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

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2018-07-11
Authors
Kaya, Orhan
Garg, Navneet
Kim, Sunghwan
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Ceylan, Halil
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Civil, Construction and Environmental Engineering

The Department of Civil, Construction, and Environmental Engineering seeks to apply knowledge of the laws, forces, and materials of nature to the construction, planning, design, and maintenance of public and private facilities. The Civil Engineering option focuses on transportation systems, bridges, roads, water systems and dams, pollution control, etc. The Construction Engineering option focuses on construction project engineering, design, management, etc.

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The Department of Civil Engineering was founded in 1889. In 1987 it changed its name to the Department of Civil and Construction Engineering. In 2003 it changed its name to the Department of Civil, Construction and Environmental Engineering.

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1889-present

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  • Department of Civil Engineering (1889-1987)
  • Department of Civil and Construction Engineering (1987-2003)
  • Department of Civil, Construction and Environmental Engineering (2003–present)

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Institute for Transportation
InTrans administers 14 centers and programs, and several other distinct research specialties, and a variety of technology transfer and professional education initiatives. More than 100 Iowa State University faculty and staff work at InTrans, and from 200 to 250 student assistants from several ISU departments conduct research while working closely with university faculty. InTrans began in 1983 as a technical assistance program for Iowa’s rural transportation agencies.
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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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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.

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