International Roughness Index Prediction for Rigid Pavements: An Artificial Neural Network Application

Ragaa Abd El-Hakim; Sherif El-Badawy

doi:10.4028/www.scientific.net/AMR.723.854

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 723International Roughness Index Prediction for Rigid...

International Roughness Index Prediction for Rigid Pavements: An Artificial Neural Network Application

Abstract:

nternational Roughness Index (IRI) is an important parameter that indicates the ride quality and pavement condition. In this study, an Artificial Neural Network (ANN) model was developed to predict the IRI for Jointed Plain Concrete Pavement (JPCP) sections. The inputs for this model are: initial IRI value, pavement age, transverse cracking, percent joints spalled, flexible and rigid patching areas, total joint faulting, freezing index, and percent subgrade passing No. 200 U.S. sieve. This data was obtained from the Long Term Pavement Performance (LTPP) Program. It is the same data and inputs used for the development of the Mechanistic-Empirical pavement Design Guide (MEPDG) IRI model for JPCP. The data includes a total of 184 IRI measurements. The results of this study shows that using the same input variables, the ANN model yielded a higher prediction accuracy (coeficint of determination: R² = 0.828, and ratio of standard error of estimate (predicted) to standard deviation of the measured IRI values: S_e/S_y =0.414) compared to the MEPDG model (R² = 0.584, S_e/S_y =0.643). In addition, the bias in the predicted IRI values using the ANN model was significantly lower compared to the MEPDG regression model.

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Periodical:

Advanced Materials Research (Volume 723)

Pages:

854-860

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.723.854

Citation:

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Online since:

August 2013

Authors:

Ragaa Abd El-Hakim, Sherif El-Badawy

Keywords:

Artificial Neural Network (ANN), Faulting, IRI, JPCP, Transverse Cracks

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