Neural Networks Applications in Pavement Engineering: A Recent Survey

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Bayrak, Mustafa
Gopalakrishnan, Kasthurirangan
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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.

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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  • 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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The use of neural networks (NNs) has increased tremendously in several areas of engineering over the last three decades. This paper is intended to provide a state-of-the-art survey of NN applications in pavement engineering over the last three decades. The reported studies are briefly summarized under eight different categories: (1) prediction of pavement condition and performance, (2) pavement management and maintenance strategies, (3) pavement distress forecasting, (4) structural evaluation of pavement systems, (5) pavement image analysis and classification, (6) pavement materials modeling, and (7) other miscellaneous transportation infrastructure applications. To maintain consistency, the review was primarily based on archival journal publications although novel application-oriented NN implementations published in peer-reviewed conference proceedings and edited books were also considered. Recent publications focusing on the development and use of hybrid neural systems in pavement engineering were also included in the review. The increasing number of publications in this area of research in combination with other soft computing techniques every year definitely indicates that more and more students, researchers, and practitioners are interested in exploring the use of NNs in the study of pavement engineering problems.


This is an article from International Journal of Pavement Research and Technology 7 (2014): 434, doi: 10.6135/ Posted with permission.

Wed Jan 01 00:00:00 UTC 2014