Modelling of the Luminance Coefficient in the Light Scattered by a Mineral Mixture Using Machine Learning Techniques

Abstract

The primary objective of the research and analysis reported in this article was to determine an effective method for predicting the luminance coefficient of the mineral mixture for asphalt concrete and stone mastic asphalt. The luminance of the mineral mixture determines the final luminance value of the surface. Predicting the luminance coefficient quickly will significantly improve the mineral–asphalt mix design efficiency in selecting aggregates that meet functional requirements and increase the brightness of the surface. The research process consisted of two stages. The first stage covered modelling the Qd luminance coefficient of aggregate, taking into account its petrographic analysis. The second fundamental stage, based on the research of the first stage, concerned the modelling of the luminance coefficient of the mineral mixture, taking into account the percentage share of a given component, its grain size, and its photometric properties. An effective technique of reinforced trees was used for modelling. As a result of its application, a model match to experimental data was achieved at the level of 87%. It has also been shown that the greatest impact on increasing the luminance coefficient of the mineral mixture was the use of light aggregate (quartzite sandstone or limestone) with a grain size of 2/5 in quantities > 40% or 8/11 in quantities > 60%. Furthermore, the quartzite sandstone aggregates with a grain size of 5/8 had the highest efficiency in lightening the mineral mixture. However, the use of basalt aggregates of the same fraction significantly worsened the photometric properties of the mineral mixture. An important element of the research was also to indicate that the mineralogical composition of the aggregate is crucial for an accurate assessment of its luminance coefficient.

Keywords

• luminance
• road aggregate
• machine learning
• petrography
• road pavement surface