Journal article
Traffic inputs for mechanistic-empirical pavement design guide using weigh-in-motion systems in Alberta
The international journal of pavement engineering, Vol.15(6), pp.483-494
07/03/2014
Handle:
https://hdl.handle.net/2376/107301
Abstract
Mechanistic-empirical pavement design guide (MEPDG) uses axle load spectra and the number of axle applications to characterise traffic loads for pavement design. Alberta Transportation installed weigh-in-motion (WIM) systems at six highway locations to characterise traffic loads in Alberta for MEPDG design. Seasonal and regional trends in traffic characteristics of the six WIM sites were investigated and compared with the default values in the MEPDG for the years 2009 and 2010. Truck traffic classification (TTC) and axle load distribution factor (ALDF) for the WIM sites showed deviations from the MEPDG defaults. Seasonal variations were also evident in the distribution of different classes of truck throughout the year. Differences are attributed to cold climate conditions and special truck traffic in Alberta because of local industries. Influence of the differences between site-specific traffic characteristics and the MEPDG defaults on the performance of both flexible and rigid pavements for Alberta conditions was investigated through a sensitivity analysis. It was found that the flexible pavement performance is sensitive to TTC and ALDF, and the rigid pavement performance is most sensitive to ALDF.
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Details
- Title
- Traffic inputs for mechanistic-empirical pavement design guide using weigh-in-motion systems in Alberta
- Creators
- Somayeh Nassiri - Markin/CNRL Natural Resources Engineering Facility, University of AlbertaAlireza Bayat - Markin/CNRL Natural Resources Engineering Facility, University of AlbertaPeter Kilburn - Alberta Transportation
- Publication Details
- The international journal of pavement engineering, Vol.15(6), pp.483-494
- Academic Unit
- Civil and Environmental Engineering, Department of
- Publisher
- Taylor & Francis
- Identifiers
- 99900547233701842
- Language
- English
- Resource Type
- Journal article