Experimental-cum-Numerical approach for thermal conductivity determination of asphalt paving mixture and its constituents

Abstract

Numerical determination of the thermal conductivity kmix of an asphalt paving mixture offers an attractive alternative to time-consuming laboratory testing. A major shortcoming of all existing numerical and empirical method of kmix determination is the need to input accurate known values of thermal conductivity kair, kasp and kagg of air, asphalt and aggregates respectively. To overcome this weakness, this research developed a one-off laboratory experimental procedure to determine kmix, without having to input measured values of kair, kasp and kagg. The proposed procedure employed an efficient row-column finite element (FE) model of asphalt mixture and a back-calculation analysis to numerically calculate kmix from assumed values of kair, kasp and kagg. The values of kair, kasp and kagg were progressively improved using the genetic-algorithm (GA) optimization technique until the calculated kmix matched closely with the kmix value measured experimentally. The proposed method was validated using two series of experimental data: one from an experimental study reported in the literature, and another from laboratory tests conducted in this study involving asphalt mixtures of different air voids contents.