Development of a New Revised Version of the Witczak E* Predictive Model for Hot Mix Asphalt Mixtures (With Discussion)
Javed Bari, Matthew W. Witczak
Abstract
Javed Bari, Matthew W. Witczak
Abstract
The main purpose of this paper is to present the development of a new revised version of the widely known Witczak E* Predictive Model. The model development was aimed at overcoming the limitations of current models available to the pavement community, which are used for predicting the dynamic modulus (E*) of hot mix asphalt mixtures (HMA). A comprehensive study was completed at Arizona State University to conduct numerous E* testing and finalize a huge E* database, containing 7400 data points from 346 HMA mixtures. This database was used to develop the new E* predictive model. The model is capable of accurately estimating changes in E* of HMA mixture as a function of changes in mixture volumetrics, material properties, temperature and loading frequency (or time) for the entire E* database used. The model has been found to be rational, unbiased, accurate, and statistically sound. The new mechanistic-empirical pavement design guide entitled “Guide for Mechanistic- Empirical Design of New and Rehabilitated Pavement Structures” developed under National Cooperative Highway Research Program (NCHRP) Project 1-37A uses the current version of Witczak E* Predictive Model in its input levels 2 and 3. It is hypothesized that due to its similar sigmoidal structure as used in the guide, the newly developed E* model can be easily incorporated in a future revision of this pavement design guide.
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The main purpose of this paper is to present the development of a new revised version of the widely known Witczak E* Predictive Model. The model development was aimed at overcoming the limitations of current models available to the pavement community, which are used for predicting the dynamic modulus (E*) of hot mix asphalt mixtures (HMA). A comprehensive study was completed at Arizona State University to conduct numerous E* testing and finalize a huge E* database, containing 7400 data points from 346 HMA mixtures. This database was used to develop the new E* predictive model. The model is capable of accurately estimating changes in E* of HMA mixture as a function of changes in mixture volumetrics, material properties, temperature and loading frequency (or time) for the entire E* database used. The model has been found to be rational, unbiased, accurate, and statistically sound. The new mechanistic-empirical pavement design guide entitled “Guide for Mechanistic- Empirical Design of New and Rehabilitated Pavement Structures” developed under National Cooperative Highway Research Program (NCHRP) Project 1-37A uses the current version of Witczak E* Predictive Model in its input levels 2 and 3. It is hypothesized that due to its similar sigmoidal structure as used in the guide, the newly developed E* model can be easily incorporated in a future revision of this pavement design guide.
Key concepts: Asphalt pavement, Asphalt, Computer science, Empirical research, Data mining, Engineering, Mathematics, Statistics