The introduction of new road pavement materials causes the need to verify whether the existing pavement design methods enable correct incorporation of their properties. In the case of asphalt pavements, the origins of contemporary methods may be traced back to the mid-20th century, when solely unmodified binders were used. The introduction of highly SBS-modified binders in 2009 significantly changed the behaviour of the asphalt mixtures and the entire pavement structure. Asphalt courses are now characterised by very high flexibility, elasticity and fatigue resistance, with simultaneous high resistance to rutting. The aim of the article is to present the effect of the use of asphalt mixtures with HiMA (Highly Modified Asphalt) binders in different variants of flexible pavement structures – including one, two or three courses containing HiMA. Fatigue life calculations were performed using the “Similarity Method”, which enables estimation of the fatigue life of the structure based on its relationship with the results of laboratory fatigue tests. The layer system with HiMA in the asphalt base course proved the most advantageous, combining excellent fatigue properties of the mixture containing HiMA with greater stiffness of the wearing and binder courses containing classic binders. The other aspect taken into account in the calculations was the effect of changing the mixture in the asphalt base course from AC 22 to AC 16. This change proved advantageous in all the analysed structures. The deflections and critical strains decreased, while pavement life, determined by fatigue and permanent deformation criteria, increased.

Creep compliance of the hot-mix asphalt (HMA) is a primary input of the current pavement thermal cracking prediction model used in the US. This paper discusses a process of training an Artificial Neural Network (ANN) to correlate the creep compliance values obtained from the Indirect Tension (IDT) with similar values obtained on small HMA beams from the Bending Beam Rheometer (BBR). In addition, ANNs are also trained to predict HMA creep compliance from the creep compliance of asphalt binder and vice versa using the BBR setup. All trained ANNs exhibited a very high correlation of 97 to 99 percent between predicted and measured values. The binder creep compliance functions built on the ANN-predicted discrete values also exhibited a good correlation when compared with the laboratory experiments. However, the simulation of trained ANNs on the independent dataset produced a significant deviation from the measured values which was most likely caused by the differences in material composition, such as aggregate type and gradation, presence of recycled additives, and binder type.