Geotechnical Engineering Problems Research Articles

Polymer-fiber combined effect for improving sand mechanical and micro-damage response

Sand instability is always an intractable problem in geotechnical engineering and its reinforcement using additives is an effective method. This paper introduces the organic polymer reinforcer (OPR) and the recycled polypropylene fiber (RPF) to modify sand. The unconfined compression and corresponding numerical tests were performed to investigate mechanical properties, deformation failure behavior, and microscopic response. Results showed that both materials positively affect the mechanical and deformation properties of modified sand synergistically. Optimized particle structure by the inter-particle polymer membrane linked the loose sand into a whole with fibers as a framework. Strain-softening properties were reduced with less strain localization under loading. This is also clearly related to the improvement of the micro-damage based on numerical results. OPR-RPF reinforcement transformed the bond failure from unidirectional concentration to multilocal dispersion by improving force chain construction, microcrack propagation, and energy allocation. Thus, the failure of modified sand showed a macroscopic progression from shear-faulting via ductile-faulting to ductile flow. Future research lies in further considering the particle shape and pore structure variations to provide new insights for a deeper understanding of sand stabilization mechanisms and engineering applications.

Analysis of the thermo-hydro-mechanical coupled dynamic response of sediments under eccentric driving of deep-sea mining vehicle

This study establishes a coupled thermo-hydro-mechanical dynamic model(THMD) for saturated porous sediments under the eccentric motion of a deep-sea mining vehicle, considering the characteristics of deep-sea sediments. We applied the method of complex Fourier series expansion to simplify the coupled equations into ordinary differential equations and obtained the complex Fourier series expansion forms of various physical quantities of sediments during the eccentric movement of the mining vehicle. The undetermined constants are determined based on boundary conditions, yielding a series representation of the solution for THMD of deep-sea sediments. Subsequently, the solution was utilized to investigate the influence of mining vehicle parameters on the dynamic response of deep-sea sediments. This study demonstrates that the vertical displacement, vertical stress, and excess pore water pressure of sediments increase as the speed of the mining vehicle increases. Furthermore, the vertical displacement, vertical stress, and excess pore water pressure increase with the increment of load amplitude, and the differences in the curves at different eccentricities gradually become larger as the load amplitude increases. The proposed model can be applied to geotechnical engineering problems in saturated porous foundations, providing a necessary theoretical basis and analytical approach.

Novel neural network-based metaheuristic models for the stability prediction of rectangular trapdoors in anisotropic and non-homogeneous clay

The problem of trapdoor stability is a crucial problem in geotechnical engineering. This study is the first to introduce novel neural network-based metaheuristic models for the stability prediction of 3D rectangular trapdoors in anisotropic and nonhomogeneous clays. However, no researcher has considered such trapdoor problems in the past. In this study, the dataset is obtained by using finite element limit analysis (FELA). The proposed hybrid machine learning models based on artificial neural networks (ANNs) and various types of optimization algorithms (OAs), namely, ant colony optimization (ACO), artificial lion optimization (ALO), the imperialist competition algorithm (ICA), and shuffled complex evolution (SCE), are also proposed in this paper by undergoing rigorous optimization to ensure accuracy and efficiency in capturing the intricate dynamics of the stability investigations from the models. The performance of the proposed ANN-based models is assessed using several performance metrics, regression plots, and rank analysis. The proposed ANN-SCE model outperforms the other proposed models in predicting trapdoor stability, where the ANN-SCE model achieved the highest rank, with a score of 58, followed by the ANN-ALO (47), ANN-ICA (33), and ANN-ACO (22) models. The proposed neural network-based metaheuristic models deliver precise and effective forecasts of trapdoor stability to make informed decisions concerning road design and mitigation tactics, ultimately improving the robustness of infrastructure facing geotechnical challenges.

Analysis of Soil Improvement Through PVD and Vacuum Preloading with Several Equivalent Permeability Methods

Vacuum preloading combined with prefabricated vertical drain (PVD) is one of the common soft soil improvement methods. Soft soils often pose significant problems in construction projects due to their low shear strength and high compressibility, leading to settlement issues and potential structural instability. The PVD combined with vacuum preloading method addresses these problems by accelerating the consolidation process and minimizing settlement during service period. The acceleration occurs due to the presence of PVD, allowing dissipation of excess pore water in horizontal direction towards the PVD. Thereafter, the water in the PVD is drained to the surface. When modelled in 2D, PVD behaves as a continuous drain in the plane strain direction, causing the flow conditions to deviate from the actual conditions. To address this issue, equivalent soil permeability values is required, allowing the 2D model to produce results closely resembling the actual conditions. This research explores the improvement of PVD vacuum preloading through three equivalent permeability approaches. Utilizing field monitoring data, which includes settlement measurements from settlement plates, changes in pore water pressure recorded by piezometers, and lateral deformation data captured by inclinometers, the study evaluates the effectiveness of these approaches. Comparative analyses with field monitoring data reveal that Indraratna equivalent permeability method has the best fit. The integration of PVD and vacuum preloading, coupled with the refinement of equivalent permeability methodologies, offers a promising solution for addressing soft soil problems in geotechnical engineering. This research contributes to the practical application of these methods in construction projects, emphasizing their potential to enhance soil stabilization and reduce settlement-related risks.

A comparative study of soil classification machine learning models for construction management

Prior to the construction of buildings and roads, soil classification plays a vital role to ensure proper project management and durability of the structure. Civil engineers need to possess knowledge about soil behavior and properties of soil classification to construct reliable and long-lasting infrastructures. However, traditional soil classification methods are both expensive and time-consuming. Recently, machine learning (ML) has become increasingly popular in solving complex problems in Geotechnical Engineering, leading to novel approaches for automating soil classification. This research evaluates the effectiveness of various machine learning algorithms, including Multinomial Logistic Regression (MLR), Gaussian Naive Bayes (GNB), Extreme Gradient Boosting (XGBoost), Random Forest, and Artificial Neural Network-Multilayer Layer Perceptron (ANN-MLP), in classifying soils. The paper also implemented Hard and Soft Voting Ensemble models. Each model was quantitatively evaluated and compared using various metrics. The models achieved high scores in multiple performance metrics across the soil classes with accuracies ranging between 87% and 93%, except the Naive Bayes model with a balanced accuracy of 77.6% while the hard-voting model outperformed the others with a balanced accuracy of 93%. The study’s results benefit industry practitioners and academic researchers for integrating and assessing ML algorithms in soil classification.

Numerical Investigation of Bearing Capacity of Centrally and Eccentrically Loaded Surface Footing on Sand-Clay with Void Using Plaxis 2D

The presence of voids under footings can significantly reduce the stability of the foundation and the superstructure above it. Estimating the bearing capacity of a footing with voids below it is a complex and challenging problem in geotechnical engineering. This study investigates the stability of centrally and eccentrically loaded surface footings supported by sand layers of variable thickness overlying clay layers, with circular voids below the center of the footing. Using the Plaxis finite element software, the influence of various parameters on the overall performance of the footing, including load eccentricity, void location, void size, and introducing geogrid reinforcement at the sand-clay interface are analyzed. The results show that increasing the void depth, add a thick layer of sand or introducing geogrid reinforcement can mitigate the impact of a void beneath a foundation.

Particle Filter based on Jaya optimisation for Bayesian updating of nonlinear models

Particle filter (PF) is a powerful and commonly used filtering technique based on Sequential Monte Carlo framework. The main challenge in using PF for nonlinear state and parameter estimation is the degeneracy of particles. Although resampling techniques can solve this to some extent, it would still result in particle impoverishment when a limited number of particles are used thereby affecting the accuracy. Hence, a hybrid metaheuristic optimisation algorithm that combines the PF with Jaya optimisation, (PF-JAYA) has been proposed and implemented for joint state and parameter estimation for geotechnical engineering problems. The performance of PF-JAYA has been compared against the traditional Particle Filter with Sampling Importance Resampling (PF-SIR) technique. The synthetic examples show that PF-JAYA outperforms PF-SIR in terms of accuracy, rate of convergence, parameter identification and particle diversity. Furthermore, the performance of PF-JAYA is independent of the choice of prior distribution and due to its superior convergence proves to be efficient when working with sparse monitoring information. The performance of PF-JAYA on Bayesian updating of state and parameters of an elastoplastic model for a synthetic embankment case has also been evaluated where, along with PF-SIR, the Ensemble Kalman Filter (EnKF) is also chosen for comparison. Finally a further evaluation using the Lorenz ‘63 model, shows the superior performance of PF-JAYA in terms of accuracy and precision over the classical Data Assimilation techniques.

Mechanical behavior assessment of retaining wall structure due to frost heave of frozen ground

Frost heave action is a major issue in permafrost regions, leading to various geotechnical engineering problems. In this study, we assess the mechanical behavior of a concrete retaining wall subjected to frost heave under different ground conditions. The assessment utilizes ABAQUS integrated with several user subroutines. The numerical simulation model employs a thermo-mechanical coupled analysis with a porosity rate function, which enables to simulate time-dependent variations in porosity and frost heave of the backfill soil. After verification of the predictive reliability of the simulation model, the frost heave action in the soil and mechanical response of the retaining wall were evaluated regarding the initial groundwater level and presence of a drainage material on the backside of the retaining wall. According to the simulation results, as the initial groundwater level decreased in the backfill soil, the area susceptible to frost heave decreased. However, the von Mises stresses applied to the retaining wall increased. Under the same ground conditions, when the drainage material was installed on the backside of the retaining wall, the frost heave pressure acting on the wall significantly decreased, and less deformation and distortion of the retaining wall occurred.

Digital tools used on the teaching-learning process in geotechnical engineering

This article presents the incorporation of information and communication technologies on the teaching-learning process of geotechnical engineering to improve the quality of education and provide practical knowledge to civil engineering students. The content of this paper is divided into three main modules, which are: Treatment of laboratory tests results, Slope stability and Rock mass stability. For this, some software were used, including 2D limit equilibrium stability analysis, stability analysis of rock wedges and a dynamic mathematics, to obtain and analyze soil shear strength parameters, among others. Furthermore, a digital tool was developed to analyze the results gathered in compaction, one-dimensional consolidation, and direct soil shear tests, in order to clarify the relationship between theoretical concepts and practical results of the tests and analyses and to help students on doubts, in addition to increase their interest and motivation to perform the complete interpretation of the collected data. The activities were conducted in classes of geotechnical disciplines of the undergraduate course in Civil Engineering at the Federal University of Viçosa, aiming to promote active learning and improve teaching quality. Based on the results of an applied feedback questionnaire, it was observed that most students were satisfied with the resources used in the classroom, demonstrating that the implemented digital tools work as a didactic instrument that facilitates learning and comprehension of practical problems, in addition to enabling the resolution of several geotechnical engineering problems much more quickly and efficiently.

Students’ perception of the impact of a Geotechnical Engineering field activity on their competences development

The need to develop several students’ competencies is one of the leading challenges for Engineering instructors in undergraduate courses. It has been quite exhausting in many cases, mainly due to the lack of professors’ expertise in engineering education. Case studies may provide examples and help develop professors’ ability to design effective learning experiences. In this context, this paper presents a case study of implementing an in-class/ ex-class activity conducted in the Soil Mechanics-I course at the Federal University of Rio Grande do Norte. Additionally, it aims to discuss students’ perception regarding development of the proposed activity and competences. The activity comprised three phases: selection of Geotechnical Engineering problems on university campus, documentation and analysis of each situation, and presentation of solutions considering technical, environmental, and social aspects. Students’ perceptions were assessed using an anonymous online survey (18 Likert and open-ended questions), divided into three categories: general impressions, competencies development, and open statements. General impressions and competencies development were mostly positive, with deadlines reported as the most challenging aspect. Open questions responses provided positive feedback, emphasizing the main developed competencies, according to students’ perspective (e.g. leadership, interpersonal relationship, and analytical view of the problem). The need of dealing with real problems and work in groups appears to be a successful approach for teaching Geotechnical Engineering courses and developing competences in Engineering undergraduate courses. This case study can support innovation in teaching any engineering course and help students face future professional challenges.

Updated Lagrangian nonlocal general particle dynamics for large deformation problems

The traditional general particle dynamics (GPD) has been successfully applied in modeling fracture propagation, frictional contact and stick–slip problems in rocks. In this study, the updated Lagrangian nonlocal general particle dynamics (UL-NGPD) method is proposed to solve large deformation problems. In the UL-NGPD, the continuity equation and momentum equation are reformulated in the integro-differential formulation by introducing nonlocal theories in the updated Lagrangian framework. The artificial viscosity and artificial force state are utilized to enhance the numerical stability and accuracy in modeling large deformation. The nonlocal velocity gradient in the deformed configuration is used to update the Cauchy stress rate from the Drucker-Prager elasto-plastic constitutive model with strain-softening behaviour for solids. The UL-NGPD paradigm is numerically implemented through an explicit Predictor-Corrector scheme for high-performance computing. The stability and accuracy of the UL-NGPD are verified by numerical tests on compression test, slope stability analysis and aluminum bar collapse experiment. Thereafter, simulations of granular soil collapse and retrogressive landslide in sensitive clays are presented to demonstrate its efficacy and robustness in modeling challenging geotechnical problems involving large deformation. The numerical results show that the proposed UL-NGPD is powerful and suitable for analyzing large deformation problems in geotechnical engineering.

Multi-field coupling analysis of mechanical responses in methane hydrate exploitation with a practical numerical approach combining T + H with DEM

The methane hydrate (MH) is widely distributed in deep-sea sedimentary layers, considered as the most promising clean energy to replace conventional oil and gas in the 21st century. Large-scale exploitation of MH may lead to a series of geotechnical engineering problems. To numerically estimate the seabed stability during MH exploitation which should involve multi-field coupling simulation of complex mechanical behaviors of methane hydrate bearing sediment (MHBS), a new practical hybrid numerical method is proposed, which combines the TOUGH + HYDRATE (T + H) code, targeting multiphase flow analysis of hydrate-bearing geologic systems, with the improved Distinct Element Method (DEM). Porosity, temperature, pressure, salinity fields are exchanged between DEM and T + H to realize the thermal-hydro-mechanical-chemical (THMC) coupling and to reproduce the progressive discontinuous failure process. The results of verification show obvious uniformity between numerical and analytical results for one-dimensional (1D) consolidation and 1D heat conduction tests on soils. The paper tries to analyze evolutions of THMC fields, gas production, hydrate bond breakage and other mechanical behaviors of actual depressurization production by using this method, focusing on geotechnical risks of exploitation. In addition, study observes significant changes within certain range from the wellbore for gas production and mechanical responses in the early stage.

A universal hydro-mechanical coupled behavior model for clay-bearing strata—Molecular-level simulation approach

Clay minerals in soils and rocks exhibit large volume change upon interaction with water and this behavior becomes even more complex when the strata are being stressed by the engineering and environmental loads. Therefore, a realistic prediction of the hydro-mechanical behavior of the clay-bearing strata is always a challenge due to their coupled swelling-mechanical response in the cases of geotechnical and geoenvironmental engineering problems, nuclear waste storage in clay-bearing rock repositories, shale gas extraction, and other uses of clay in the manufacturing industry. All the existing behavior models have restricted applications in the engineering and other fields of practice mainly due to the partial consideration of the structure and fabric of clay-bearing strata in the model formulation. In this study, a hydro-mechanical behavior model has been formulated using the parameters acquired from the molecular-level simulations and modeling of the volume change and stress–strain behavior of the clay-bearing structure. The Molecular Mechanics and Molecular Dynamic simulations were performed on the natural structure of the clay-bearing strata formulated using Monte Carlo technique. The mathematical model, developed from the simulation results, can predict the overall hydro-mechanical behavior of clay-bearing strata for all possible combinations of clay minerals, non-clay minerals, salts causing cementation of the soil/rock structure, confining pressures, and the induced strain levels. The developed model has successfully been validated through laboratory and field testing on the clay-bearing strata in both the elastic and elasto-plastic regions of the stress–strain behavior and also from the data of two (02) swelling clays (MX-80 and FEBEX Bentonite) from the existing literature, supporting the universal nature of the developed behavior model.

Evaluation of Shear Stress in Soils Stabilized with Biofuel Co-Products via Regression Analysis Methods

In recent years, the employment of artificial neural networks (ANNs) has risen in various engineering fields. ANNs have been applied to a range of geotechnical engineering problems and have shown promising outcomes. The aim of this article is to enhance the effectiveness of estimating unfamiliar intermediate values from existing shear stress data by employing ANNs. Artificial neural network modelling was undertaken through the Regression Learner program that is integrated with the Matlab 2023a software package. This program offers a user-friendly graphical interface for developing AI models absent of the need for any coding. The validation and training of the ANNs were executed by relying on shear box test data which had been conducted at the Geotechnical Laboratory situated at Iowa State University. The objective of these experiments was to explore the potential of biofuel co-products (BCPs) in soil stabilization. The data should be structured with input and output parameters in columns and samples in rows. The dataset comprises a 216 × 6 matrix. The data columns provide information on soil type (pure soil—unadulterated; and 12% BCP-adulterated soil), time (1, 7, and 28 days), normal stress (0.069-DS10, 0.138-DS20, and 0.207-DS30 MPa), moisture content (OMC−4%, OMC%, and OMC+4%), and corresponding shear stress (σ, MPa) values. The AI predictions for the test data output provide an outstanding R2 score of 0.94. This indicates that employing ANN to teach shear test data facilitates gaining a large quantity of data more efficiently, with fewer experiments and in less time. Such an approach seems encouraging for geotechnical engineering.

An improved convected particle domain interpolation material point method for large deformation geotechnical problems

AbstractA novel material point method called the improved convected particle domain interpolation (ICPDI) is proposed in this paper to simulate large‐deformation problems in geotechnical engineering. The ICPDI framework is based on the existing convected particle domain interpolation (CPDI) and employs an adaptive orthogonal improved interpolating moving least square (AOIIMLS) shape functions. The AOIIMLS shape functions prevent computational instability problems caused by singular or ill‐conditioned matrices through avoiding the direct solving of inverse matrices. Additionally, ICPDI adopts the velocity gradient to calculate the velocity field in particle domains and reduces cell‐crossing errors by using AOIIMLS shape functions. To reconstruct the velocity function in the background grid, the velocities of the material points and four related corner points are employed through AOIIMLS shape functions. The bar axis‐aligned vibration, ring radial expansion, slope elasto‐plastic slumping, and post‐failure tunnel collapse behavior examples are used to verify the accuracy and effectiveness of ICPDI by comparing the results with other numerical methods and experiments. Numerical simulation results show that ICPDI outperforms the material point method (MPM) and CPDI in terms of computational accuracy and convergence while reducing cell‐crossing errors. Consequently, this study demonstrates the capability of ICPDI in simulations of large‐deformation geotechnical problems.

Seismic active earth pressure for 3D earth retaining structure following a nonlinear failure criterion

Stability analysis for three-dimensional (3D) earth retaining structures (ERSs) is a pivotal problem in geotechnical engineering, particularly for seismically active areas. In this work, the 3D ERSs in soil are assessed for seismic stability following a nonlinear strength criterion. The power-law strength criterion and seismic forces are introduced into the 3D ERS stability analysis via a multi-cone failure mechanism. The coefficient of active soil pressure Ka is derived by exploiting the energy balance equation. The upper bound solutions, that is the maximum results of the Ka are captured with the help of a genetic algorithm. The validity of the current study is verified by a comparative analysis, and the effects of soil strength nonlinearity, the seismic forces, the soil-wall friction angle, the height, the inclined angle and the 3D geometric traits of ERSs on the Ka solutions and the stability of ERSs are investigated. It is indicated that the height and the 3D geometric characteristics of the ERSs will not only determine the Ka solutions directly, but also influence the impact of soil strength nonlinearity on the stability of ERSs. Strength criteria should be chosen combining the geometric characteristics of ERSs to derive more critical estimates on the stability of 3D ERSs. Additionally, a range of seismic and static stability charts used for preliminary design are put forward as well.

Efficient slope reliability analysis under soil spatial variability using maximum entropy distribution with fractional moments

Spatial variability of soil properties imposes a challenge for practical analysis and design in geotechnical engineering. The latter is particularly true for slope stability assessment, where the effects of uncertainty are synthesized in the so-called probability of failure. This probability quantifies the reliability of a slope and its numerical calculation is usually quite involved from a numerical viewpoint. In view of this issue, this paper proposes an approach for failure probability assessment based on Latinized partially stratified sampling and maximum entropy distribution with fractional moments. The spatial variability of geotechnical properties is represented by means of random fields and the Karhunen-Loève expansion. Then, failure probabilities are estimated employing maximum entropy distribution with fractional moments. The application of the proposed approach is examined with two examples: a case study of an undrained slope and a case study of a slope with cross-correlated random fields of strength parameters under a drained slope. The results show that the proposed approach has excellent accuracy and high efficiency, and it can be applied straightforwardly to similar geotechnical engineering problems.

Short-Term Predictions of Evaporation Using SoilCover at the Near-Surface of a Mine Waste Pile following Heavy Rainfall Events

Accurate measurements and predictions of near-surface soil drying and evaporation following heavy rainfall events are often needed for research in agriculture and hydrology. However, such measurements and predictions at mine waste pile and tailing settings are limited. The prediction of evaporation at mine waste piles is essential for many problems in geotechnical engineering, including the design of soil cover systems for the long-term closure of hazardous waste sites, and thus mitigates, for example, the generation of acid mine drainage (AMD) and metal leaching. AMD is one of mining’s most serious threats to the environment. This study investigated the short-term (8 days) and medium-term (27 days) drying rates and evaporative fluxes at the surface and near-surface of the Deilmann South waste-rock (DSWR) pile at the Key Lake uranium mine, northern Saskatchewan, using the gravimetric (GV) method and SoilCover (SC) model, respectively, during and following heavy rainfall events for the environment. The SC simulation results showed that during the weather-controlled stage (Stage I) of the first 5-day period of rainfall events, while the surface was wet, the potential evaporation (PE) was equal to the actual evaporation (AE) (i.e., AE/PE = 1). As the surface became drier on Day 6, the cumulative PE began to separate from the cumulative AE and the surface’s drying rate rapidly diverged from those at the deeper depths. This occurrence signaled the onset of the soil profile property-controlled stage (Stage II). As the drying continued, the surface became desiccated and the slow-rate drying stage (Stage III) was established from Day 7 onward. The SC-simulated AE results were compared to those measured using the eddy covariance (EC) method for the same test period at the DSWR pile in a different study. The comparison showed that the two methods yielded similar AE results, with 18% relative errors. The results of this study provided the opportunity to validate the SC model using actual data gathered under field conditions and to ascertain its ability to accurately predict the PE and AE at the surfaces of mine waste piles.

Numerical Investigation of the Pile–Soil Interaction Problem under Dynamic Loads

One of the most important problems of geotechnical engineering is the design of pile foundations. Piles are generally designed for vertical and horizontal static loads. However, it is important to design piles against dynamic loads in regions with high seismicity. This study verified a centrifuge experiment obtained from the literature with a finite element program. With the kinematic interaction analyses performed for this purpose, the effect of parameters such as layered soil, groundwater, pile stiffness, and earthquake acceleration were investigated numerically. According to the results obtained, it was understood that the earthquake magnitude is the most important parameter in kinematic interaction analyses. In addition, in layered soil conditions, the fact that 75% of the pile length is in the weak soil creates the most unfavorable situation. It was observed that pile stiffness and groundwater level also have certain effects on the kinematic interaction.

Verification of 3D DDA-SPH coupling method and its application in the analysis of geological disasters

At present, discontinuous deformation analysis (DDA) is in an active development stage and its application in the field of geohazards is expanding. Recently, the DDA method has been coupled with smooth particle hydrodynamics (SPH) to analyze some geohazards involving fluid-solid interactions (FSI) and has shown good prospects in the field. However, the interaction between rocks and water in geological disasters is extremely complex and the comprehensive verifications of the simulation results between the block and fluid under different interactive modes are never found. To solve this problem, we first introduced the basic principle of the DDA and SPH method, followed by the coupling algorithm of the two methods. Then, the correctness of the coupling method was verified through analyses of fluid and solid movement by adopting the dam break test, Scott Russell's wave generator experiment, water entry test of a single sphere, and landslide-generated impulsive wave, which represent different interaction modes between blocks and fluids respectively. Finally, based on the 3D DDA-SPH method, this paper simulated the chain disasters of the water inrush induced by tunnel collapse and the dam instability induced by slope sliding, and analyzed the chain mechanism between the different disasters, which illustrates the feasibility of 3D DDA-SPH coupling method in simulating the FSI problem in geotechnical engineering.