Acharjee, P. K., & Souliman, M. (2022, March), Development of Dynamic Modulus Predictive Model Using Artificial Neural Network (ANN)  Paper presented at 2022 ASEE Gulf Southwest Annual Conference, Prairie View, Texas. 10.18260/1-2--39173

\bibitem{asee_peer_39173}
  Acharjee, P. K., \& Souliman, M. (2022, March), \emph{Development of Dynamic Modulus Predictive Model Using Artificial Neural Network (ANN)}
  Paper presented at 2022 ASEE Gulf Southwest Annual Conference, Prairie View, Texas.
  10.18260/1-2--39173

Prashanta Kumar Acharjee and Mena Souliman.  "Development of Dynamic Modulus Predictive Model Using Artificial Neural Network (ANN)".  2022 ASEE Gulf Southwest Annual Conference, Prairie View, Texas, 2022, March.  ASEE Conferences, 2022.  https://peer.asee.org/39173 Internet.  18 Apr, 2024

\bibitem{asee_peer_39173}
  Prashanta Kumar Acharjee and Mena Souliman.
  "Development of Dynamic Modulus Predictive Model Using Artificial Neural Network (ANN)".
  \emph{2022 ASEE Gulf Southwest Annual Conference, Prairie View, Texas, 2022, March}.
  ASEE Conferences, 2022.
  https://peer.asee.org/39173 Internet.  18 Apr, 2024

@INPROCEEDINGS{asee_peer_39173
author = "Prashanta Kumar Acharjee and Mena Souliman"
title = "Development of Dynamic Modulus Predictive Model Using Artificial Neural Network (ANN)"
booktitle = "2022 ASEE Gulf Southwest Annual Conference"
year = "2022"
month = "March"
address = "Prairie View, Texas"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/39173}
number = {10.18260/1-2--39173}
}

TY  - CPAPER
AB  - In Mechanistic-empirical Pavement Design Guide (MEPDG), dynamic modulus |E*| is identified as a key property for Hot Mix Asphalt (HMA). Determining |E*| in the laboratory requires several days of sophisticated testing procedures and expensive instruments. To bypass the long testing time, sophisticated testing procedure, and expense, several multivariate regression analysis-based models have been developed to predict the dynamic modulus from simpler materials properties and volumetrics. Witczak 1999 and Modified Witczak 2006 are the two most widely used dynamic modulus predictive models in the asphalt community. There are several other regression-based models that have been developed earlier such as the Hirsch Model, the Law of Mixtures Parallel Model, and the Resilient Modulus-based Model. Using Artificial Neural Network (ANN) a |E*| prediction model is developed in this study. To train the ANN model a dataset with 7400 data points is used, which is the same dataset used in the Modified Witczak 2006 model development. The overall R-value for the ANN model is very promising and is better than other regression-based models. An equation is also extracted from the ANN model. This equation can be used for quick prediction of |E*| without performing any sophisticated test.
AU  - Prashanta Kumar Acharjee
AU  - Mena Souliman
CY  - Prairie View, Texas
DA  - 2022/03/16
PB  - ASEE Conferences
TI  - Development of Dynamic Modulus Predictive Model Using Artificial Neural Network (ANN)
UR  - https://peer.asee.org/39173
DO  - 10.18260/1-2--39173
ER  -