Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Calibration of Pavement ME Design and Mechanistic-Empirical Pavement Design Guide Performance Prediction Models for Iowa Pavement Systems
The AASHTO mechanistic-empirical pavement design guide (MEPDG) pavement performance models and the associated AASHTOWare pavement ME design software are nationally calibrated using design inputs and distress data largely from the national long-term pavement performance (LTPP). Further calibration and validation studies are necessary for local highway agencies’ implementation by taking into account local materials, traffic information, and environmental conditions. This study aims to improve the accuracy of MEPDG/pavement ME design pavement performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 70 sites from Iowa representing both jointed plain concrete pavements (JPCPs) and hot mix asphalt (HMA) pavements were selected. The accuracy of the nationally calibrated MEPDG prediction models for Iowa conditions was evaluated. The local calibration factors of MEPDG performance prediction models were identified using both linear and nonlinear optimization approaches. Local calibration of the MEPDG performance prediction models seems to have improved the accuracy of JPCP performance predictions and HMA rutting predictions. A comparison of MEPDG predictions was also performed between two software programs to assess if the local calibration coefficients determined from one software program is acceptable with the use of another software program, which has not been addressed before. Few differences are observed between one software program and MEPDG predictions with nationally and locally calibrated models for: (1) faulting and transverse cracking predictions for JPCP; and (2) rutting, alligator cracking, and smoothness predictions for HMA. With the use of locally calibrated JPCP smoothness (IRI) prediction model for Iowa conditions, the prediction differences between the two software programs are reduced. Finally, recommendations are presented on the use of identified local calibration coefficients with the two software programs for Iowa pavement systems.
Calibration of Pavement ME Design and Mechanistic-Empirical Pavement Design Guide Performance Prediction Models for Iowa Pavement Systems
The AASHTO mechanistic-empirical pavement design guide (MEPDG) pavement performance models and the associated AASHTOWare pavement ME design software are nationally calibrated using design inputs and distress data largely from the national long-term pavement performance (LTPP). Further calibration and validation studies are necessary for local highway agencies’ implementation by taking into account local materials, traffic information, and environmental conditions. This study aims to improve the accuracy of MEPDG/pavement ME design pavement performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 70 sites from Iowa representing both jointed plain concrete pavements (JPCPs) and hot mix asphalt (HMA) pavements were selected. The accuracy of the nationally calibrated MEPDG prediction models for Iowa conditions was evaluated. The local calibration factors of MEPDG performance prediction models were identified using both linear and nonlinear optimization approaches. Local calibration of the MEPDG performance prediction models seems to have improved the accuracy of JPCP performance predictions and HMA rutting predictions. A comparison of MEPDG predictions was also performed between two software programs to assess if the local calibration coefficients determined from one software program is acceptable with the use of another software program, which has not been addressed before. Few differences are observed between one software program and MEPDG predictions with nationally and locally calibrated models for: (1) faulting and transverse cracking predictions for JPCP; and (2) rutting, alligator cracking, and smoothness predictions for HMA. With the use of locally calibrated JPCP smoothness (IRI) prediction model for Iowa conditions, the prediction differences between the two software programs are reduced. Finally, recommendations are presented on the use of identified local calibration coefficients with the two software programs for Iowa pavement systems.
Calibration of Pavement ME Design and Mechanistic-Empirical Pavement Design Guide Performance Prediction Models for Iowa Pavement Systems
Kim, Sunghwan (Autor:in) / Ceylan, Halil (Autor:in) / Ma, Di (Autor:in) / Gopalakrishnan, Kasthurirangan (Autor:in)
02.07.2014
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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