Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Calibration and sensitivity analysis of rut prediction model for semi-rigid pavement using AASHTOWare ME design
Rutting is one of the most serious distresses of semi-rigid asphalt pavements in Jiangsu. In this research, AASHTOWare pavement ME Design software was used for rutting prediction of semi-rigid pavements. A semi-rigid pavement is composed of a flexible layer and a rigid layer. Local calibration was done first by taking into account local materials, traffic information, and environmental conditions. A total of eight sites representing typical mixture types of Jiangsu province were selected to complete the calibration and another four highways were chosen to make the validation. Then the calibrated software was used to identify sensitive factors for rutting prediction. Critical inputs needed for the calibration and sensitive analysis were tested or collected from local project-level Pavement Management System (PMS) database. The modulus of semi-rigid base was back-calculated from deflection basin data. Calibration coefficients for each typical structure and the optimal coefficients for this region were identified, respectively. Results also demonstrate that traffic inputs and thickness of Hot Mixed Asphalt (HMA) layers are more sensitive to the predicted rutting depth than void contents of HMA mixtures.
Calibration and sensitivity analysis of rut prediction model for semi-rigid pavement using AASHTOWare ME design
Rutting is one of the most serious distresses of semi-rigid asphalt pavements in Jiangsu. In this research, AASHTOWare pavement ME Design software was used for rutting prediction of semi-rigid pavements. A semi-rigid pavement is composed of a flexible layer and a rigid layer. Local calibration was done first by taking into account local materials, traffic information, and environmental conditions. A total of eight sites representing typical mixture types of Jiangsu province were selected to complete the calibration and another four highways were chosen to make the validation. Then the calibrated software was used to identify sensitive factors for rutting prediction. Critical inputs needed for the calibration and sensitive analysis were tested or collected from local project-level Pavement Management System (PMS) database. The modulus of semi-rigid base was back-calculated from deflection basin data. Calibration coefficients for each typical structure and the optimal coefficients for this region were identified, respectively. Results also demonstrate that traffic inputs and thickness of Hot Mixed Asphalt (HMA) layers are more sensitive to the predicted rutting depth than void contents of HMA mixtures.
Calibration and sensitivity analysis of rut prediction model for semi-rigid pavement using AASHTOWare ME design
Zhu, Yuqin (Autor:in) / Ni, Fujian (Autor:in) / Li, Hongmei (Autor:in)
Road Materials and Pavement Design ; 18 ; 23-32
10.07.2017
10 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
| Taylor & Francis Verlag | 2024
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