Modeling Road Pavement Rutting Using Artificial Neural Network and Conventional Measurements

Abstract

Rutting leads hydroplaning, accidents, poor riding quality, and significant maintenance costs. This study assists the development of statistical and Artificial pavement rutting models. The proposed methodology is reliable, time-saving, cost-saving, and comfortable. The suggested technique to anticipate rutting considers traffic volumes, pavement, and geometrical parameters such as lane and shoulder widths. This research modeled 33 main highways' ruts. Most of these roads have serious de-stressing problems with rutted pavement. The developed rutting prediction models demonstrated a medium to high correlation between rut depth and independent variables including annual average daily traffic, truck fleet percentage, pavement thickness, and number of lanes. The correlation coefficients such as R2 were found to be moderate for most of the developed models. The linear models of rutting prediction were statistically significant, with R2 values averaging around 66%, whereas the logistic regression model was the best developed rutting model, with an R2 value of 67%, when all variables, including traffic, pavement, and geometry, were considered. Nonlinear models with an R2 value of 57% were used to get similar findings. The artificial neural network (ANN) has been used in this study to model rut depth with same independent variables and gave higher results with R2 value of 82%. The findings showed that an ANN outperformed regression modeling in predicting the depth of a rut.