Applied Sciences

The permeability characteristics of expansive soil have a significant impact on slope stability. This study investigates the permeability evolution of three expansive soils (B, GW, GB) with distinct swelling potentials (68%, 42%, and 28%) under repeated wet–dry cycles, combining laboratory falling-head tests and in situ four-ring infiltrometer measurements. The results demonstrate that the permeability coefficient increases consistently with cycle number. The high-expansivity soil (B) exhibited the most pronounced change, with permeability increasing from 10⁻⁶ cm/s to 10⁻³ cm/s after five cycles, whereas the low-expansivity soil (GB) remained stable. A significant specimen size effect was observed in initial permeability, which stabilized beyond a critical infiltration area of 1102.7 cm². However, this size dependence diminished after three cycles due to extensive cracking and structural homogenization. Crack density analysis further confirmed microstructural stabilization at sample diameters between 32 and 38 cm, validating the adopted in situ seepage diameter of 37 cm. Linear regression revealed greater discrepancies between laboratory and in situ permeability values in high-swelling soils. Empirical formulas incorporating wet–dry cycles were developed to accurately predict in situ permeability, providing practical tools for engineering design and risk assessment.

This study investigates water–air coupled transport characteristics during ponded water infiltration in unsaturated sand columns through systematic laboratory experiments. The experiments considered three soil textures, two initial dry densities (1.50 and 1.60 g/cm³), and four initial saturations (0% to 41%), with synchronous monitoring of pore pressure and volumetric water content using pressure sensors (P1–P7) and moisture sensors (W1–W5) to track dynamic changes in wetting front, pressure, and saturation. The results reveal four distinct stages of pore pressure variation during ponded water infiltration: pressure soars (Stage I), pressure ascends with air compression (Stage II), pressure surges due to air breakthrough (Stage III), and pressure stabilization (Stage IV). The duration, intensity of these stages, and wetting front migration rates are significantly influenced by soil texture, initial dry density, and initial saturation. Specifically, lower dry density and clay content shorten the time for the wetting front to reach the column bottom, while higher initial saturation promotes entrapped air bubble breakthrough, triggering Stage III. This study enhances understanding of water–air coupled transport in unsaturated sandy soils, providing insights for optimizing irrigation and soil-water conservation strategies.

In the context of rapidly growing logistics demand, traditional warehouse management methods are inadequate in meeting contemporary efficiency and accuracy requirements. The present study proposes the development of an intelligent warehouse visualization platform, the objective of which is to address issues such as high labor dependency, opaque inventory, and operational inefficiencies. The construction of a virtual warehouse environment was undertaken using Unity3D, with the aim of simulating real-world zones. These comprised storage areas, automatic guided vehicle (AGV) pathways, and operational spaces. The platform incorporates radio frequency identification devices (RFID) for item tracking and a role-based access system, enabling real-time monitoring and management of inbound, inventory, and outbound processes. In order to optimize AGV path planning, the proposed algorithm incorporates a dynamic weighted heuristic, a five-neighborhood search, a bidirectional search, and Bézier curve-based smoothing. The efficacy of these enhancements has been demonstrated through a reduction in searched nodes, computation time, and path length, while simultaneously enhancing smoothness. As demonstrated by simulations conducted in Unity3D, the optimized algorithm exhibits a reduction in search nodes of 59.19%, in time of 45.41%, and in path length of 18%, in comparison with the conventional A-star algorithm. The platform offers a safe, efficient, and scalable solution for enterprise training and operational simulation, contributing valuable insights for intelligent warehouse upgrading.