Spatial distribution characteristics and mechanical properties of artificial coarse aggregates with different morphologies in asphalt mixtures
Source abstract
Abstract
While the morphological characteristics of aggregates are known to significantly influence asphalt mixture performance, current methods lack systematic approaches for controlling and evaluating these effects. To address this gap, an innovative framework was developed using cement-based grouting to fabricate artificial coarse aggregates with controlled morphologies, enabling quantitative analysis of their spatial distribution characteristics and mechanical properties in asphalt mixtures. Industrial CT scanning was employed to examine the internal components of asphalt mixture samples with four different types of artificial coarse aggregates. A method to assess the spatial distribution uniformity of the aggregates was proposed, and a mesoscopic 3D model of the mixture was developed. Uniaxial compression tests revealed that, compared to 3D printing methods, cement-based aggregates with varied shapes exhibited pointedness and flakiness indices similar to natural aggregates, ensuring a close match in shape and angularity . Although the surface fractal dimension was slightly lower (2–5 %) than that of natural gravel, it was significantly better than 3D-printed resin aggregates. The mechanical properties of cement-based aggregates, such as apparent density and abrasion resistance , closely matched those of natural aggregates. The three-parameter Weibull model was effective in describing the distribution uniformity of aggregates, with ideal uniformity observed when the pointedness index reached 1.0. These findings highlight the advantages of cement-based aggregates in enhancing asphalt mixture performance.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Asphalt Pavement Performance Evaluation · Innovative concrete reinforcement materials · Concrete and Cement Materials Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (53)
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