Seepage problems in half-space domains are crucial in hydrology, environmental, and civil engineering, involving groundwater flow, pollutant transport, and structural stability. Typical examples include seepage through dam foundations, coastal aquifers, and levees under seepage forces, requiring accurate numerical modeling. However, existing methods face challenges in handling complex geometries, heterogeneous media, and anisotropic properties, particularly in multi-domain half-spaces. This study addresses these challenges by extending the modified scaled boundary finite element method (SBFEM) and using this method to explore steady seepage problems in complex half-space domain. In the modified SBFEM framework, segmented straight lines or curves, parallel to the far-field infinite boundary, are introduced as scaling lines, with a one-dimensional discretization applied to them, thereby reducing computational costs.Then the weighted residual method is applied to obtain the modified SBFEM governing equations and boundary conditions of steady-state seepage problem according to the Laplace diffusion equation and Darcy's law. Furthermore, the steady seepage matrix at infinity is obtained by solving the eigenvalue problem of Schur decomposition and then the 4th-order Runge-Kutta algorithm is used to iteratively solve until the seepage matrix at the boundary lines is reached. Comparisons between the present numerical results and solutions available in the published work have been conducted to demonstrate the efficiency and accuracy of this method. At the same time, the influences of the geometric parameters and complex half-space domain on the seepage flow characteristics in complex half-space domain are investigated in detail.

Landslides pose significant risks to human life and infrastructure, particularly in mountainous regions like Inje, South Korea. This study aims to develop detailed landslide susceptibility maps (LSMs) using statistical (i.e., Frequency Ratio (FR), Logistic Regression (LR)) models and a hybrid integrated approach. These models incorporated various factors influencing landslides, including aspect, elevation, rainfall, slope, soil depth, slope length, and landform, derived from comprehensive geospatial datasets. The FR method assesses the likelihood of landslides based on historical occurrences relative to specific factor classes, while the LR method predicts landslide susceptibility through the statistical modeling of multiple predictor variables. The results from the FR, LR, and hybrid methods showed that the cumulative area covered by high and very high landslide susceptibility zones was 13.8%, 13.0%, and 14.28%, respectively. The results were validated using Receiver Operating Characteristic (ROC) curves and the Area Under the Curve (AUC), revealing AUC values of 0.83 for FR, 0.86 for LR, and 0.864 for the hybrid method, indicating high predictive accuracy. Subsequently, we used K-mean clustering algorithms on the hybrid LSI to identify the higher LSI cluster of the region. Furthermore, sensitivity analysis based on landslide density confirmed that all methods accurately identified high-risk areas. The resulting LSMs provide critical insights for land-use planning, infrastructure development, and disaster risk management, enhancing predictive accuracy and aiding in the prevention of future landslide damage.

The soil moisture content (SMC) of moist clay directly affects the traction performance of off-road tire. This study set up a high-fidelity interaction model between off-road tire and moist clay with various moisture content, developed by coupling the finite element method (FEM) and smoothed particle hydrodynamics (SPH) algorithm. The interaction behavior between pneumatic tire and moist clay is studied. Firstly, a finite element model of tire which can characterize the complex structure and nonlinear mechanical properties is established. The Drucker-Prager (D-P) constitutive model parameters of clay with various moisture levels are calibrated by soil mechanical test. The moist clay with various moisture content is modeled through the SPH algorithm. The hybrid FEM-SPH interaction model is used to define the tire-moist clay interaction. Moreover, a traction performance test device suitable for tire-moist clay is developed to verify the accuracy of the interaction model. The influence of soil moisture content and tire operating conditions include vertical load and inflation pressure on the longitudinal traction coefficient, rolling resistance coefficient and instantaneous sinkage of tire center are quantitatively analyzed. The purpose of this study is to provide accurate tire force information under moist clay for unmanned ground vehicle (UGV), which can improve the problem of wheel instantaneous sinkage of tire center and slip under moist clay, and effectively reduce the yaw phenomenon in the path tracking process.

Stress-strain behavior of two different soil specimens subjected to cyclic compressive loading are studied herein, the goal being to present a simple dynamic uniaxial mem-modeling approach that aids physical insight and enables system identification. In this paper, mem stands for memory, i.e., hysteresis. Mem-models are hysteresis models transferred from electrical engineering using physical analogies. Connected in series, a mem-dashpot and mem-spring are employed to model inter-cycle strain ratcheting and intra-cycle gradual densification of the two soil specimens. Measured time histories of stress and strain are first decomposed so that the two modeling components, mem-dashpot and mem-spring, can be identified separately. This paper focuses on the mem-dashpot, a nonlinear generalization of a linear viscous damper. A mem-spring model is also devised built on an extended Masing model. Nonlinear dynamic simulations (with inertia) employing the identified mem-dashpot and mem-spring demonstrate how well the identified mem-models reproduce the measured early-time data (first 200 cycles of loading). Choices of state variables inherited from bond graph theory, the root of mem-models, are introduced, while MATLAB time integrators (i.e., ode solvers) are used throughout this study to explore a range of contrasting damper and spring models. Stiff solver and the state event location algorithm are employed to solve the equations of motion involving piecewise-defined restoring forces (when applicable). Computational details and results are relegated to the appendices. This is the first study to use single-degree-of-freedom (SDOF) system dynamic simulations to explore the consistency of mem-models identified from real-world data.

The joint roughness coefficient (JRC) is a key parameter in the assessment of mechanical properties and the stability of rock masses. This paper presents a novel approach to JRC evaluation using a genetic algorithm-optimized backpropagation (GA-BP) neural network. Conventional JRC evaluations have typically depended on two-dimensional (2D) and three-dimensional (3D) parameter calculation methods, which fail to fully capture the nonlinear relationship between the complex surface morphology of joints and their roughness. Our analysis from shear tests on eight different joint types revealed that the strength and failure characteristics of the joints not only exhibit directional dependence but also positively correlate with surface dip angles, heights, and back slope morphological features. Subsequently, five simple statistical parameters, i.e. average dip angle, median dip angle, average height, height coefficient of variation, and back slope feature value (K), were utilized to quantify these characteristics. For the prediction of JRC, we compiled and analyzed 105 datasets, each containing these five statistical parameters and their corresponding JRC values. A GA-BP neural network model was then constructed using this dataset, with the five morphological characteristic statistics serving as inputs and the JRC values as outputs. A comparative analysis was performed between the GA-BP neural network model, the statistical parameter method, and the fractal parameter method. This analysis confirmed that our proposed method offers higher accuracy in evaluating the roughness coefficient and shear strength of joints. (c) 2025 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Published 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/).

The soil packing, influenced by variations in grain size and the gradation pattern within the soil matrix, plays a crucial role in constituting the mechanical properties of sandy soils. However, previous modeling approaches have overlooked incorporating the full range of representative parameters to accurately predict the soaked California bearing ratio (CBRs) of sandy soils by precisely articulating soil packing in the modeling framework. This study presents an innovative artificial intelligence (AI)-based approach for modeling the CBRs of sandy soils, considering grain size variability meticulously. By synthesizing extensive data from multiple sources, i.e. extensive tailored testing program undertaking multiple tests and extant literature, various modeling techniques including genetic expression programming (GEP), multi-expression programming (MEP), support vector machine (SVM), and multi-linear regression (MLR) are utilized to develop models. The research explores two modeling strategies, namely simplified and composite, with the former incorporating only sieve analysis test parameters, while the latter includes compaction test parameters alongside sieve analysis data. The models' performance is assessed using statistical key performance indicators (KPIs). Results indicate that genetic AI-based algorithms, particularly GEP, outperform SVM and conventional regression techniques, effectively capturing complex relationships between input parameters and CBRs. Additionally, the study reveals insights into model performance concerning the number of input parameters, with GEP consistently outperforming other models. External validation and Taylor diagram analysis demonstrate the GEP models' superiority over existing literature models on an independent dataset from the literature. Parametric and sensitivity analyses highlight the intricate relationships between grain sizes and CBRs, further emphasizing GEP's efficacy in modeling such complexities. This study contributes to enhancing CBRs modeling accuracy for sandy soils, crucial for pertinent infrastructure design and construction rapidly and cost-effectively. (c) 2025 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Published 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/).

The cohesion and internal friction angle of loess are important macro-mechanical parameters for evaluating the safety and stability of engineering construction. Traditional laboratory measurement methods are time-consuming and difficult to conduct on-site. This study aims to compare the effectiveness of five Machine Learning (ML) methods, namely Random Forest (RF), Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), BPNN optimized by Particle Swarm Optimization (PSO-BPNN) and BPNN optimized by Genetic Algorithm (GA-BPNN), in predicting the macro-mechanical properties of loess. To this end, the study collected data from 89 undisturbed loess samples and 229 remolded loess samples to construct training and testing datasets, and used three correlation analysis methods to analyze the influence of physical parameters on mechanical properties. The study found that the water content has the most significant impact on the mechanical properties of loess. In terms of prediction ability, SVM performs the best among the ML methods used, and the determination coefficient for cohesion of undisturbed loess reaches 0.857. Although the training data is limited, the prediction performance of BPNN is significantly improved after being optimized by PSO or GA. The research results show that ML provides an effective way to study the complex mechanical behavior of loess.

The rapid acceleration of urbanization, combined with the proliferation of impervious surfaces and the inherently low permeability of soil layers, has worsened urban waterlogging. This study explores the layout of filter element seepage wells within a sponge city framework to enhance rainwater infiltration and reduce surface water accumulation, proposing an optimized method for determining well spacing and depth. The optimization uses a multi-objective genetic algorithm to target the construction cost, seepage velocity, total head, and pore water pressure. A combined weighting method assigns weights to each aim, while the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) determines the perfect spacing and depth. The results show that the optimal spacing and depth of the filter element seepage wells are 1.572 m and 2.794 m, respectively. Compared to the initial plan, the optimized scheme reduces construction costs by 21.31%, increases the rainwater infiltration efficiency by approximately 200%, raises the total hydraulic head by 17.23%, and decreases the pore water pressure by 5.73%. Sensitivity analysis shows that the optimized scheme remains stable across different weight combinations. This optimized layout significantly improves both the infiltration capacity and cost-effectiveness.

To ensure the sustainable development of the surrounding environment and the sustainable operation of landfills, detecting landfill leakage is of great significance. In landfills lacking a leakage monitoring system, the inability to detect and locate damaged High-Density Polyethylene (HDPE) membranes can lead to the contamination of soil and groundwater by landfill leachate. To address this issue, this study proposes a resistivity tomography inversion model based on the external-electrode power supply mode. Utilizing the resistivity difference between the leakage zone and the surrounding soil, electrodes are arranged symmetrically for both power supply and measurement. Upon applying direct current (DC) excitation, potential data are collected, with the finite volume method employed for inversion and the Gauss-Newton method integrated with an adaptive particle swarm optimization algorithm for parameter fitting. Experimental results show that the combined algorithm provides better clarity in edge recognition of low-resistance models compared with single algorithms. The maximum deviation between inferred leakage coordinates and the actual location is 10.1 cm, while the minimum deviation is 6.4 cm, satisfying engineering requirements. This method can effectively locate point sources and line sources, providing an accurate solution for subsequent leakage point filling and improving repair efficiency.

Shallow landslides are often unpredictable and seriously threaten surrounding infrastructure and the ecological environment. Traditional landslide prediction methods are time-consuming, labor-intensive, and inaccurate. Thus, there is an urgent need to enhance predictive techniques. To accurately predict the runout distance of shallow landslides, this study focuses on a shallow soil landslide in Tongnan District, Chongqing Municipality. We employ a genetic algorithm (GA) to identify the most hazardous sliding surface through multi-iteration optimization. We discretize the landslide body into slice units using the dynamic slicing method (DSM) to estimate the runout distance. The model's effectiveness is evaluated based on the relative errors between predicted and actual values, exploring the effects of soil moisture content and slice number on the kinematic model. The results show that under saturated soil conditions, the GA-identified hazardous sliding surface closely matches the actual surface, with a stability coefficient of 0.9888. As the number of slices increases, velocity fluctuations within the slices become more evident. With 100 slices, the predicted movement time of the Tongnan landslide is 12 s, and the runout distance is 5.91 m, with a relative error of about 7.45%, indicating the model's reliability. The GA-DSM method proposed in this study improves the accuracy of landslide runout prediction. It supports the setting of appropriate safety distances and the implementation of preventive engineering measures, such as the construction of retaining walls or drainage systems, to minimize the damage caused by landslides. Moreover, the method provides a comprehensive technical framework for monitoring and early warning of similar geological hazards. It can be extended and optimized for all types of landslides under different terrain and geological conditions. It also promotes landslide prediction theory, which is of high application value and significance for practical use.