This study investigated the effect of the input level of axle load spectra on performance predictions obtained with the Mechanistic–Empirical Pavement Design Guide (MEPDG). Traffic monitoring data collected from 50 weigh-in-motion (WIM) stations distributed across the state of Ohio were analyzed to obtain site-specific and regional axle load spectra. The site-specific axle load spectra were calculated for each axle group (single, tandem, tridem, and quad) and truck class (Classes 4 through 13). The regional axle load spectra were developed on the basis of statewide averages and cluster analysis. The statewide average was calculated with information from all sites, whereas the cluster averages were created on the basis of Class 9 tandem axles. Baseline designs for new flexible pavements were defined in the MEPDG for each of the WIM sites having available data, and site-specific, statewide average, cluster average, and MEPDG default axle load spectra were used to assess the impact of the input level of axle load spectra on the performance of the pavement structure. The statewide average and the cluster averages were found to yield similar predictions of pavement performance, with the cluster averages being slightly closer to the site-specific predictions of pavement performance. In addition, the MEPDG default axle load spectra were found to underestimate the pavement service life significantly and, if used for design purposes, would result in overly conservative pavement layer thicknesses. Therefore, the authors recommend the use of site-specific axle load spectra when possible in the design of flexible pavements. However, if site-specific data are not available, statewide average axle load spectra should be used.
Get full access to this article
View all access options for this article.
References
1.
Guide for Design of Pavement Structures.AASHTO, Washington, D.C., 1993.
2.
ARA, Inc., ERES Consultants Division.Guide for Mechanistic–Empirical Design of New and Rehabilitated Pavement Structures. Final report, NCHRP Project 1–37A. Transportation Research Board of the National Academies, Washington, D.C., 2004. http://www.trb.org/mepdg/guide.htm.
3.
Mechanistic–Empirical Pavement Design Guide, Interim Edition: A Manual of Practice.AASHTO, Washington, D.C., 2008.
4.
TamW. O., and Von QuintusH.Use of Long-Term Pavement Performance Data to Develop Traffic Defaults in Support of Mechanistic–Empirical Pavement Design Procedures. In Transportation Research Record: Journal of the Transportation Research Board, No. 1855, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 176–182.
5.
PapagiannakisA., BracherM., and JacksonN.Utilizing Clustering Techniques in Estimating Traffic Data Input for Pavement Design.Journal of Transportation Engineering, Vol. 132, 2006, pp. 872–879.
6.
LuQ., and HarveyJ. T.Characterization of Truck Traffic in California for Mechanistic–Empirical Design. In Transportation Research Record: Journal of the Transportation Research Board, No. 1945, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 61–72.
7.
TranN. H., and HallK. D.Development and Significance of Statewide Volume Adjustment Factors in Mechanistic–Empirical Pavement Design Guide. In Transportation Research Record: Journal of the Transportation Research Board, No. 2037, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 97–105.
8.
WangY., HancherD., and MahboubK.Axle Load Distribution for Mechanistic–Empirical Pavement Design.Journal of Transportation Engineering, Vol. 133, No. 8, 2007, pp. 469–479.
9.
SwanD. J., TardifR., HajekJ. J., and HeinD. K.Development of Regional Traffic Data for the Mechanistic–Empirical Pavement Design Guide. In Transportation Research Record: Journal of the Transportation Research Board, No. 2049, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 54–62.
10.
LiJ., PierceL. M., HallenbeckM. E., and UhlmeyerJ. S.Sensitivity of Axle Load Spectra in the Mechanistic–Empirical Pavement Design Guide for Washington State. In Transportation Research Record: Journal of the Transportation Research Board, No. 2093, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 50–56.
11.
LuQ., ZhangY., and HarveyJ. T.Estimation of Truck Traffic Inputs for Mechanistic–Empirical Pavement Design in California. In Transportation Research Record: Journal of the Transportation Research Board, No. 2095, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 62–72.
12.
BuchN., HaiderS. W., BrownJ., and ChattiK.Characterization of Truck Traffic in Michigan for the New Mechanistic–Empirical Pavement Design Guide. Report No. RC-1537. Michigan Department of Transportation, Lansing, 2009.
13.
SmithB. C., and DiefenderferB. K.Analysis of Virginia-Specific Traffic Data for Use with Mechanistic–Empirical Pavement Design Guide. In Transportation Research Record: Journal of the Transportation Research Board, No. 2154, Transportation Research Board of the National Academies, Washington, D.C., 2010, pp. 100–107.
14.
SmithB. C., and DiefenderferB. K.Analysis of Virginia-Specific Traffic Data Inputs for Use with the Mechanistic–Empirical Pavement Design Guide. Report VTRC 10-R192010. Virginia Department of Transportation, Richmond, 2010.
15.
SayyadyF., StoneJ. R., TaylorK. L., JadounF. M., and KimY. R.Clustering Analysis to Characterize Mechanistic–Empirical Pavement Design Guide Traffic Data in North Carolina. In Transportation Research Record: Journal of the Transportation Research Board, No. 2160, Transportation Research Board of the National Academies, Washington, D.C., 2010, pp. 118–127.
16.
RuizJ. I. R.Characterization of Traffic Load and Local Calibration of the MEPDG for New Mexico. MA thesis. University of New Mexico, Albuquerque, 2011.
17.
IshakS., ShinH. C., and SridharB.Characterization and Development of Truck Load Spectra and Growth Factors for Current and Future Pavement Design Practices in Louisiana. Report FHWA/LA.11/445. Louisiana Department of Transportation and Development, Baton Rouge, 2011.
18.
RomanoschiS. A., MominS., BethuS., and BendanaL.Development of Traffic Inputs for New Mechanistic–Empirical Pavement Design Guide: Case Study. In Transportation Research Record: Journal of the Transportation Research Board, No. 2256, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 142–150.
19.
SayyadyF., StoneJ. R., ListG. F., JadounF. M., KimY. R., and SajjadiS.Axle Load Distribution for Mechanistic–Empirical Pavement Design in North Carolina: Multidimensional Clustering Approach and Decision Tree Development. In Transportation Research Record: Journal of the Transportation Research Board, No. 2256, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 159–168.
20.
WangK., LiQ., HallK., NguyenV., and XiaoD.Development of Truck Loading Groups for the Mechanistic–Empirical Pavement Design Guide.Journal of Transportation Engineering, Vol. 137, No. 12, 2011, pp. 855–862.
21.
HaiderS. W., BuchN., ChattiK., and BrownJ.Development of Traffic Inputs for Mechanistic–Empirical Pavement Design Guide in Michigan. In Transportation Research Record: Journal of the Transportation Research Board, No. 2256, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 179–190.
22.
El-BadawyS. M., BayomyF. M., and FugitS. W.Traffic Characteristics and Their Impact on Pavement Performance for the Implementation of the Mechanistic–Empirical Pavement Design Guide in Idaho.International Journal of Pavement Research and Technology, Vol. 5, No. 6, 2012, pp. 386–394.
23.
BayomyF., El-BadawyS., and AwedA.Implementation of the MEPDG for Flexible Pavements in Idaho. Report FHWA-ID-12-193. Idaho Transportation Department, Boise, 2012.
24.
SchildhauerC., and SikR.Calibration, Verification and Binning Resolution Correction Techniques of WIM Data. Presented at 32nd Annual Southern African Transport Conference, Pretoria, South Africa, 2013.
25.
SlavikM.Axle Load Estimation Without Weigh-in-Motion Survey. Presented at 32nd Annual Southern African Transport Conference, Pretoria, South Africa, 2013.
26.
PapagiannakisA. T., BracherM., LiJ., and JacksonN.Sensitivity of NCHRP 1–37A Pavement Design to Traffic Input. In Transportation Research Record: Journal of the Transportation Research Board, No. 1945, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 49–55.
27.
SayyadyF., StoneJ. R., and KimY. R.Effects of Sampled Weigh-in-Motion Data on Axle Load Distribution for Mechanistic–Empirical Pavement Design in North Carolina. Presented at 90th Annual Meeting of the Transportation Research Board, Washington, D.C., 2011.
28.
TranN. H., and HallK. D.Development and Influence of Statewide Axle Load Spectra on Flexible Pavement Performance. In Transportation Research Record: Journal of the Transportation Research Board, No. 2037, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 106–114.
29.
Traffic Monitoring Guide, 2001 ed. FHWA, U.S. Department of Transportation, 2001.
