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The oscillation of asphalt mix composition on a daily basis significantly 10 affects the achieved properties of the asphalt during production, thus resulting in 11 conducting expensive laboratory tests in order to determine existing properties and 12 predicting the future results. In order to decrease the amount of such tests, a 13 development of artificial neural network (ANN) and multiple linear regression (MLR) 14 models in the prediction process of predetermined dependent variables air void and 15 soluble binder content is presented. The input data were obtained from a single 16 laboratory and consists of testing 386 mixes of hot mix asphalt (HMA). It was found 17 that it is possible and desirable to apply such models in the prediction process of the 18 HMA properties. The final aim of the research was to compare results of the prediction 19 models on an independent dataset and analyse them through the boundary conditions of 20 technical regulations and the standard EN 13108-21. 21

Development of artificial neural network and multiple linear regression models in the prediction process of the hot mix asphalt properties