Integrated Experimental-Numerical Approach for Estimating Asphalt Mixture Induction Healing Level through Discrete Element Modeling of a Single-Edge Notched Beam Test

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Department of Civil, Environmental, and Geospatial Engineering


The induction healing effects have been experimentally investigated through the cyclic beam fracture and healing testing of asphalt mixture samples with conductive steel wool fibers. This study aims to investigate the healing level of asphalt mixture through the integrated work of laboratory experiment and numerical simulation. Nine single-edge notched beam (SENB) samples containing steel fibers were prepared and used for the laboratory fracture test. Afterward, three divided groups of fractured samples were subjected to the induction healing at controlled temperatures of 60°C, 80°C, and 100°C, respectively. Then the reloading fracture test was conducted with three groups of samples to measure the fracture strength recovery ratio (FSRR) after induction-healing process, which is defined as the ratio of the recovered peak load to the original peak load. The discrete element method (DEM) was utilized to simulate the beam fracture process. A fracture toughness-based bond healing model was used to simulate the microcrack healing effect. The reloading experimental results (after the first healing process) showed that the average FSRR values of sample groups healed at 60°C, 80°C, and 100°C were 0.825, 0.965, and 0.900, respectively. The simulated fracture test results using DEM were in good agreement with the experimental results with respect of the peak load and the slope of load increase. A highly linear correlation between the FSRR and the bond healing level were found based on the numerical simulation and the experimental results. Based on the correlation, the actual bond healing levels of the samples healed at 60°C, 80°C, and 100°C were obtained as 76.2%, 92.1%, and 84.7%, respectively. This study provides a microstructural DEM fracture-healing model to predict the healing level of asphalt mixture by comparing it with experimental data.

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Journal of Materials in Civil Engineering