The 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. The paper introduces a numerical approach for decomposing non-stationary random vibration signals into constituent Gaussian elements by fitting a series of straight lines through the distribution estimates. The technique is based on the fact that the probability distribution function of a signal composed of a sequence of random Gaussian processes can be expressed as the sum of the individual distribution functions each weighted by the time fraction for which a Gaussian process of a particular standard deviation exists. The validity of the method was tested using a representative set of pavement profiles. Results yielded by the research is significant in that it affords great simplicity for the synthesis of road profiles which can be achieved without much difficulty when the process is Gaussian or, as is has been shown to be the case, a sequence of Gaussian events. (a) For the covering entry of this conference, please see ITRD abstract no. E217099.
Samenvatting