Analytical Approach to Predict Temperature Profile in a Multilayered Pavement System Based on Measured Surface Temperature Data

This paper presents an algorithm to predict one-dimensional (1D) temperature profiles in a multilayered pavement system on the basis of measured surface temperature data. The model inputs are pavement layer thicknesses, thermal conductivity and diffusivity of layer materials, average initial pavement temperatures along pavement depths, and measured pavement surface temperature data. The main mathematical tools employed in deriving the analytical solution of pavement layer temperature predictions are the Laplace transform and numerical inverse Laplace transform. Measured in situ temperature data from a two-layer flexible pavement system demonstrate that the derived analytical solution generates reasonable temperature profiles in the asphalt concrete layer. The main advantages of the proposed algorithm are that it can rapidly predict the pavement temperature profile when the thermal conductivity and diffusivity values of the layer material are selected and the surface temperature data are measured at end points of each equally spaced time interval. Climatic data, such as air temperature, solar radiation intensity, and wind speed, are not needed to implement this algorithm. This algorithm can be applied to assist field engineers in estimating temperature profiles in a multilayered pavement system for the period during which falling weight deflectometer (FWD) tests are performed.