The oscillation of asphalt mix composition on a daily basis significantly affects the achieved properties of the asphalt during production, thus resulting in conducting expensive laboratory tests to determine existing properties and predicting the future results. To decrease the amount of such tests, a development of artificial neural network and multiple linear regression models in the prediction process of predetermined dependent variables air void and soluble binder content is presented. The input data were obtained from a single laboratory and consists of testing 386 mixes of hot mix asphalt (HMA). It was found that it is possible and desirable to apply such models in the prediction process of the HMA properties. The final aim of the research was to compare results of the prediction models on an independent dataset and analyze them through the boundary conditions of technical regulations and the standard EN 13108-21.
The maintenance planning within the urban road infrastructure management is a complex problem from both the management and technoeconomic aspects. The focus of this research is on decision-making processes related to the planning phase during management of urban road infrastructure projects. The goal of this research is to design and develop an ANN model in order to achieve a successful prediction of road deterioration as a tool for maintenance planning activities. Such a model is part of the proposed decision support concept for urban road infrastructure management and a decision support tool in planning activities. The input data were obtained from Circly 6.0 Pavement Design Software and used to determine the stress values (560 testing combinations). It was found that it is possible and desirable to apply such a model in the decision support concept in order to improve urban road infrastructure maintenance planning processes.
In this paper, we studied the properties of hot mix asphalt with substituted waste glass, used for surface layers, made in accordance with standard EN 13108-1. The waste glass was sourced by overcrushing glass from old bottles and broken glass from window frames. We aimed to substitute fractions of individual and cumulative aggregate and fillers in the mineral mixture with glass. We also studied how the compaction in the Marshall's tamper affected the overcrushing of the mineral mixture with the waste glass. Increasing the glass fraction in the asphalt mixture decreased the density, stability, and void content of the mixture, as well as the proportion of voids filled with bitumen.
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