Storage stability of SBS/sulfur modified bitumens at high temperature: influence of bitumen composition and structure

Polymer modified bitumens, PMBs, are usually prepared at high temperature and subsequently stored for a period of time, also at high temperature. The stability of PMBs, in these conditions, has a decisive influence in order to obtain the adequate performances for practical applications. In this article the attention is focused in the analysis of the factors that determine the stability of styrene–butadiene–styrene copolymer (SBS)/sulfur modified bitumens when the mixtures are maintained at high temperature. Bitumens from different crude oil sources were used to prepare SBS/sulfur modified bitumens. Changes in the values of viscosity, softening point, as well as in the morphology of PMB samples, stored at 160 °C, were related to the bitumen chemical composition and to the amount of asphaltene micelles present in the neat bitumen used in their preparation El trabajo se centra en el estudio de la influencia de la estructura /composición del betún sobre la compatibilidad del sistema betún/SBS. Cuatro betunes provenientes de dos crudos distintos se seleccionaron y sus mezclas se utilizaron para preparar betunes modificados con contenidos de SBS del 3% en peso

Multi-dimensional classification with super-classes

The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.