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 optimization of geotextile mechanical properties is crucial for enhancing their performance in civil engineering applications such as soil reinforcement and stabilization. This study focuses on the influence of manufacturing parameters on the static puncture (CBR) properties of polyester geotextiles. Polyester geotextile samples were manufactured using various parameters, including needle-punching density, penetration depth, calendering temperature, and speed. The mechanical properties of the samples, specifically strength and elongation, were evaluated using the CBR test according to EN ISO 12236. The data were analyzed using multivariate analysis of variance, followed by statistical analysis to determine the influence of the manufacturing parameters on the mechanical properties. Furthermore, the relationship between these parameters and the mechanical properties was modeled using artificial neural networks (ANN) and regression analysis. The results indicated that all manufacturing parameters significantly impacted the strength and elongation of the geotextiles. The ANN models, employing two hidden layers, predicted the strength and elongation with errors of 1.43% and 1.26%, respectively.