Development of Standardization and Management System for the Severity of Unpaved Test Courses

Kang, Do-Kyung; Lee, Sang-Ho; Goo, Sang-Hwa

doi:10.3390/s7092004

Cited by 10 publications

(3 citation statements)

References 5 publications

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“…Several road characterization techniques have been developed for the automotive industry. These methods can be separated in two overall categories: direct measurement of the pavement profile with various types of instrumentation [12] [13] and the use of mechanical devices or systems with acceleration measurements to infer the pavement profile [13][14][15][16][17]. The technique presented in this paper belongs to the second category.…”

Section: Methodsmentioning

confidence: 99%

A Laboratory Excitation Technique to Test Road Bike Vibration Transmission

2016

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This paper describes a technique designed to measure the in-situ acceleration signals that will be used to drive a road simulator in the study of road bike vibration transmission in a laboratory setting. To measure the signals, a bike mounted by a cyclist and towed by a motor vehicle is used. A road simulator using actuators driven by a digital signal is described. The impulse response of the bike used to measure road data is convoluted with the road acceleration in order to obtain the required actuator signal. The reproduction capacity of the simulator is evaluated by comparing the frequency content as well as the time statistical parameters of the acceleration signal measurement on road to the acceleration obtained on the simulator. On a granular road with a broadband excitation spectrum, the vertical excitation obtained with the simulator adequately mimics the measured road acceleration. This technique can be used to compare vibration transmission characteristics among different road bikes.

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Section: Methodsmentioning

confidence: 99%

A Laboratory Excitation Technique to Test Road Bike Vibration Transmission

2016

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“…It is also relatively easy to translate DSDs into International Roughness Indices (IRIs) which themselves are popular measures of road roughness. A similar approach was employed by Kang, Lee and Goo [18] except that they used the DSDs computed from profilometer measured data which might be quite demanding.…”

mentioning

confidence: 99%

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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“…Kang, Lee and Goo [17] developed a road profilometer for unpaved courses by employing a momentum back-propagation neural network to estimate the road profiles. Yousefzadeh, Azadi and Soltani [18] demonstrate a methodology where the vertical vehicle accelerations are determined through an ADAMS model, and a static feed-forward neural network is used for estimating the road profiles.…”

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confidence: 99%

Application of an ANN-based methodology for road surface condition identification on mining vehicles and roads

Ngwangwa

Heyns

2014

Journal of Terramechanics

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 No vehicle models are employed for generation of training data. Vehicles are tested in their normal operating environments. Road profiles are crudely measured yet of satisfactory frequency content. The methodology is tested for different vehicle speeds and road profiles. AbstractAn artificial neural networks-based methodology for the identification of road surface condition was applied to two different vehicles in their normal operating environments at two mining sites.An ultra-heavy haul truck used for hauling operations in surface mining and a small utility underground mine vehicle were utilised in the current investigation. Unlike previous studies where numerical models were available and road surfaces were accurately profiled with profilometers, in this study, that was not the case in order to replicate the real mine road management situation. The results show that the methodology performed very well in reconstructing discrete faults such as bumps, depressions or potholes but, owing to the inevitable randomness of the testing conditions, these conditions could not fit the fine undulations present on the arbitrary random rough surface. These are better represented by the spectral displacement densities of the road surfaces. Accordingly, the proposed methodology can be applied to road condition identification in two ways: firstly, by detecting, locating and quantifying any existing discrete road faults/features, and secondly, by identifying the general level of the road's surface roughness.2

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Development of Standardization and Management System for the Severity of Unpaved Test Courses

Cited by 10 publications

References 5 publications

A Laboratory Excitation Technique to Test Road Bike Vibration Transmission

A Laboratory Excitation Technique to Test Road Bike Vibration Transmission

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

Application of an ANN-based methodology for road surface condition identification on mining vehicles and roads

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