Local calibration of rigid pavement performance models using resampling methods

References

• AASHTO. 2008. Mechanistic-empirical pavement design guide: a manual of practice: interim edition. Washington DC: American Association of State Highway and Transportation Officials.
   Google Scholar
• Baladi, G.Y., Dawson, T., and Sessions, C., 2009. Pavement subgrade MR design values for michigan’s seasonal changes, final report. Lansing, MI: Michigan Department of Transportation, Construction and Technology Division.
   Google Scholar
• Baladi, G.Y., Thottempudi, K.A., and Dawson, T., 2010. Backcalculation of unbound granular layer moduli, final report. Lansing, MI: Michigan Department of Transportation, Construction and Technology Division.
   Google Scholar
• Brink, W., et al., 2013. Sensitivity of input variables for rigid pavement rehabilitation strategies in the MEPDG. In: ASCE T&DI eds. Congress airfield and highway pavements. Los Angeles CA: ASCE, 528–538.10.1061/9780784413005
   Google Scholar
• Buch, N., et al., 2008. Evaluation of the 1-37A design process for new and rehabilitated JPCP and HMA pavements, final report. Lansing: Michigan Department of Transportation, Construction and Technology Division.
   Google Scholar
• Buch, N., et al., 2009. Characterization of truck traffic in Michigan for the new mechanistic empirical pavemnet design guide, final report. Lansing, MI: Michigan Department of Transportation, Construction and Technology Division.
   Google Scholar
• Buch, N. and Jahangirnejad, S., 2008. Quantifying coefficient of thermal expansion values of typical hydraulic cement concrete paving mixtures, final report. Lansing, MI: Michigan Department of Transportation, Construction and Technology Division.
   Google Scholar
• Fox, J., 2002. Bootstrapping regression models. In: An R and S-PLUS companion to applied regression: a web appendix to the book. Thousand Oaks, CA: Sage. Available from: http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-bootstrapping.pdf.
   Google Scholar
• Glover, L.T. and Mallela, J., 2009. Guidelines for implementing NCHRP 1-37A M-E design procedures in Ohio: volume 4 – MEPDG models validation & recalibration. Columbus, OH: Department of Transportation.
   Google Scholar
• Haider, S.W., Brink, W., and Buch, N., 2015. Calibrating the flexible new and rehabilitation pavement performance models using bootstrapping. In: Proceedings of the 94th annual transportation research board annual meeting CD ROM. Washington, DC: Transportation Research Board.
   Google Scholar
• Haider, S.W., et al., 2014. Preparation for implementation of the mechanistic-empirical pavement design guide in Michigan – part 3: local calibration and validation of the pavement-ME performance models. Lansing, MI: Michigan Department of Transportation.
   Google Scholar
• Harsini, I., et al., 2013. Sensitivity of input variables for flexible pavement rehabilitation strategies in the M-E PDG. In: Proceedings of ASCE T&DI, congress airfield and highway pavements, Los Angeles, CA: ASCE, 539–550.
   Google Scholar
• Hoerner, T.E., et al., 2000. Improved prediction models for PCC pavement performance-related specifications. McLean, VA: Federal Highway Administration.
   Google Scholar
• Khazanovich, L., et al., 1998. Common characteristics of good and poorly performing PCC pavements. McLean, VA: Federal Highway Administration.
   Google Scholar
• Kutay, E. and Jamrah, A., 2012. Preparation for implementation of the mechanistic-empirical pavement design guide in Michigan: part 1 - HMA mixture characterization. Lansing, MI: Michigan Department of Transportation, West Ottawa.
   Google Scholar
• Li, J., et al., 2005. Calibration of the rigid pavement portion of the NCHRP 1-37A software for use by the Washington state department of transportation. Washington, DC: Transportation Research Record.
   Google Scholar
• Li, J., et al., 2006. Calibration of the rigid pavement portion of the NCHRP 1-37A software for use by the Washington state department of transportation. CD-ROM, Washington, DC: Transportation Research Record.
   Google Scholar
• Mallela, J., et al., 2009a. Implementing the AASHTO mechanistic-empirical pavement design guide in Missouri volume II: MEPDG model validation and calibration. Springfield, MO: Department of Transportation.
   Google Scholar
• Mallela, J., et al., 2009b. Implementing the AASHTO mechanistic-empirical pavement design guide in Missouri volume I: study findings, conclusions, and recommendations. Springfield, MO: Missouri Department of Transportation.
   Google Scholar
• Mallela, J., et al., 2013. Implementation of the AASHTO mechanistic-empirical pavement design guide for Colorado. Denver, CO: Colorado Department of Transportation.
   Google Scholar
• NCHRP Project 1-40B, 2009. Local calibration guidance for the recommended guide for mechanistic-empirical pavement design of new and rehabilitated pavement structures. Washington, DC: Final NCHRP Report.
   Google Scholar
• Pierce, L.M., and McGovern, G., 2014. Implementation of the AASHTO mechanistic-empirical pavement design guide and software. Washington, DC: Transportation Research Board.
   Google Scholar
• Sahinler, S. and Topuz, D., 2007. Bootstrap and jackknife resampling algorithms for estimation of regression parameters. Journal of Applied Quantitative Methods, 2 (2), 188–199.
   Google Scholar
• Schwartz, C.W., et al., 2011. Sensitivity evaluation of MEPDG performance prediction. Washington, DC: Transportation Research Board.
   Google Scholar
• Velasquez, R., et al., 2009. Implementation of the MEPDG for new and rehabilitated pavement structures for design of concrete and asphalt pavements in Minnesota. St. Paul, MN: Minnesota Department of Transportation.
   Google Scholar
• Von Quintus, H., et al., 2003. Jackknife testing – an experiment approach to refine model calibration and validation. Washington, DC: Research Results Digest 283, National Cooperative Highway Research Program.
   Google Scholar