Geotechnics

This study investigates the behaviour of dense silty sands with kaolinite clay under static drained/undrained conditions at low confining stress. Conventional laboratory tests assessed the mixtures’ physical properties, but standard void ratio methods proved inadequate for silty sands with kaolinite. Despite targeting 80% relative density, specimens exhibited loose sand behaviour in both drained and undrained tests. With increasing kaolinite content, conventionally reconstituted mixtures exhibit reduced peak stress ratios up to 10% fines, with little change beyond, while critical ratios generally rise at 25 kPa but remain unchanged or decrease slightly at 50 kPa. Analytical redefinition of minimum/maximum void ratios (based on sand–clay volumetric fractions) improved specimen reconstitution, yielding dense behaviour matching that of the host sand. The alternatively reconstituted mixtures display increasing drained peaks and minor changes in undrained peaks with increasing kaolinite content, with critical ratios increasing markedly at 25 kPa and only slightly at 50 kPa. However, this analytical void ratio determination method is limited to non-expansive, low-plasticity clays. Void ratios in silty sands with clay mineras are influenced by confining stress, drainage, saturation, clay content, and the sand skeleton structure. Unlike pure sands, these mixtures exhibit variable void ratios due to changes in the clay phase under different saturation levels. A new evaluation method is needed that accounts for clay composition, saturation-dependent consistency, and initial sand skeleton configuration to characterise these soils accurately. The findings highlight the limitations of conventional approaches and stress the need for advanced frameworks to model complex soil behaviour in geotechnical applications.

Accurate prediction of soil–structure interface shear strength (τ_max) is critical for reliable geotechnical design. This study combines experimental testing with interpretable machine learning to overcome the limitations of traditional empirical models and black-box approaches. Ninety large-displacement ring shear tests were performed on five sands and three interface materials (steel, PVC, and stone) under normal stresses of 25–100 kPa. The results showed that particle morphology, quantified by the regularity index (RI), and surface roughness (R_t) are dominant factors. Irregular grains and rougher interfaces mobilised higher τ_max through enhanced interlocking, while smoother particles reduced this benefit. Harder surfaces resisted asperity crushing and maintained higher shear strength, whereas softer materials such as PVC showed localised deformation and lower resistance. These experimental findings formed the basis for a hybrid symbolic regression framework integrating Genetic Programming (GP) with Shapley Additive Explanations (SHAP), Fourier feature augmentation, and physics-informed constraints. Compared with multiple linear regression and other hybrid GP variants, the Physics-Informed Neural Fourier GP (PIN-FGP) model achieved the best performance (R² = 0.9866, RMSE = 2.0 kPa). The outcome is a set of five interpretable and physics-consistent formulas linking measurable soil and interface properties to τ_max. The study provides both new experimental insights and transparent predictive tools, supporting safer and more defensible geotechnical design and analysis.

Accurate evaluation of subgrade behaviour under dynamic loading is essential for the long-term performance of transport infrastructure. While the Light Weight Deflectometer (LWD) is commonly used to assess subgrade stiffness, it provides only a single stiffness value and may not fully capture the time-dependent response of soil. This study presents an image-based vision system developed to monitor soil surface displacements during loading, enabling more detailed analysis of dynamic behaviour. The system incorporates high-speed cameras and MATLAB-based computer vision algorithms to track vertical movement of the plate during impact. Laboratory and field experiments were conducted to evaluate the system’s performance, with results compared directly to those from the LWD. A strong correlation was observed (R² = 0.9901), with differences between the two methods ranging from 0.8% to 13%, confirming the accuracy of the vision-based measurements despite the limited dataset. The findings highlight the system’s potential as a practical and cost-effective tool for enhancing subgrade assessment, particularly in applications requiring improved understanding of ground response under repeated or transient loading.