Modeling of Asphalt Pavement Surface Temperature for Prevention of Icing on the Surface

Abstract

Hydronic heating systems are emerging as one of the best methods, which are environmentally friendly, clean, and sustainable modern ice prevention methods, an alternative to traditional ice precautions in the pavements. In this present study, temperatures were measured on asphalt samples prepared using the hydronic heating system when the air temperature in situ fell below 0 °C. T(minute), the temperature of influent (°C), air temperature (°C), temperature of effluent (°C) and pavement mean temperature (°C) were measured for four different asphalt samples. The results of the measurements were then modeled separately for four samples (345×4=1380 data) by using multiple linear regression (MLR), multi-layer perceptron (MLP), and radial basis neural network (RBNN). The results were discussed as tables and graphs. The performances of the models were evaluated using the root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R2). According to the results, the RBNN models of four inputs had the best performance for each sample. The RBNN (4,0.6,9) model, which refers to 4-inputs, spread coefficient of 0.6 and hidden nodes of 9, of sample-3 with RMSE=0.76 °C and MAE=0.63 °C and R2=0.91 had the best performance among all models. In addition, it is thought that the models having low errors in this concept can be evaluated for early warning systems for the ice condition of the roads.