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. (10th April 2023)

• Record Type: Journal Article
• Authors: Mohsen Motevalizadeh, Seyed; Kavussi, Amir; Mollenhauer, Konrad; Vuye, Cedric; Hasheminejad, Navid
• Is Part Of: Construction & building materials. Volume 373(2023)
• Publication Date: 2023-04-10
• Subjects: Warm mix asphalt -- Recycled asphalt pavement -- Cohesive zone model -- Machine learning -- Random forest algorithm -- Multi-output regression modeling; Building materials -- Periodicals; 624.18
• DOI: 10.1016/j.conbuildmat.2023.130870
• Languages: English
• ISSNs: 0950-0618

Abstract

Graphical abstract: Highlights: An intrinsic cohesive zone model was generated to simulate the cracking behavior of recycled warm mix asphalt. Importance of mix components and experimental variables were investigated on the formation of cohesive zone model. The relationship between the cohesive zone input factors and the experimental variables was studied through the machine learning approach. A machine learning-based model (using random forest algorithm) was generated aimed at approximating the cohesive zone input factors. 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.