Predicting Pavement Performance Utilizing Artificial Neural Network (ANN)

Pavement management systems (PMS) play a significant role in cost-effective management of highway networks to optimize pavement performance over predicted service life of the pavements. Although, the Iowa DOT manages three primary highway systems (i.e., Interstate, US, and Iowa highways) that represent 8% (approximately 9,000 miles) of the total roadway system in the state (114,000 miles), these systems carry around 62% of the total vehicle miles traveled (VMT) and 92% of the total large truck VMT. In this research, historical climate data was acquired from relevant agencies and integrated with pavement condition data to include all related variables in prediction modeling. An artificial neural network (ANN) model was used to predict the performance of ride, cracking, rutting, and faulting indices on different pavement types. The goodness of fit of the ANN prediction models was compared with the conventional multiple linear regression (MLR) models. The results show that ANN models are more accurate in predicting future conditions than MLR models. The contribution of input variables in prediction models were also estimated. The results indicated that climate factors directly influenced the pavement conditions, and ANN model results can be used by the decision makers to establish appropriate management actions to withstand harsh weather over the years.