Use of a machine learning-based framework to approximate the input features of an intrinsic cohesive zone model of recycled asphalt mixes tested at low temperatures

Abstract

Although the cohesive zone model (CZM) provides numerous advancements in simulating the crack initiation and evolution in asphalt mixes, its efficiency and applicability are still challenging. This is because asphalt mixes are principally assumed to be homogeneous in CZM modeling despite intrinsic heterogeneity. Therefore, it is essential to calibrate the CZM model, by adjusting the input factors, aiming at alleviating this inconsistency between the model and reality. Accordingly, this research was aimed at presenting a model to approximate the calibrated input features as a function of a wide variety in testing conditions and mix criteria. To this end, an experimental dataset was collected by investigating the fracture mechanic responses of various recycled warm asphalt mixtures (prepared using three categories using organic and chemical materials, and using foam bitumen technology). Mixes were consisted of virgin aggregates and those containing up to 70% recycled asphalt pavement particles (i.e. 0, 30, 50, and 70%). Mixes were subjected to freeze and thaw cycles (i.e. 0, 1, and 3 cycles) and semi-circular bending test was conducted at subzero temperatures (i.e. 0, −10, and −20℃). Experimental results were analyzed using machine learning (ML) algorithms including random forest, gradient boosting regression, and multi-output regression methods. The results proved the efficiency of the random forest regression model in predicting the required input factors of CZM model. The ML model was also validated by predicting the input factors (of CZM model) for another fracture dataset, demonstrating that this ML model can be promisingly used as an approximation tool.