No abstract

In this work, the physical and chemical changes of alkali-activated slag (AAS) after a prolonged drying treatment under various relative humidity (RH) conditions were studied. The results show that the shrinkage kinetics of AAS is strongly dependent on the RH, but irrespective of the moisture loss. At high RH, AAS exhibits a pronounced viscous characteristic, which is likely due to the rearrangement and reorganization of calcium-alumina-silicate-hydrate (C-A-S-H). Meantime, micropore closure, silicate polymerization, and new interlayer formation were observed during microstructure rearrangement, indicating a strong interaction between adjacent C-A-S-H particles. The large shrinkage in AAS may be attributed to the structural incorporation of alkali cations in C-A-S-H, which reduces the stacking regularity of C-A-S-H layers and makes the C-A-S-H easier to collapse and redistribute upon drying.

In this work, drying shrinkage of four alkali-activated slag (AAS) mortars, prepared using various types/dosages of activator, was characterized at four different levels of relative humidity (RH) and two drying regimes (i.e. direct and step-wise drying). The results show that drying shrinkage values of AAS are significantly dependent on the drying rate, as AAS shrinks more when the RH is decreased gradually instead of directly. At high RH, the drying shrinkage of AAS exhibits a considerable visco-elastic/visco-plastic behavior, in comparison to ordinary portland cement (OPC). It is concluded that the cause of high-magnitude shrinkage in AAS mortar is due to the high visco-elastic/visco-plastic compliance (low creep modulus) of its solid skeleton. Furthermore, the activator affects the shrinkage behaviors of AAS by influencing the pore structure and mechanical properties.

Concrete, as the most widely used construction material, is inextricably connected with human development. Despite conceptual and methodological progress in concrete science, concrete formulation for target properties remains a challenging task due to the ever-increasing complexity of cementitious systems. With the ability to tackle complex tasks autonomously, machine learning (ML) has demonstrated its transformative potential in concrete research. Given the rapid adoption of ML for concrete mixture design, there is a need to understand methodological limitations and formulate best practices in this emerging computational field. Here, we review the areas in which ML has positively impacted concrete science, followed by a comprehensive discussion of the implementation, application, and interpretation of ML algorithms. We conclude by outlining future directions for the concrete community to fully exploit the capabilities of ML models.

No abstract