [6,7] using an eDAQ lite measurement system while the road profiles were measured by Becker [8,9] using a 'can-can' machine. The tests were performed at Gerotek, a world class vehicle testing infrastructure in Pretoria, South Africa.…”

mentioning

confidence: 99%

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Reconstruction of road defects and road roughness classification using Artificial Neural Networks simulation and vehicle dynamic responses: Application to experimental data

Ngwangwa

Heyns

Breytenbach

et al. 2014

Journal of Terramechanics

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Highlights Estimation of road profiles and classes using neural networks on measured data. Discrete obstacles are reconstructed with higher correlation than Belgian pave. Ride comfort mode has better quality in reconstructed profiles than handling mode. Consistently good approximations of DSDs occur between 0.2 and 1.8 cycles/m. AbstractThis paper reports the performance of an Artificial Neural Network based road condition monitoring methodology on measured data obtained from a Land RoverDefender 110 which was driven over discrete obstacles and Belgian paving. In a previous study it was demonstrated, using data calculated from a numerical model, that the neural network was able to reconstruct road profiles and their associated defects within good levels of fitting accuracy and correlation. A nonlinear autoregressive network with exogenous inputs was trained in a series-parallel framework. When compared to the parallel framework, the series-parallel framework offered the advantage of fast training but had a shortcoming in that it required feed-forward of true road profiles. In this study, the true profiles are not available and the test data are obtained from field measurements. Training data are numerically generated by making minor adjustments to the real measured profiles and applying them to a full vehicle 2 model of the Land Rover. This is done to avoid using the same road profile and acceleration data for training and testing or validating the neural network. A static feedforward neural network is trained and consequently tested on the real measured data.The results show very good correlations over both the discrete obstacles and the Belgian paving. The random nature of the Belgian paving necessitated correlations to be made using their displacement spectral densities as well as evaluations of RMS error percent values of the raw road profiles. The use of displacement spectral densities is considered to be of much more practical value than the road profiles since they can easily be interpreted into road roughness measures by plotting them over an internationally recognized standard roughness scale.

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Section: Nominal Road Profilesmentioning

confidence: 99%

Section: Nominal Road Profilesmentioning

confidence: 99%