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Keywords: pavement roughness indices, international roughness index, wavelet analysis, heavy articulated truck index, pavement characterizing.

Summary

Road Authorities have long sought a measure of the performance of a road as a smooth surface that provides meaningful information about the condition of the road which in turn, indicates the level of user's comfort and safety. Review of the most popular road roughness measurements specifies that early attempts at assessment were based on the judgment of ride quality by a panel of experts. This was followed by a technique using the measurement of the accumulated vertical travel of an axle of a vehicle travelling over a fixed distance. The most recent methods which are widely adopted by road authorities use the computer generated passage of a quarter - car model over a road surface measured by a laser profilometer. In these methods, roughness is essentially reported in terms of accumulated vertical axle travel with distance. The roughness indices obtained by such an approach gives a general indication about unevenness of the surface of the road, however, they are not able to estimate the accurate location of a problem surface. With significant improvements in the accuracy of surface measurement and computation technology, it is expected to develop and adopt new techniques of calculating road roughness directly from raw profile data that is able to localize the problems of a defected surface. Attempts have been made to assess surface condition in terms of the wavebands by power spectral analysis (PSD) analysis and wavelet analysis.

In this study, we first classified and briefly reviewed the most popular road roughness indices. Then, a wavelet transform was used as a tool to analyse and characterise Australian thin seal flexible pavements with low volume traffic. A total of 571 pavement test sections (each 100m in length) located in Victoria, Australia were included in this study. Correlations have been evaluated for wavelet analysis versus the most common road roughness indices used in Australia, including the International Roughness Index (IRI) and the Heavy Articulated Truck Index (HATI). Correlations have also been evaluated against other less commonly used road profile statistics such as the Root Mean Square Vertical Acceleration (RMSVA) and the Mean Absolute Vertical Acceleration (MAVA). The IRI and HATI measurements were obtained for each test section from the RoadRuf software and wavelet analysis and other roughness indices were calculated using MATLAB. Regression statistical analysis was also performed. Results show that wavelet based road profile statistics has proven to be a useful tool for multi-resolution measurement of pavement roughness. Wavelet analysis of road profile measurements can explain the use of different roughness indices as there is a good correlation between some of the energy wave-bands and other roughness indices.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 91 PROCEEDINGS OF THE TWELFTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING Edited by: B.H.V. Topping, L.F. Costa Neves and R.C. Barros Paper 231 A Comparative Study of Roughness Indices for Monitoring the Performance of Thin Seal Flexible Pavements subjected to Low Traffic Volumes in Australia R. Hesami, K.J. McManus, R.P. Evans and R. Hassan Centre for Sustainable Infrastructure, Swinburne University of Technology, Hawthorn, Victoria, Australia doi:10.4203/ccp.91.231 purchase the full-text of this paper Full Bibliographic Reference for this paper R. Hesami, K.J. McManus, R.P. Evans, R. Hassan, "A Comparative Study of Roughness Indices for Monitoring the Performance of Thin Seal Flexible Pavements subjected to Low Traffic Volumes in Australia", in B.H.V. Topping, L.F. Costa Neves, R.C. Barros, (Editors), "Proceedings of the Twelfth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Stirlingshire, UK, Paper 231, 2009. doi:10.4203/ccp.91.231 Keywords: pavement roughness indices, international roughness index, wavelet analysis, heavy articulated truck index, pavement characterizing. Summary Road Authorities have long sought a measure of the performance of a road as a smooth surface that provides meaningful information about the condition of the road which in turn, indicates the level of user's comfort and safety. Review of the most popular road roughness measurements specifies that early attempts at assessment were based on the judgment of ride quality by a panel of experts. This was followed by a technique using the measurement of the accumulated vertical travel of an axle of a vehicle travelling over a fixed distance. The most recent methods which are widely adopted by road authorities use the computer generated passage of a quarter - car model over a road surface measured by a laser profilometer. In these methods, roughness is essentially reported in terms of accumulated vertical axle travel with distance. The roughness indices obtained by such an approach gives a general indication about unevenness of the surface of the road, however, they are not able to estimate the accurate location of a problem surface. With significant improvements in the accuracy of surface measurement and computation technology, it is expected to develop and adopt new techniques of calculating road roughness directly from raw profile data that is able to localize the problems of a defected surface. Attempts have been made to assess surface condition in terms of the wavebands by power spectral analysis (PSD) analysis and wavelet analysis. In this study, we first classified and briefly reviewed the most popular road roughness indices. Then, a wavelet transform was used as a tool to analyse and characterise Australian thin seal flexible pavements with low volume traffic. A total of 571 pavement test sections (each 100m in length) located in Victoria, Australia were included in this study. Correlations have been evaluated for wavelet analysis versus the most common road roughness indices used in Australia, including the International Roughness Index (IRI) and the Heavy Articulated Truck Index (HATI). Correlations have also been evaluated against other less commonly used road profile statistics such as the Root Mean Square Vertical Acceleration (RMSVA) and the Mean Absolute Vertical Acceleration (MAVA). The IRI and HATI measurements were obtained for each test section from the RoadRuf software and wavelet analysis and other roughness indices were calculated using MATLAB. Regression statistical analysis was also performed. Results show that wavelet based road profile statistics has proven to be a useful tool for multi-resolution measurement of pavement roughness. Wavelet analysis of road profile measurements can explain the use of different roughness indices as there is a good correlation between some of the energy wave-bands and other roughness indices. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £140 +P&P)
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