Modelling the Indirect Tensile Strength of Asphalt Mixtures with the Use of Artificial Intelligence
Abstract
The authors estimate the indirect tensile strength of asphalt mixtures containing recycled asphalt and foamed bitumen using linear regression and neural network models. By comparing the random forest and the neural network model, the applicability of machine learning techniques in this field was proven. In the course of the research work, three models were developed, which are able with a high R2 value to predict the relationship between the ITS (wet and dry) value and two factors affecting it, namely the foamed bitumen content and the Reclaimed Asphalt Pavement (%).
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