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Dynamic Modulus Characterization of Alaskan Asphalt Mixtures for Mechanistic-Empirical Pavement Design
AbstractDynamic modulus (|E*|) is one of the primary required inputs for the mechanistic-empirical pavement design of asphalt concrete (AC) pavements. Prediction of |E*| using specific models and certain inputs has been proven to provide good accuracy and is valuable for states that do not have the equipment to conduct |E*| testing and/or do not have a |E*| database. However, when |E*| prediction is needed, it is still uncertain which model and material inputs provide good-enough or best accuracy, and what the significant influencing factors are for |E*| prediction. In this study, |E*| characterization of typical Alaskan asphalt mixtures was performed. In addition, the currently existing body of knowledge on |E*| studies conducted in the United States and other countries was reviewed and evaluated. Based on this evaluation, three commonly used models were selected for further evaluation of their |E*| prediction accuracy for 34 Alaskan mixtures. Various statistics were used for data analyses; however, results of the goodness-of-fit statistics such as R2 with the reference to the line of equality were given the highest level of attention following the recommendations. It was found that using Level 3 inputs for two of the models could provide good-enough accuracy for |E*| prediction of Alaskan asphalt mixtures regardless of the influencing factors evaluated in this study. It is recommended that mixtures be grouped (e.g., hot mix asphalt, warm mix asphalt, with and without recycled asphalt pavement) if future modification of the predictive equations is needed.
Dynamic Modulus Characterization of Alaskan Asphalt Mixtures for Mechanistic-Empirical Pavement Design
AbstractDynamic modulus (|E*|) is one of the primary required inputs for the mechanistic-empirical pavement design of asphalt concrete (AC) pavements. Prediction of |E*| using specific models and certain inputs has been proven to provide good accuracy and is valuable for states that do not have the equipment to conduct |E*| testing and/or do not have a |E*| database. However, when |E*| prediction is needed, it is still uncertain which model and material inputs provide good-enough or best accuracy, and what the significant influencing factors are for |E*| prediction. In this study, |E*| characterization of typical Alaskan asphalt mixtures was performed. In addition, the currently existing body of knowledge on |E*| studies conducted in the United States and other countries was reviewed and evaluated. Based on this evaluation, three commonly used models were selected for further evaluation of their |E*| prediction accuracy for 34 Alaskan mixtures. Various statistics were used for data analyses; however, results of the goodness-of-fit statistics such as R2 with the reference to the line of equality were given the highest level of attention following the recommendations. It was found that using Level 3 inputs for two of the models could provide good-enough accuracy for |E*| prediction of Alaskan asphalt mixtures regardless of the influencing factors evaluated in this study. It is recommended that mixtures be grouped (e.g., hot mix asphalt, warm mix asphalt, with and without recycled asphalt pavement) if future modification of the predictive equations is needed.
Dynamic Modulus Characterization of Alaskan Asphalt Mixtures for Mechanistic-Empirical Pavement Design
Li, Peng (author) / Liu, Jenny / Saboundjian, Steve / Zhao, Sheng
2017
Article (Journal)
English
BKL:
56.45
Baustoffkunde
Local classification TIB:
535/6520/6525/xxxx
British Library Online Contents | 2017
|Mechanistic-empirical pavement performance of asphalt mixtures with recycled asphalt shingles
British Library Online Contents | 2018
|Mechanistic-empirical pavement performance of asphalt mixtures with recycled asphalt shingles
British Library Online Contents | 2018
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