TY - JOUR

T1 - On the use of non-Gaussian models for statistical description of road micro-surface profiles

AU - Steinwolf, Alexander

AU - Wangenheim, Matthias

AU - Wallaschek, Jörg

N1 - Funding information: Alexander Steinwolf earned his PhD in Ukraine, where he became a Senior Scientist in a government research laboratory. After being a Visiting Researcher in Germany, the UK, and the USA, he spent several years as a faculty member at the University of Auckland and, then, founded the AST Consulting Ltd. He was the recipient of research fellowships from A von Humboldt Foundation, the UK Royal Society, The Leverhulme Trust, the European Commission, and the US National Research Council. He lectured at and conducted joint research with the US Air Force Research Laboratory, the US Army Aberdeen Test Center, and NASA’s Langley Research Center.

PY - 2019/8/8

Y1 - 2019/8/8

N2 - When analysing vehicle-road interaction, probability density function (PDF) of random micro-surface is required. Since the asperity tops are polished by tyres stronger than the valley bottoms, the surface height profiles become asymmetrical. As a result, the PDFs of micro-surface signals are often different from the Gaussian model and one needs a non-Gaussian PDF model operating with skewness and kurtosis. Previous solutions by the Pearson and Johnson distributions do not lend themselves for further implementation in analytical form. To overcome this difficulty, a non-Gaussian PDF can be constructed from a few Gaussian sections with different mean values and standard deviations. To use such a piecewise-Gaussian model for analytical derivations, it is simply necessary to apply the classic Gaussian equation several times. An example of skewed PDF of micro-surface of an asphaltic concrete highway measured by a laser scanning system was adequately approximated by the tetra-Gaussian model consisting of four Gaussian sections.

AB - When analysing vehicle-road interaction, probability density function (PDF) of random micro-surface is required. Since the asperity tops are polished by tyres stronger than the valley bottoms, the surface height profiles become asymmetrical. As a result, the PDFs of micro-surface signals are often different from the Gaussian model and one needs a non-Gaussian PDF model operating with skewness and kurtosis. Previous solutions by the Pearson and Johnson distributions do not lend themselves for further implementation in analytical form. To overcome this difficulty, a non-Gaussian PDF can be constructed from a few Gaussian sections with different mean values and standard deviations. To use such a piecewise-Gaussian model for analytical derivations, it is simply necessary to apply the classic Gaussian equation several times. An example of skewed PDF of micro-surface of an asphaltic concrete highway measured by a laser scanning system was adequately approximated by the tetra-Gaussian model consisting of four Gaussian sections.

KW - Kurtosis

KW - Non-Gaussian

KW - Probability density function

KW - Random micro-surface

KW - Skewness

KW - Vehicle-road interaction

UR - http://www.scopus.com/inward/record.url?scp=85070665286&partnerID=8YFLogxK

U2 - 10.1504/IJVSMT.2019.101551

DO - 10.1504/IJVSMT.2019.101551

M3 - Article

AN - SCOPUS:85070665286

VL - 13

SP - 260

EP - 274

JO - International Journal of Vehicle Systems Modelling and Testing

JF - International Journal of Vehicle Systems Modelling and Testing

SN - 1745-6436

IS - 3

ER -