Prediction of Thermal Cracks in Pavements Using Artificial Neural Network Modeling

Thermal cracks are considered one of the most prevalent and critical forms of pavement distress. While recent studies have proven that the regression method to explain thermal cracks is not an accurate representation to quantify distress, linear models are still commonly used in engineering practices. Using long-term pavement performance (LTPP) data from 15 different road sections located in the Midwest region of the US, an artificial neural network (ANN) model was developed to predict the count of thermal cracks given the extracted input parameters: average annual temperature, annual average freeze index, 18 Kip ESAL, thermal conductivity, heat capacity, surface shortwave absorption, and coefficient of thermal contraction. The results for 7-9-9-1 ANN structure with TANSIG-LOGSIG transfer functions generated the closest thermal cracking estimate with root mean square error (RMSE) of 0.089, mean absolute percentage error (MAPE) of 0.10, and a regression coefficient (R) of 0.94, which confirmed that the model was adequate to predict thermal cracks in the pavement.