Author(s)

R. HASSAN

Abstract

Wavelet analysis is a signal processing technique that can be used to decompose longitudinal road surface profile signal into a number of wavebands. The outputs of the analysis include the signals (elevation vs. distance) and energies (a measure of elevation variation, i.e. surface roughness) in the different wavebands. The application of wavelet analysis in road pavement management at project level is described herein through two case studies. The first involves using wavelet analysis outputs in identifying and locating excitation sources of dynamic wheel loads (DWL) along a road section. The second case study involves using these outputs in assessing the effectiveness of rehabilitation treatment in reducing surface roughness in the different wavebands along the length of a road section. The outcomes of this research study indicate that the proposed applications are effective in addressing the intended purposes. Study findings also indicate that using HATI to highlight sections subject to high DWL at network level is promising. However, further testing is required to confirm its suitability at different speeds and operating environments.

These assessment approaches help asset managers to identify and locate surface characteristics that increase pavement damage, propose suitable treatments and assess the quality of these treatments. In addition to achieving value for money, adopting such approach would ensure their assets’ sustainability, mobility and comfort of all road users, in particular truck drivers. Long wavelength roughness with high contributions to DWL also has a detrimental effect on heavy vehicle ride.

Keywords

Dynamic wheel loads, granular overlay, heavy articulated truck index, profile data, road pavements, roughness, simulation, wavelet analysis