Decomposing pavement surface profiles into a Gaussian sequence

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.