Interface frictional anisotropy of dilative sand.

Understanding direction-dependent friction anisotropy is necessary to optimize interface shear resistance across soil-structure. Previous studies estimated interface frictional anisotropy quantitatively using contractive sands. However, no studies have explored how sand with a high dilative tendency around the structural surface affects the interface shear response. In this study, a series of interface direct shear tests are conducted with selected French standard sand and snakeskin-inspired surfaces under three vertical stresses (50, 100, and 200 kPa) and two shearing directions (cranial → caudal or caudal → cranial). First, the sand-sand test observes a higher dilative response, and a significant difference between the peak and residual friction angles (ϕpeak - ϕres = 8°) is obtained at even a lower initial relative density Dr = 40%. In addition, the interface test results show that (1) shearing against the scales (cranial shearing) mobilizes a larger shear resistance and produces a dilative response than shearing along the scales (caudal shearing), (2) a higher scale height or shorter scale length exhibits a higher dilative tendency and produces a higher interface friction angle, and (3) the interface anisotropy response is more pronounced during cranial shearing in all cases. Further analysis reveals that the interface friction angle and dilation angle are decreased with the scale geometry ratio (L/H). For L/H values between 16.67 and 60, the interface dilation angle varies between 9° and 4° for cranial first shearing and 3.9°-2.6° for caudal first shearing. However, the difference in dilation angle within the same shearing direction is less than 1°.

Estimation of interface frictional anisotropy between sand and snakeskin-inspired surfaces.

The transmission of loads across the soil-structure mobilizes direction-dependent shear resistance, which can be selectively used to design geo-structures. A previous study confirmed the frictional anisotropy induced by the interface between the soil and snakeskin-inspired surfaces. However, it is necessary to estimate the interface friction angle quantitatively. In this study, a conventional direct shear apparatus is modified, and 45 cases are performed in two-way shearing directions between bio-inspired surfaces and Jumunjin standard sand under three vertical stresses (50, 100, and 200 kPa). The results show that: (1) shearing against the scales (cranial shearing) mobilizes larger shear resistance and produces a dilative response than shearing along the scales (caudal shearing) and (2) higher scale height or shorter scale length exhibits dilative tendency and produces higher interface friction angle. Further analysis is conducted to capture the frictional anisotropy as a function of the scale geometry ratio, which reveals that the interface anisotropy response is more pronounced during cranial shearing in all the cases, and the difference in the interface friction angle for the caudal → cranial test is higher than that for the cranial → caudal test at the given scale ratio.

A robust prediction model for evaluation of plastic limit based on sieve # 200 passing material using gene expression programming.

This study aims to propose a novel and high-accuracy prediction model of plastic limit (PL) based on soil particles passing through sieve # 200 (0.075 mm) using gene expression programming (GEP). PL is used for the classification of fine-grained soils which are particles passing from sieve # 200. However, it is conventionally evaluated using sieve # 40 passing material. According to literature, PL should be determined using sieve # 200 passing material. Although, PL200 is considered the accurate representation of plasticity of soil, its' determination in laboratory is time consuming and difficult task. Additionally, it is influenced by clay and silt content along with sand particles. Thus, artificial intelligence-based techniques are considered viable solution to propose the prediction model which can incorporate multiple influencing parameters. In this regard, the laboratory experimental data was utilized to develop prediction model for PL200 using gene expression programming considering sand, clay, silt and PL using sieve 40 material (PL40) as input parameters. The prediction model was validated through multiple statistical checks such as correlation coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and relatively squared error (RSE). The sensitivity and parametric studies were also performed to further justify the accuracy and reliability of the proposed model. The results show that the model meets all of the criteria and can be used in the field.