Prediction of the Pavement Serviceability Ratio of Rigid Highway Pavements by Artificial Neural Networks

Abstract

The term ‘‘present serviceability’’ was adopted to represent the momentary ability of pavement to serve traffic, and the performance of the pavement was represented by its serviceability history in conjunction with its load application history. Serviceability was found to be influenced by longitudinal and transverse profile as well as the extent of cracking and patching. The amount of weight to assign to each element in the determination of the over-all serviceability is a matter of subjective opinion. In this study, the present serviceability index of rigid highway pavements has been predicted by an artificial neural network (ANN) model. For this model, the 49 experimental data obtained from AASHTO include slope variance, faulting, cracking, spalling and patching. The developed ANN model has a higher regression value than the AASHO model. This approach can be easily and realistically performed to solve the problems which do not have a formulation or function for the solution.

Keywords

• Artificial Neural Networks, Pavement, Rigid Pavements, Serviceability

Prediction of the Pavement Serviceability Ratio of Rigid Highway Pavements by Artificial Neural Networks

Abstract

Keywords

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References

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