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 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.

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.

Accurate inversion of geotechnical parameters is essential for assessing foundation-bearing capacity and stability, which directly impact structural safety and serviceability. Accurate prediction of load settlement behavior is crucial to prevent overdesign and underperformance, ensuring that foundations support anticipated loads without excessive deformation or failure. This paper presents an integrated optimization system combining ABAQUS (2022), Python (PyCharm21.3.3), and MATLAB (2022b) software, based on the Duncan-Chang (DC) model, for inversion of key geotechnical parameters. The ABAQUS UMAT subroutine customizes the DC model, facilitating its application in finite element simulations for soil-structure interaction analysis. To improve the optimization process, an adaptive genetic algorithm that dynamically adjusts crossover and mutation rates, thereby improving solution searches and parameter space exploration, is implemented. Key parameters of the DC model-the initial tangent stiffness (K) and nonlinear deformation characteristics (n) of soil-are inverted. The accuracy of this inversion is validated through comparisons with experimental pressure-settlement curves obtained from indoor bearing plate tests. Therefore, this optimization system effectively integrates intelligent algorithms with finite element analysis, serving as a reliable tool for precise geotechnical parameter inversion, with potential for improving foundation design accuracy, optimizing soil-structure interaction predictions, and improving the overall stability and safety of geotechnical structures.

Voronoi tessellations are a mathematical concept that appears in many examples in nature, such as the skin of giraffes, dry soil, and vegetable cells. In the context of biomimicry, these tessellations have been used to build impressive structures worldwide that are both aesthetically pleasing and structurally efficient. This paper proposes a methodology based on genetic algorithms (GA) to determine the structural topology of Voronoi flat roofs with tubular steel cross sections and a given boundary. The design variables correspond to the number and position of the Voronoi centers that form the tessellations within the roof, as well as the dimensions of the structural elements. This representation of the design variables creates an unstructured optimization problem. Such characteristic is addressed by an implicit redundant representation of possible solutions, which generates chromosomes with varying numbers of variables. The objective function relates to the weight of the roof, considering constraints raised in technical and constructive issues. The methodology was applied to four different roof boundaries: triangular, pentagonal, square, and rhombic. In general, the results provide optimal aesthetic solutions with a few Voronoi tessellations, based on the algorithm configuration and the multimodal nature of the search space. Convergence analysis indicates the possibility of the algorithm getting stuck in an optimum local and shows the progressive reduction of Voronoi centers. Lastly, it is observed that the maximum displacement constraint leads to the shape of the optimal roof.

The hardening soil model with small-strain stiffness (HSS model) is widely applied in deep foundation pit engineering in coastal soft-soil areas, yet it is characterized by a multitude of parameters that are relatively cumbersome to acquire. In this study, we incorporate a genetic algorithm and a back-propagation neural network (BPNN) model into an inversion analysis for HSS model parameters, with the objective of facilitating a more streamlined and accurate determination of these parameters in practical engineering. Utilizing horizontal displacement monitoring data from retaining structures, combined with local engineering, both a BPNN model and a BPNN optimized by a genetic algorithm (GA-BPNN) model were established to invert the stiffness modulus parameters of the HSS model for typical strata. Subsequently, numerical simulations were conducted based on the inverted parameters to analyze the deformation characteristics of the retaining structures. The performances of the BPNN and GA-BPNN models were evaluated using statistical metrics, including R2, MAE, MSE, WI, VAF, RAE, RRSE, and MAPE. The results demonstrate that the GA-BPNN model achieves significantly lower prediction errors, higher fitting accuracy, and predictive performance compared to the BPNN model. Based on the parameters inverted by the GA-BPNN model, the average compression modulus Es1-2, the reference tangent stiffness modulus Eoedref, the reference secant stiffness modulus E50ref, and the reference unloading-reloading stiffness modulus Eurref for gravelly cohesive soil were determined as Eoedref=0.83Es1-2 and Eurref=8.14E50ref; for fully weathered granite, Eoedref=1.54Es1-2 and Eurref=5.51E50ref. Numerical simulations conducted with these stiffness modulus parameters show excellent agreement with monitoring data, effectively describing the deformation characteristics of the retaining structures. In situations where relevant mechanical tests are unavailable, the application of the GA-BPNN model for the inversion analysis of HSS model parameters is both rational and effective, offering a reference for similar engineering projects.

Despite extensive research on slope seepage mechanisms, a reliable long-term prediction method for slope deformation considering rainfall remains undeveloped, largely due to the complexity of rainfall-induced slope instability. This study leverages a project in slope engineering to explore slope deformation under heavy rainfall using intelligent monitoring techniques and genetic algorithm (GA) optimization for neural network prediction. By analyzing slope deformation patterns under varied rainfall intensities, results reveal that limited rainfall has minimal impact on slope stability, whereas excessive rainfall disrupts internal seepage patterns, increasing pore water pressure and reducing soil shear strength, it thereby enhances the risk of slope instability and potential landslides, significantly impacting slope stability. The GA-optimized network accurately captures abrupt slope deformation stages, avoids local optima, and provides a viable framework for early warning of slope instability.

Aiming to address the problem of selecting the parameters of a soil constitutive model in the calculation of foundation pit stability, this paper proposes a hybrid genetic differential evolution algorithm (GADE) which performs by jumping out of local optima with fast convergence based on the hybrid optimization algorithm strategy and compares the advantages and disadvantages of genetic algorithms (GAs) and differential evolution algorithms (DEs). Three typical test functions were used to evaluate the search efficiency and convergence speed of GAs, DEs, and GADE, respectively. It was found that GADE has the fastest convergence speed and can search for the global optimal solution to the problem, which highlights its excellent optimization performance. At the same time, taking the Shimao Binjiang deep foundation pit as an example, GADE was used to invert the soil modulus parameters of a CX1 measuring point and construct a finite-element model for calculation. The results showed that the simulated calculation curve and the measured displacement curve were in good agreement and the curve fitting reached 95.05%, indicating the applicability and feasibility of applying GADE to identify soil parameters.

To reduce the potential threat of soil loss due to ephemeral gullies, it is crucial to adopt Best Management Practices (BMPs) that prevent damage to landscapes by reducing sediments load. The current research evaluated the impact of five BMPs, including cover crops, grassed waterways, no-till, conservation tillage, and riparian buffer strips for reduction of sediment load from sheet/rill, and ephemeral gully erosion in an agricultural watershed in Southern Ontario, Canada. The study aimed to automatically calibrate AnnAGNPS using genetic algorithm and the most sensitive parameters of the model identified using a combination of Latin Hypercube Sampling (LHS) and One-At-a-Time (OAT) approach. It also utilized the calibrated model to simulate the effectiveness of BMPs in reducing the average seasonal and annual sediment loads from both sources of erosion (sheet/rill, and ephemeral gully) to determine the most effective practices. Riparian buffer strips were consistently successful in decreasing average seasonal sediment load of sheet/rill erosion, with an average reduction efficiency of 72 % in Spring, 64 % in Summer, 65 % in Fall, and 76 % in Winter. In terms of reducing average seasonal sediment load from ephemeral gully erosion, grassed waterways proved to be the most effective BMPs. They showed efficiency of 90 % in Spring; 83 % in Summer; 79 % in Fall; and 75 % in Winter. Considering the average annual sediment load, riparian buffer strips were consistently successful in decreasing average annual sediment load of sheet/rill erosion, with 69% reduction efficiency. Similarly, grassed waterways were the most effective BMPs for reducing average annual sediment load of ephemeral gully erosion, with an efficiency of 81 %. Additionally, grassed waterways were found to be the most efficient BMPs for reducing average annual total sediment load with reduction efficiency of 71 %. These results demonstrate the importance of implementing effective BMPs to address ephemeral gully erosion in watersheds where ephemeral gullies are the main source of erosion.