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dc.contributor.advisorTurochy, Rod E.
dc.contributor.authorMai, Derong
dc.date.accessioned2013-11-15T21:57:51Z
dc.date.available2013-11-15T21:57:51Z
dc.date.issued2013-11-15
dc.identifier.urihttp://hdl.handle.net/10415/3925
dc.description.abstractThe Mechanistic-Empirical Pavement Design Guide (MEPDG) is well-recognized as the next generation of pavement design. State transportation agencies across the U.S. are moving toward this method by implementing the MEPDG software, now known as DARWin-ME and made available through AASHTO. Compared to the AASHTO Pavement Design Guide of 1993, one of the major improvements in the MEPDG occurs in its characterization of traffic. Instead of converting all truck axles to 18,000 lb equivalent single axles (ESALs), the Mechanistic-Empirical Pavement Design Guide (MEPDG) simulates every truck axle, and the associated stresses and strains imposed on the pavement structure, from a wide range of vehicle class distributions (VCD) and axle load spectra (ALS). The MEPDG also enables pavement engineers to design pavements for various circumstances at different levels (among site/direction-specific, cluster-averaged, statewide, and nationwide) based on available traffic data. However, the recommendations of appropriate levels of traffic inputs in the MEPDG for local pavement design are currently an issue. The MEPDG community generally agreed to use quality control (QC), sensitivity analysis, and cluster analysis, to solve this problem, but subjective factors are currently involved. This dissertation developed objective procedures in QC, sensitivity analysis, and cluster analysis that also streamline the overall processes. As the first step of the overall procedure, an objective approach to QC of WIM data includes threshold checks that detect implausible values of individual variables in the truck weight records and rational checks that examine patterns in axle load distributions and relationships among the variables. Instead of using subjective visual comparisons of gross vehicle weight (GVW) distributions, the QC in this research implements a peak-range check, peak-shift check, and correlation analysis to quantify the ALS comparison process of rational checks. Following the QC procedure, sensitivity analysis was developed to examine the potential for implementation of various levels of traffic inputs. The changes of design pavement thicknesses due to variations of traffic factors are used as sensitivity indicators. The effects of traffic inputs on pavement design can be deemed practically significant when the thickness needed to maintain an acceptable level of pavement performance changed 0.5 inches or more from the baseline thickness developed from statewide traffic inputs. The third procedure presented a new clustering combination method, correlation-based clustering, that consider the effects of traffic inputs on pavement design thicknesses, so that determinations of the numbers of clusters and recommendations of data levels are made objectively. New procedures developed in this research have been implemented for 22 direction-specific WIM stations in Alabama, and the recommendations of data levels for use in the MEPDG were drawn.en_US
dc.rightsEMBARGO_NOT_AUBURNen_US
dc.subjectCivil Engineeringen_US
dc.titleQuality Control, Sensitivity Analysis, and Development of Traffic Factors for Mechanistic-Empirical Pavement Designen_US
dc.typedissertationen_US
dc.embargo.lengthNO_RESTRICTIONen_US
dc.embargo.statusNOT_EMBARGOEDen_US


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