Over the past 40+ years, a computerized climatic model has evolved that can accurately predict the temperature in pavement systems based on atmospheric weather data inputs and pavement materials. This model is the Enhanced Integrated Climatic Model (EICM). The EICM started as the University of Illinois Heat Transfer Model in 1969 and has undergone continuous improvement, allowing it to be used with current pavement materials and structure data, historical weather data, and forecast atmospheric weather data to forecast pavement temperature and moisture conditions. When used as a real?time monitoring tool, the EICM can evaluate the probability of icing conditions at the pavement surface. The five weather?related parameters that are required to run the EICM are air temperature, wind speed, percentage of sunshine, precipitation, and relative humidity. These inputs are used to estimate the heat transfer between the road and the atmosphere. Using the EICM allows pavement surface conditions to be monitored on a frequent basis, identifying times of high probability of surface ice formation. The EICM was used to model measured pavement temperatures. The objective of this project was to evaluate the use of the EICM for determining pavement surface temperature for winter maintenance operations. Modeling the pavement temperatures could provide virtual RWIS data at a cost that is considerably less than the cost of physical sensors and systems. The study involved collecting pavement material information, modeling the pavements over the past 5 years at these locations with actual atmospheric weather data, and evaluating the difference between actual and EICM modeled pavement temperatures. Comparisons between the measured and predicted pavement temperature were made by collecting measured pavement temperatures and pavement structure information for various sites across the State of Illinois. A total of 38 sites was selected, which consisted of 25 Illinois Department of Transportation (IDOT) sites, 11 Illinois Tollway sites, and two McHenry County sites. The temperature data were compared using the Pavement ME default parameters for thermal conductivity, heat capacity, and absorptivity. Additionally, the model was calibrated for each site by adjusting the parameters discussed above. Two different climate datasets were used to model the predicted pavement temperatures. Both climate datasets showed very good results when comparing the measured and predicted pavement temperatures (R2 > 0.8). The average mean temperature difference among all sites was 2.5°F for all temperatures and 1.1°F for both ±10°F and ±5°F from freezing (32°F). The calibration process was completed by running a design matrix of different thermal conductivity, heat capacity, and absorptivity values. The matrix consisted of 27 total combinations for both flexible and rigid pavements. The EICM was executed for each combination, and the root mean square error (RMSE) and mean error were determined. The top ten combinations based on the ±5°F RMSE were used to determine the new recommended values for Illinois pavements. Based on the results, the recommended values for thermal conductivity, heat capacity, and absorptivity of pavements with a PCC surface were 1.5, 0.3, and 0.85, respectively. The values were 1.0, 0.25, and 0.85 for flexible and composite pavements. Using the recommended thermal inputs and the actual in?place pavement structure (material types and layer thicknesses) as inputs to the EICM yields reasonable model accuracy with a mean error that is generally less than 2°F. Understanding that the model error is a function of the quality of the weather data, the quality of the sensor data, and the validity of the model, an error of less than 2°F is considered reasonable and is appropriate for use in a virtual RWIS. (Author/publisher)
Samenvatting