Neural network models for analysis and prediction of raveling

Author

Miradi, M.

Author_Institution

Delft Univ. of Technol.

Volume

2

fYear

2004

fDate

1-3 Dec. 2004

Firstpage

1226

Lastpage

1231

Abstract

The most unacceptable damage on porous asphalt is raveling. Therefore it is important to predict when porous asphalt will achieve a critical level of raveling. In this paper artificial neural network (ANN) was employed to predict raveling having input parameters related to time-series data of raveling, climate, construction and traffic factors obtained from SHRP-NL database. For raveling low, moderate and high correlation factors were R = 0.986, R² = 0.926 and R² = 0,976. Another ANN model provided sensitivity analysis indicating relative contribution percentage of input parameters. Finally another model analyzed the relationship between materials and raveling. ANN proved to be powerful technique to predict and analyze raveling

Keywords

asphalt; civil engineering computing; neural nets; sensitivity analysis; time series; SHRP-NL database; artificial neural network; porous asphalt; raveling prediction; sensitivity analysis; time-series data; Artificial neural networks; Asphalt; Biological neural networks; Biological system modeling; Building materials; Databases; Neural networks; Predictive models; Roads; Telecommunication traffic;

fLanguage

English

Publisher

ieee

Conference_Titel

Cybernetics and Intelligent Systems, 2004 IEEE Conference on

Conference_Location

Singapore

Print_ISBN

0-7803-8643-4

Type

conf

DOI

10.1109/ICCIS.2004.1460766

Filename

1460766