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As the list of states adopting the Hamburg wheel tracking device continues to grow, there is a need to evaluate how the results are used. AASHTO T-324 does not standardize the analysis and reporting of test results. Furthermore, the processing and the reporting of the results of manufacturers are not uniform. This inconsistency is partly the result of the variation of agency reporting requirements. Some requirements include only the midpoint rut depth; others include the average across the entire length of the wheel track. No guidance is given when the stripping infection point (SIP) is reported. To eliminate bias in reporting, statistical analysis was performed on more than 135 test runs on adjoining gyratory specimens. Measurement location was found to be a source of significant variation for rut depth in the Hamburg wheel tracking device. This variation was likely the result of the nonuniform wheel speed across the specimen, geometry of the specimen, and air void profile. Eliminating this source of bias when rutting results are reported is feasible although the feasibility depends on the average rut depth at the final pass. When the results of a test that has an average rut depth of less than 12 mm at the final pass is reported, the average of the measurements along a 6.4-in. path beginning at 0.5 in. from the specimen edge nearest the gear housing should be reported. When the average final-pass rut depth is greater than 12 mm, it is reasonable to report the average of the measurements just off the center of the rutting track along a 3.2-in. path beginning at 2.1 in. from the specimen edge nearest the gear housing. SIP values were also analyzed. It is reasonable to report the average of the SIP values across all locations while calculated values measured more than 2 in. from the specimen's edge are discarded and for cases in which the ratio of the stripping slope to creep slope exceeds 2.0. Validation is needed for multiple machines.

Researchers and practitioners have long recognized the advantages of mechanistic modeling. As agencies implement the Guide for Mechanistic–Empirical Design of New and Rehabilitated Pavement Structures [referred to as the Mechanistic–Empirical Pavement Design Guide (MEPDG)] in the local project-level pavement management system (PMS), its potential as a planning tool in network-level analyses remains unrecognized. A unique, large-scale application of MEPDG is presented. The Nebraska Department of Roads uses a decision tree that systematically screens and identifies candidates for entry into a multiyear optimization program. The process uses linear deterioration rates that have been shown to provide an R2 value as low as 14%. In a retroactive 5-year analysis, 86 sections were prescreened by using the existing models. Predictions indicated that 85 of the 86 sections would have been candidates for maintenance in the first 5 years, whereas field data suggested that only 23 should have been targeted. Calibrated MEPDG distress models calculated with local performance indices replaced the existing linear models. When the analysis was repeated with the mechanistic–empirical models, 35 of the 86 sections qualified for entry into the 5-year analysis, a 70% improvement in the accuracy of the forecasted cost. This research fulfills a timely need for the transitioning of network-level PMS toward mechanistic practices. Consideration of fundamental material properties, climatic conditions, and the structural response to traffic loading provides improved accuracy in planning. Furthermore, production variability and prediction uncertainties can be quantified and used as an additional probabilistic decision-making parameter. Detailed results of the implementation of MEPDG in the local network-level PMS are presented.