@article{ref1,
title="Decomposing pavement surface profiles into a Gaussian sequence",
journal="International journal of vehicle modelling and testing",
year="2009",
author="Rouillard, Vincent",
volume="4",
number="4",
pages="288-305",
abstract="This paper proposes that the non-Gaussian (leptokurtic) nature of pavement surface elevation data is a direct result of the inherent level-type non-stationarity of the process manifested as variations in magnitude or roughness. The hypothesis that random pavement profiles are essentially composed of a sequence of zero-mean random Gaussian processes of varying standard deviations is put forward and tested. This paper introduces a numerical approach for decomposing non-stationary random vibration signals into constituent Gaussian elements by extracting Gaussian component of varying root mean square (RMS) levels from the distribution estimates using a curve fitting algorithm. The validity of the method was tested using a representative set of pavement profiles. The decomposition method presented is significant in that it affords great simplicity for the synthesis of non-stationary pavement profiles which can be achieved without much difficulty when the process is represented by a sequence of Gaussian events.<p />",
language="",
issn="1745-6436",
doi="10.1504/IJVSMT.2009.032021",
url="http://dx.doi.org/10.1504/IJVSMT.2009.032021"
}