Local calibration of the mechanistic empirical pavement design guide for Kansas

dc.contributor.authorSufian, Abu Ahmed
dc.date.accessioned2016-11-18T18:07:18Z
dc.date.available2016-11-18T18:07:18Z
dc.date.graduationmonthDecemberen_US
dc.date.issued2016-12-01en_US
dc.date.published2016en_US
dc.description.abstractThe Kansas Department of Transportation is transitioning from adherence to the 1993 American Association of State Highway and Transportation Officials (AASHTO) Pavement Design Guide to implementation of the new AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) for flexible and rigid pavement design. This study was initiated to calibrate MEPDG distress models for Kansas. Twenty-seven newly constructed projects were selected for flexible pavement distress model calibration, 21 of which were used for calibration and six that were selected for validation. In addition, 22 newly constructed jointed plain concrete pavements (JPCPs) were selected to calibrate rigid models; 17 of those projects were selected for calibration and five were selected for validation. AASHTOWare Pavement ME Design (ver. 2.2) software was used for design analysis, and the traditional split sampling method was followed in calibration. MEPDG-predicted distresses of Kansas road segments were compared with those from Pavement Management Information System data. Statistical analysis was performed using the Microsoft Excel statistical toolbox. The rutting and roughness models for flexible pavement were successfully calibrated with reduced bias and accepted null hypothesis. Calibration of the top-down fatigue cracking model was not satisfactory due to variability in measured data, and the bottom-up fatigue cracking model was not calibrated because measured data was unavailable. AASHTOWare software did not predict transverse cracking for any projects with global values. Thus thermal cracking model was not calibrated. The JPCP transverse joint faulting model was calibrated using sensitivity analysis and iterative runs of AASHTOWare to determine optimal coefficients that minimize bias. The IRI model was calibrated using the generalized reduced gradient nonlinear optimization technique in Microsoft Excel Solver. The transverse slab cracking model could not be calibrated due to lack of measured cracking data.en_US
dc.description.advisorMustaque A. Hossainen_US
dc.description.degreeMaster of Scienceen_US
dc.description.departmentDepartment of Civil Engineeringen_US
dc.description.levelMastersen_US
dc.description.sponsorshipKansas Department of Transportation (KDOT)en_US
dc.identifier.urihttp://hdl.handle.net/2097/34533
dc.language.isoen_USen_US
dc.publisherKansas State Universityen
dc.subjectCalibrationen_US
dc.subjectMEPDG
dc.subjectPavement
dc.subjectDesign
dc.subjectKansas
dc.titleLocal calibration of the mechanistic empirical pavement design guide for Kansasen_US
dc.typeThesisen_US

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