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A platform for research: civil engineering, architecture and urbanism
International Roughness Index Prediction for Rigid Pavements: An Artificial Neural Network Application
International 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: Se/Sy =0.414) compared to the MEPDG model (R2 = 0.584, Se/Sy =0.643). In addition, the bias in the predicted IRI values using the ANN model was significantly lower compared to the MEPDG regression model.
International Roughness Index Prediction for Rigid Pavements: An Artificial Neural Network Application
International 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: Se/Sy =0.414) compared to the MEPDG model (R2 = 0.584, Se/Sy =0.643). In addition, the bias in the predicted IRI values using the ANN model was significantly lower compared to the MEPDG regression model.
International Roughness Index Prediction for Rigid Pavements: An Artificial Neural Network Application
Abd El-Hakim, Ragaa (author) / El-Badawy, Sherif (author)
2013
7 Seiten
Conference paper
English
| British Library Conference Proceedings | 2013
Artificial neural network modelling to predict international roughness index of rigid pavements
| Springer Verlag | 2020