Models Development for Asphalt Pavement Performance Index in Different Climate Regions Using Soft Computing Techniques

Abstract

The Pavement Condition Index (PCI) is one of the most critical pavement performance indicators and ride quality. This study aims to develop PCI models based on pavement distress parameters using conventional technique and artificial neural network (ANN) technique across two climate regions in the U.S. and Canada. The long-term pavement performance (LTPP) database was used to obtain pavement distress data, including pavement age, rutting, fatigue cracking, block cracking, longitudinal cracking, transverse cracking, potholes, patching, bleeding, and ravelling, as input variables for predicting PCI. Forty-three flexible pavement segments were considered with 333 observations. The type, severity, and extent of surface damage and the PCI were determined for each pavement segment. Two modelling techniques were conducted to predict the PCI, namely, multiple linear regression (MLR) and artificial neural network (ANN). The coefficient of determination (R2), 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 determined that both ANN and MLR models could predict PCI with high accuracy; ANN models were more accurate and efficient. ANN provided the highest accuracy in predicting PCI of pavement for wet and wet no-freeze climate regions, with R2 values of 99.8%, 98.3 %: RMSE values of 0.44%, 1.413%, and MAE values of 0.44%, 1.022%, respectively. Whereas in the MLR method, R2 values of 86.8% and 89.4%: RMSE values of 7.195%, 7.324%, and MAE values of 5.616%, 5.79% for wet and wet no freeze climate regions, respectively.

Keywords

• artificial neural network
• pavement distresses
• pavement condition index (pci)
• long-term pavement performance (ltpp)