Application of Artificial neural network technique for prediction of pavement roughness as a performance indicator

One of the most important and widely accepted pavement performance and ride quality indicators is the International Roughness Index (IRI). This study investigates the combined effect of pavement distress on flexible pavement performance in two climate regions (wet freeze and wet freeze) in the U.S. and Canada. The long-term pavement performance (LTPP) database was used to obtain pavement distress data. Data from forty-three of the LTPP pavement sections (333 observations) with no previous maintenance were collected. The proposed models predict the IRI as a function of pavement distress variables, namely the pavement age, rutting, fatigue cracking, block cracking, longitudinal cracking, transverse cracking, potholes, patching, bleeding, and ravelling. After the data were collected, modelling was conducted to predict IRI using two techniques: multiple linear regression (MLR) and artificial neural network (ANN). The coefficient of determination ($R^2$), root mean squared error (RMSE) and mean absolute error (MAE) were used to examine the performance of the two techniques adopted in this study. The models' results revealed that both ANN and MLR models could predict IRI with good accuracy. The MLR models yielded the $R^2$ values of 77.7% and 89.3%, whereas the ANN models resulted in the $R^2$ values of 99.1% and 97.5% for wet freeze and wet no freeze climate regions, respectively. As a result, ANN models are more accurate and efficient than MLR models.