论文信息 - Development of Artificial Neural Networks Based Predictive Models for Dynamic Modulus of Airfield Pavement Asphalt Mixtures

Development of Artificial Neural Networks Based Predictive Models for Dynamic Modulus of Airfield Pavement Asphalt Mixtures

As part of asphalt mix design for flexible airfield pavements, the Federal Aviation Administration (FAA) collects asphalt volumetric mixture properties and aggregate gradations. Binder properties as well as laboratory dynamic modulus |E*| measurements for asphalt mixes are performed for flexible airfield pavements research. An artificial neural networks (ANN) model was developed using collected volumetric properties, aggregate gradation, and binder properties as well as laboratory |E*| measurements from seven hot-mix asphalt (HMA) and warm mix asphalt (WMA) mixtures. ANN model predictions were compared with the modified Witczak predictive model calculations for the same mixtures, and it was found that the developed ANN model successfully predicted |E*| for airfield pavement asphalt mixtures. Disciplines Civil and Environmental Engineering | Structural Engineering | Structural Materials | Transportation Engineering Comments This proceeding is published as Kaya, Orhan, Navneet Garg, Halil Ceylan, and Sunghwan Kim. "Development of Artificial Neural Networks Based Predictive Models for Dynamic Modulus of Airfield Pavement Asphalt Mixtures." In International Conference on Transportation and Development (2018): 1-7. doi: 10.1061/ 9780784481554.001. Posted with permission. Rights Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. This conference proceeding is available at Iowa State University Digital Repository: https://lib.dr.iastate.edu/ccee_conf/92 International Conference on Transportation and Development 2018 1 © ASCE Development of Artificial Neural Networks Based Predictive Models for Dynamic Modulus of Airfield Pavement Asphalt Mixtures Orhan Kaya; Navneet Garg; Halil Ceylan; and Sunghwan Kim Dept. of Civil, Construction and Environmental Engineering, Iowa State Univ., Ames, IA. E-mail: okaya@iastate.edu FAA Airport Technology R&D Branch, ANG-E262, William J. Hughes Technical Center, Atlantic City, NJ. E-mail: navneet.garg@faa.gov Dept. of Civil, Construction, and Environmental Engineering, Iowa State Univ., Ames, IA. E-mail: hceylan@iastate.edu Dept. of Civil, Construction, and Environmental Engineering, Iowa State Univ., Ames, IA. E-mail: sunghwan@iastate.edu

Halil Ceylan | Navneet Garg | Sunghwan Kim | Orhan Kaya

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