As a common molecule in biomineralization, L-aspartic acid (L-Asp) has been proven to be able to induce in vitro CaCO₃ precipitation, but its application in sand reinforcement has never been studied. In this study, L-Asp was employed in sand reinforcement for the first time through the newly developed biomimetic carbonate precipitation (BCP) technique. Specimens with different number of BCP spray cycles were prepared, and a series of direct shear tests were conducted to investigate the impact of spray number on shear strength, critical displacement, and residual strength. Then a simplified power model for shear stress–displacement behavior was established and calibrated with the measured data. The results show that BCP can significantly improve the shear strength of sand. As the number of spray cycles increases, both the shear strength and residual strength increase, while the critical displacement decreases. Such variations can be described with two sigmoid models and a linear model, respectively. The simplified power model performs well in most cases, especially at higher spray numbers. This study is expected to provide a practical model for the shear behavior of BCP-treated mortar.

The utility of geotechnical centrifuge tests depends on how correctly they predict the physical and mechanical behaviour of concrete. In this study, a model concrete material that consisted of α-gypsum plaster, fine silica sand, and water was developed. An orthogonal test design was used to evaluate the effect of the mix proportion on the model concrete performance. The physical (i.e., flowability and bleeding rate) and mechanical (i.e., compressive and flexural strength) characteristics were considered as indices. Various mix ratios resulted in remarkable relative contributions to model concrete performance, and each raw material dosage exhibited positive or negative synergy. The water–plaster ratio (W/P) and aggregate–plaster ratio (A/P) strongly influenced the mechanical and physical characteristics, respectively. Multiple linear regression analysis (MLRA) was carried out to determine a forecast model for various small-scale test demands. Finally, the applicability and outlines of the presented forecasting method in proportioning design were evaluated by typical use of model concrete in small-scale model tests.