Campus Units

Civil, Construction and Environmental Engineering, Center for Nondestructive Evaluation (CNDE), Institute for Transportation

Document Type

Conference Proceeding

Conference

International Conference on Transportation and Development 2018

Publication Version

Published Version

Publication Date

7-11-2018

Journal or Book Title

International Conference on Transportation and Development

First Page

1

Last Page

7

Research Focus Area

Structural Engineering, Transportation Engineering

DOI

10.1061/9780784481554.001

Conference Date

July 15–18, 2018

City

Pittsburgh, PA

Abstract

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.

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.

Language

en

File Format

application/pdf

Share

Article Location

 
COinS