Wavelet analysis and interpretation of road roughness

Road roughness indices such as the International Roughness Index, Quarter-car Index, and root-mean-square vertical acceleration are useful as indicators of the level of pavement serviceability performance. Each of these summary roughness statistics offers a convenient index for monitoring the trend of pavement roughness deterioration with time. However, they do not retain the actual contents of pavement surface roughness. Such detailed roughness information may be useful for maintenance operations, detection of pavement surface distresses, and detailed analysis of the trend of pavement roughness deterioration. This paper presents an application procedure based on wavelet theory to offer supplementary information to a roughness index and provide additional information on the characteristics of the roughness profile of interest. The procedure is able to identify the characteristics of a pavement roughness profile in both the frequency and distance domains. This study proposes methods of roughness data processing using different wavelet transformation and analysis techniques to extract useful information for pavement maintenance management. Numerical examples based on measured roughness profiles of the Long Term Pavement Performance (LTPP) database are presented to illustrate the types of useful information derivable with the proposed method of analysis. It is demonstrated that using appropriately selected analysis methods and wavelet parameters, detailed roughness features of interest to pavement engineers not currently available from summary roughness statistics can be obtained together with summary roughness statistics as part of the roughness survey report for highway agencies.