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This paper explores the uplift behavior of horizontal anchor plates in frictional-cohesive soils subjected to seismic loading. The study focuses on three seismic stability factors: F c , F q , and F γ , which depend on seismic coefficient K h , depth-to-width ratio H / B , and soil internal friction angle φ. To evaluate ultimate uplift load capacity while considering the influence of K h , H / B , and φ , Terzaghi's principle of superposition is introduced. Robust predictive models for these stability factors are developed using advanced computational techniques like finite element limit analysis (FELA) and Levenberg-Marquardt backpropagation neural networks (LMBPNN). These neural network models are meticulously optimized, resulting in high R-squared values (R2) and low root mean square errors (RMSE) for both training and testing datasets. Sensitivity analysis and feature importance assessments on the three stability factors indicate that the design parameter H / B has the largest SHAP value, that is followed by φ and K h. The findings significantly enhance our understanding of uplift behavior in frictional-cohesive soils under seismic loading conditions, as well as providing precise and reliable design of anchor plate systems in diverse soil environments exposed to seismic forces. • Innovative seismic uplift analysis combining stability factors and machine learning. • Meticulously curated dataset for robust and reliable engineering insights. • Practical implications for marine structures' stability and seismic safety. • Rigorous comparative analysis with existing studies validates our findings. • Interdisciplinary contribution at the intersection of marine and geotechnical engineering. [ABSTRACT FROM AUTHOR]

Estimating active earth pressure in cohesionless backfill behind rigid retaining walls has been significantly improved through the application of cutting-edge soft computing techniques in this paper. This study delves into the intricate interplay of negative wall-soil friction with load transfer mechanisms, underpinned by a comprehensive dataset obtained from a conservative solution that leverages statically admissible stress fields. Utilizing a Bayesian regularization backpropagation neural network, we construct an explicit function for active earth pressure estimation, ensuring the model's reliability through its remarkable alignment with measured values. Furthermore, feature importance analysis and advanced mathematical modeling enrich the study, providing a practical tool for retaining wall design and analysis. This tool is adept at addressing complex scenarios, including those involving negative wall-soil friction, thereby advancing the state-of-the-art in geotechnical engineering. [ABSTRACT FROM AUTHOR]

This paper presents rigorous plasticity solutions to the classical active and passive earth pressure problems. The primary method adopted is a conventional equation based on the soil property and three stability factors (F c , F s, and F γ), that are analogous to Terzaghi's bearing capacity factors (N c , N s , and N γ) of strip footings. The proposed method is considered as a practical yet smart approach to the determination of earth pressures and is one of the main novelties in this study. To achieve this, the finite element limit analysis (FELA) of upper and lower bounds solutions, a powerful tool in studying the behaviour of soil stability, is employed to compute the three stability factors for a wide range of soil internal friction angles, wall roughness, and surcharge pressures. In addition, analytical solutions based on statistically admissible stress fields are adopted to validate the proposed FELA solutions. The validation satisfactorily demonstrated the accuracy and reliability of the proposed solutions. It follows that the numerical results are presented in the form of tables, figures, and several examples provided to demonstrate the practical use of the three factors in estimating active and passive earth pressures with various backfill soils, wall roughness, and surcharge pressure. The study should, therefore, be of value to practitioners. [ABSTRACT FROM AUTHOR]

The study presents an innovative hybrid machine learning model tailored for predicting the load-displacement characteristics of bored piles, specifically those integral to high-rise buildings. Incorporating critical design parameters—diameter, length, Standard Penetration Test (SPT) indices, and effective overburden pressure—the model leverages a dataset of 1650 samples from static load tests in Vietnam. This hybrid approach integrates the Three Modified Symbiotic Organisms Search algorithm (3mSOS) with the Levenberg–Marquardt backpropagation neural network (LMNN) to establish the intricate relationship between these design parameters and the load-displacement response of the piles. Numerical results underscore the model's exceptional performance in accurately predicting the load-displacement behavior of bored piles. Rigorous validation employs an independent dataset derived from bidirectional pile load tests, affirming the model's reliability. A comprehensive sensitivity analysis provides valuable insights into the mechanisms governing load-bearing. Feature importance analysis and partial dependence plots reveal nuanced relationships among input variables and output behavior. The model's novelty lies in pioneering the application of advanced metaheuristic algorithms, notably 3mSOS, in pile foundations—a distinctive contribution to geotechnical engineering. This research holds significant promise for enhancing the efficiency and accuracy of pile design in high-rise buildings, thereby bolstering the overall reliability of foundation design. • 3mSOS-LMNN transforms large bored pile foundation design, ensuring precise load-displacement predictions. • SHAP analysis pinpoints parameters influencing pile head displacement, aiding informed decision-making. • PDPs provide a profound understanding of influential parameters, emphasizing their crucial role in pile behavior. [ABSTRACT FROM AUTHOR]

Cement-based columns in combination with geosynthetic reinforcement is a well-established soft ground improvement technique to enhance embankment stability. This paper aims to present a finite-element (FE) study based on a case history of a geosynthetic-reinforced column-supported (GRCS) embankment over soft soil. In this study, the columns are simulated with an advanced Concrete model to simulate the development of possible cracking and induced strain-softening. Numerical results are compared against published centrifuge tests, giving confidence to the established FE model with the Concrete model. New insights into the progressive failure mechanisms of GRCS embankments over soft soil are then discussed by examining the stress paths, internal forces, and cracks, as well as the plastic failure zones of columns. In addition, the role of columns and geosynthetics on the progressive failure mechanisms (failure loads and sequences) is also examined by an extensive parametric study. The results suggest that provided the optimization of compressive and tensile forces in the columns combined with the tensile stiffness of the geosynthetics is put in place, more columns can be mobilized to resist global sliding failure and to improve the bearing capacity of GRCS embankments. • The crack-induced softening of DCM columns is captured using the Concrete model. • Progressive mechanisms of GRCS embankments over soft clay are interpreted. • The role of geosynthetic and columns on progressive failure mechanisms of GRCS embankments are identified. [ABSTRACT FROM AUTHOR]

• Novel coupled approach for undrained stability of rectangular tunnels. • Isogeometric analysis and deep learning ensure accuracy and efficiency. • High accuracy with mean squared error of 10−6 in predicting tunnel stability. The problem considered in this paper is for the stability evaluation of a rectangular tunnel in undrained clay during lining process. The approach adopted involved the use of isogeometric analysis (IGA) and the upper bound limit analysis formulation for the stability analysis. For the geometrical representation, B-spline basis functions are used to generate a set of B-spline surfaces that define the boundary of the soil domain, allowing for the exact representation of the tunnel geometry. The upper bound limit analysis is then formulated as a second-order cone program (SOCP), which can be solved by using a numerical optimization algorithm. The accuracy and reliability of the proposed method are validated by comparing the results with those published in previous studies. Furthermore, a large dataset is generated by randomly varying the input parameters, and a deep learning model is trained to learn the dataset. The deep learning model is trained using the mean squared error (MSE) metric, which yields an MSE as small as 10−6, indicating the high accuracy and precision of the proposed approach. Feature analysis and Partial Dependence Plots (PDPs) were used to gain insights into physical behavior by identifying important variables and understanding their individual and collective effects. In conclusion, the coupling of IGA and upper bound limit analysis provides a comprehensive and reliable solution to the stability of rectangular tunnels. The results obtained from this approach are accurate and can be achieved with reduced computational cost, making it an attractive approach for practical engineering applications. This approach can be used as a basis for future research on tunnel and tunnel stability analysis and may be extended to other types of soil structures under complex geometries. [ABSTRACT FROM AUTHOR]

• A novel approach to study uplift resistance of suction caisson in NGI-ADP soils. • 2D finite element analysis for suction caisson. • Failure mechanism, respective calculated tables and charts are presented. • Machine learning approach for building empirical equation and sensitivity analysis. The paper presents a novel solution for evaluating the uplift resistance of caisson foundations in anisotropic clay using displacement finite element analysis (FEA) and artificial neural network (ANN). The commercial FEA PLAXIS code is used to examine the uplift resistance of caisson foundations with a built-in anisotropic soil model developed by Norwegian Geotechnical Institute (NGI) based on the Active-Direct Shear-Passive concept (NGI-ADP model). The uplift resistance is expressed by a dimensionless stability factor that is a function of four dimensionless design parameters, i.e., the ratio of depth to diameter, the adhesion factor at the interface between caisson and clay, the shear strength gradient ratio, and the anisotropic strength ratio. Using the produced numerical results, ANN is adopted as the data driven technique to propose an empirical equation to estimate the uplift resistance factor. Furthermore, a sensitivity study is implemented to evaluate the relative importance of the four considered parameters on the uplift resistance of the caisson. Design charts, tables and empirical equations are developed and can be used to assist in practical designs. [ABSTRACT FROM AUTHOR]

This paper presents a stability study on the collapse mechanisms of a plane-strain tunnel face in c - ϕ soils using the upper bound finite element method with rigid translatory moving elements (UBFELA-RTME) and nonlinear programming technique. Practical considerations are given to the unlined length influence behind the tunnel face. An advanced mesh adaptive updating strategy is adopted, aiming to improve the computational efficiency, the accuracy of upper-bound solutions, as well as the produced collapse mechanisms. The unlined length influence on the face stability and collapse mechanism of the tunnel face are determined with various combinations of tunnel depth ratios, soil friction angles, and dilatancy angles. Using the UBFELA-RTME with the Davis's approach and a mesh adapting strategy, the non-associated plasticity flow rule can be well approximated. The developed technique was validated against different numerical methods, and it is concluded that the tunnel face stability can be improved by increasing soil friction and dilatancy angles, and yet weakens as the unlined length increases where a mesh-liked collapse zone gradually appears on the tunnel vault top. It gradually evolves to a global collapse failure till the ground surface. The findings contribute to a better understanding of the ground surface failure under the unlined support length influence in tunnel construction. [ABSTRACT FROM AUTHOR]

The use of finite elements and limit analysis in the stability analysis of geo-structures is not uncommon nowadays, with the assumption of associated flow rule and rigid perfectly plastic constitutive soil model. Nevertheless, the simplified assumption may lead to misevaluation of the objective function for the nonlinear optimisation solution, thus giving rise to uncertainty in the design of geo-structures. In this study, the influence of non-associated flow rule on the stability evaluations for obliquely- and eccentrically-loaded shallow foundations, as well as for retaining structures with cohesionless granular backfill, is examined using lower bound finite elements and second-order cone programming (SOCP). A wide range of dilation angles (ψ) is considered for the study (e.g., from zero to a maximum of soil internal friction angle φ), and it is generally observed that failing to consider the non-associated flow rule in the soil constitutive model would yield non-conservative designs of geo-structures. These inaccurate predictions and subsequent unreliable stability analysis are more pronounced in cases of large soil internal friction angles. The findings in this study would be of great importance for practical design applications. [ABSTRACT FROM AUTHOR]