ROUGHNESS PREDICTION MODEL BASED ON THE ARTIFICIAL NEURAL NETWORK APPROACH

Prediction of the future roughness of a flexible pavement section is important in programming rehabilitation needs. This study utilizes the Artificial Neural Network (ANN) approach to predict the future International Roughness Index (IRI) year by year up to a maximum pavement age of 20 years. The ANN program requires key information about a pavement section including the current IRI, pavement design, climate, traffic and other variables to predict yearly IRI values into the future. The program was developed initially using synthetic data to reproduce a well defined function and then utilized data from the Long-Term Pavement Performance (LTPP) program database to calibrate the ANN. Accuracy of the predictions was tested using LTPP sections not used in the development and was found to be reasonable for programming purposes. The ANN was developed as a Microsoft Windows95 based software tool and can be used in routine pavement management programming