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Automatic Detection Method of Localized Pavement Roughness Using Quarter Car Model by Lifting Wavelet Filters

並列摘要


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.

參考文獻


Fugal, D.L. (2009). Conceptual Wavelets in Digital Signal Processing, Space and Signals Technical Publishing, San Diego, CA, USA.
Wei, L., and Fwa, T.F. (2004). Characterizing Road Roughness By Wavelet Transform. Transportation Research Record, No. 1869, pp. 152-158.
Shokouhi, P., Gucunski, N., Maher, A., and Zaghloul, S.M. (2005). Wavelet-Based Multiresolution Analysis of Pavement Profiles as a Diagnostic Tool, Transportation Research Record, No. 1940, pp. 79-88.
Sweldens, W. (1997). The Lifting Scheme: A Construction of Second Generation Wavelets, SIAM Journal on Mathematical Analysis, 29(2), pp. 511-546.
Jansen, M., and Oonincx, P. (2004). Second Generation Wavelets and Application, Springer, London, UK.

被引用紀錄


Liu, Y. W. (2014). 含曲率效應之阮道-桑卓姆型膜宇宙學:早期及晚期宇宙 [doctoral dissertation, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU.2014.01802

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