Evaluation of acceptance risks and rewards for highway materials using statistical power analysis

References

• American Association of State Highway and Transportation Officials, 2010. Standard specifications for transportation materials and methods of sampling and testing, 30, Washington, DC.
   Google Scholar
• American Association of State Highway and Transportation Officials. AASHTO. Highway Subcommittee on Construction, 1996. Implementation manual for quality assurance.
   Google Scholar
• Burati, J.L., et al, 2003. Optimal procedures for quality assurance specifications (No. FHWA-RD-02-095). Turner-Fairbank Highway Research Center.
   Google Scholar
• Burati, J.L., et al., 2004. Evaluation of procedures for quality assurance specifications (No. FHWA-HRT-04-046). Turner-Fairbank Highway Research Center.
   Google Scholar
• Burati, J.L., et al., 2010. Validation of contractor HMA testing data in the materials acceptance process (No. FHWA-SC-10-02). South Carolina. Dept. of Transportation.
   Google Scholar
• Carr, E.A., Shuler, M.G., and Burati Jr, J.L., 2016. Validation of contractor HMA testing data in the materials acceptance process-phase II (No. FHWA-SC-16-03). South Carolina. Dept. of Transportation.
   Google Scholar
• Coenen, A.R., et al., 2019. State DOT implementation of statistical analysis and percent within limits. Transportation Research Record, 2673 (2), 583–592.
   Web of Science ®Google Scholar
• Federal Highway Administration (FHWA), 1995. Quality assurance procedures for construction. Final rule, 23 CFR 637. Federal Register, 60 (125), 33712–33719.
   Google Scholar
• FHWA, 2021. Quality assurance in materials and construction: Observations and recommendations, Washington, D.C. Retrieved October 10, 2021, from https://www.fhwa.dot.gov/construction/cqit/qamc0607/05.cfm.
   Google Scholar
• Grogg, M.G, 2021. Quality assurance stewardship review, Summary Report 2013 through 2018 (No. FHWA-HIF-20-059).
   Google Scholar
• Hughes, C.S., et al., 2011. Guidelines for quality-related pay adjustment factors for pavements. NCHRP Project, (10-79).
   Google Scholar
• Karimi, S.S., Goulias, D.G., and Schwartz, C.W, 2012. Evaluation of superpave HMA mixture properties at the plant versus behind the paver: statistical comparison of QC and QA data. Journal of Transportation Engineering, 138 (7), 924–932.
   Google Scholar
• Karimi, S.S., Goulias, D.G., and Schwartz, C.W, 2015. Risk and expected pay factor analysis for assessing gap and dense-graded Superpave mixture specifications. International Journal of Pavement Engineering, 16 (1), 69–79.
   Web of Science ®Google Scholar
• Killingsworth, B.M., and Hughes, C.S, 2002. Issues related to use of Contractor quality control data in acceptance decision and payment: benefits and pitfalls. Transportation Research Record, 1813 (1), 247–252.
   Google Scholar
• LaVassar, C.J., et al., 2009. Statistical assessment of quality assurance-quality control data for hot mix asphalt. Report No. WA-RD, 686.
   Google Scholar
• Mahboub, K.C., Hancher, D.E., and Wang, Y, 2004. Contractor-performed quality control: Is the fox guarding the henhouse? Journal of Professional Issues in Engineering Education and Practice, 130 (4), 255–258.
   Web of Science ®Google Scholar
• Maryland State Highway Administration. Standard Specifications for Construction and Materials, section 504, 2021. MDOT. Hanover, MD.
   Google Scholar
• National Academies of Sciences, Engineering, and Medicine, 2020. Procedures and guidelines for validating contractor test data.
   Google Scholar
• Nimeri, M.A., 2019. Statistical validation implementation in pavement construction projects (Doctoral dissertation, University of Nevada, Reno).
   Google Scholar
• Schmitt, R.L., et al., 2001. Statistically based methods for verification testing. Transportation Research Record, 1761 (1), 86–92.
   Google Scholar
• Transportation Research Circular E-C235: Glossary of Highway Quality Assurance Terms, 2018. Transportation research board of the national academies, Washington, D.C.. http://onlinepubs.trb.org/onlinepubs/circulars/ec235.pdf.
   Google Scholar
• Turochy, R.E., and Parker, F, 2007. Comparisons of contractor and state transportation agency quality assurance test results on mat density of hot-mix asphalt concrete: findings of multistate analysis. Transportation Research Record, 2040 (1), 41–47.
   Google Scholar
• Wani, S.S., and Gharaibeh, N.G, 2013. Evaluating the use of contractor-performed test results in highway construction and material acceptance decisions. Journal of Infrastructure Systems, 19 (1), 67–73.
   Web of Science ®Google Scholar
• Zhao, Y., and Goulias, D, 2021a. Evaluation of acceptance risks through percent within limit for highway materials and pavement construction. Transportation Research Record, 2675 (9), 1175–1184.
   Web of Science ®Google Scholar
• Zhao, Y., and Goulias, D, 2021b. Pay factor analysis for highway pavements through percentage of material within specification limits and monte carlo simulation. Transportation Research Record, 2675 (11), 1258–1271.
   Web of Science ®Google Scholar
• Zhao, Y, and Goulias, D., 2022. “Risk and pay factor assessment of using contractor quality control data for acceptance.” 101st Transportation Research Board Annual Meeting, National Academy of Science. Washington D.C. January 9-13, 2022.
   Google Scholar