Abstract
Causative pavement cracks in surface roughness require the functional evaluation derived from road profile data. This study examines an automatic detection method of transverse cracks on a surface profile in terms of a quarter car (QC) filtered roughness profile by lifting wavelet filters. Lifting wavelet filters are adaptive biorthogonal wavelet filters containing free parameters. In this study, we design a set of lifting wavelet filters for detecting severe cracks from the roughness profile. The set of filters includes free parameters that are intended to emphasize causative crack characteristics in the roughness profile. According to the results of adapting the filters to the roughness profile, the locations of severe cracks are identified, whereas locations that are not related to the QC response are not detected. Therefore, we conclude that the performance of the lifting wavelet filters contributes to automatic distress detection using response type profiling systems.
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