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.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784481554
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Supplemental Notes:
- © 2018 American Society of Civil Engineers.
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Authors:
- Kaya, Orhan
- Garg, Navneet
- Ceylan, Halil
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0000-0003-1133-0366
- Kim, Sunghwan
- Publication Date: 2018-7
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 1-7
Subject/Index Terms
- TRT Terms: Airport runways; Asphalt pavements; Dynamic modulus of elasticity; Flexible pavements; Mix design; Neural networks; Predictive models
- Subject Areas: Design; Highways; Pavements; Terminals and Facilities;
Filing Info
- Accession Number: 01867712
- Record Type: Publication
- ISBN: 9780784481554
- Files: TRIS, ASCE
- Created Date: Dec 16 2022 9:41AM