Signal processing approach to road roughness analysis and measurement

In this paper, the application of signal processing for the analysis of discretely sampled road profile data of sealed bituminous (or flexible) pavements is considered. Firstly, road profile data is modelled as a function of time. Then power spectrum density (PSD) analysis is briefly introduced as a tool for estimating the distribution of energy of various wavebands embedded in the road elevation. It is shown that this analysis can be used to discriminate the deterioration modes in pavement structure by identifying features that contribute to the roughness. Moreover, wavelet analysis has been introduced as an alternative signal processing method for road profile analysis. It not only verifies road roughness features, but it is also able to locate high frequency defects such as cracks and potholes. Two experimental low traffic volume highway sections of 100 m in length (one smooth and one rough) are selected to examine PSD and wavelet analysis. Results show that wavelet based road profile analysis can be used as a better diagnostic tool than PSD for multiresolution analysis and measurement of pavement roughness.