Enhancing sulfate resistance of cement-stabilized recycled aggregate with steel slag: Optimized mix design and mechanistic insights
Source abstract
Abstract
Cement-stabilized recycled aggregate (CSR) has garnered considerable attention as a sustainable material in pavement construction. However, its performance significantly deteriorates under sulfate attack. This study aims to enhance the sulfate resistance of CSR by incorporating steel slag aggregate (SSA) and optimizing the mix design using the D-optimal mixture design (DOD) method. Dosage ranges for recycled aggregate (RA) and SSA were determined by unconfined compressive strength (UCS) and thermal shrinkage tests. The optimal mix proportion of cement-stabilized RA and SSA (CSRS) was established using the DOD model. X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy-energy dispersive spectroscopy (SEM–EDS) were employed to investigate the mechanism by which SSA enhances the sulfate resistance of CSR. The results indicated that the replacement ratios of RA and SSA for natural aggregate (NA) should be limited to 60% and 36%, respectively. The optimal CSRS mix proportion determined by the DOD model comprised 40% NA, 48.5% RA, and 17% SSA. Experimental validation demonstrated that all absolute relative deviations (ARD) were below 5%, verifying the accuracy of the DOD model. The reactive minerals in SSA, primarily C 2 S and C 3 S, participated in hydration reactions, leading to the formation of additional C-S-H gel, which refined the interfacial transition zone (ITZ), reduced its width to 27 μm, limited sulfate ingress pathways, and mitigated sulfate attack on CSRS. These findings provide valuable insights for optimizing similar mixture designs and promoting RA utilization in pavement base applications.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Recycled Aggregate Concrete Performance · Innovative concrete reinforcement materials · Concrete and Cement Materials Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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