The ability to predict the soil mechanical parameters swiftly is critical for off-road vehicle mobility. This paper introduces a novel interpretation methodology for determining critical soil mechanical parameters by impact penetration tests, enabling rapid and remote assessment of terramechanics properties. Initially, the method employs the Mohr-Coulomb constitutive model and the Coupled Eulerian-Lagrangian (CEL) finite element method to generate a dataset of soil impact penetration resistance and acceleration responses. Subsequently, a Radial Basis Function (RBF) neural network is employed as a surrogate model and integrated with the Nondominated Sorting Genetic Algorithm II (NSGA-II) to accurately interpret parameters such as density, cohesion, internal friction angle, elastic modulus, and Poisson's ratio. Experimental validation using sand and silty clay from Yangbaijing, Tibet, confirmed the accuracy and robustness of the method. The results indicate that the mean absolute percentage error for interpreted values was below 25%, with relative errors for some key parameters even below 10%. Furthermore, each single-condition calculation was completed on a standard computer in less than one minute. Comparative analyses with other algorithms, including MIGA and POS, demonstrated the superior performance of NSGA-II in avoiding local optima. The proposed interpretation framework offers a rapid, reliable, and remote solution for identifying the soil mechanical properties. Its potential applications range from disaster mitigation and emergency response operations to extraterrestrial soil exploration and other scenarios where in-situ investigations are challenging.

The aperture of natural rock fractures significantly affects the deformation and strength properties of rock masses, as well as the hydrodynamic properties of fractured rock masses. The conventional measurement methods are inadequate for collecting data on high-steep rock slopes in complex mountainous regions. This study establishes a high -resolution three-dimensional model of a rock slope using unmanned aerial vehicle (UAV) multi-angle nap-of-the-object photogrammetry to obtain edge feature points of fractures. Fracture opening morphology is characterized using coordinate projection and transformation. Fracture central axis is determined using vertical measuring lines, allowing for the interpretation of aperture of adaptive fracture shape. The feasibility and reliability of the new method are verified at a construction site of a railway in southeast Tibet, China. The study shows that the fracture aperture has a significant interval effect and size effect. The optimal sampling length for fractures is approximately 0.5-1 m, and the optimal aperture interpretation results can be achieved when the measuring line spacing is 1% of the sampling length. Tensile fractures in the study area generally have larger apertures than shear fractures, and their tendency to increase with slope height is also greater than that of shear fractures. The aperture of tensile fractures is generally positively correlated with their trace length, while the correlation between the aperture of shear fractures and their trace length appears to be weak. Fractures of different orientations exhibit certain differences in their distribution of aperture, but generally follow the forms of normal, log -normal, and gamma distributions. This study provides essential data support for rock and slope stability evaluation, which is of significant practical importance. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).