Geotechnical Engineering Research Articles

Development of the triaxial equipment: a state of art

Triaxial shear testing is one of the most crucial investigations in geotechnical laboratories. It is essential for designing specific projects and understanding soil behavior in geotechnical engineering. Since Casagrande developed the modern shear apparatus at Harvard University, it has been refined to provide a more accurate characterization of shear strength behavior. This research paper aims to offer a concise overview of triaxial equipment development, focusing on apparatus that is easy to set up, prepare, and operate reliably, while remaining cost-effective. The primary objective is to contribute to the accurate determination of suitable techniques and devices that meet specific testing objectives. We review the theoretical background of both the triaxial apparatus and the direct shear box and illustrate the basic working principles of various volume change measurement techniques using triaxial devices. We also present different operational principles employed in designing volume change devices for soil testing. Our analysis reveals that some of these devices require complex operational techniques and equipment, making them challenging to use for repetitive testing. Consequently, we conclude that the ideal volume change device for soil mechanics testing should prioritize robustness and ease of operation.

Accurate Prediction of Compression Index of Normally Consolidated Soils Using Artificial Neural Networks

The compression index (Cc) serves as a crucial parameter in predicting consolidation settlement in fine-grained soils, representing the slope of the void ratio logarithmic effective stress curve obtained from oedometer tests. However, traditional consolidation testing methods are notably time-consuming, typically spanning a 15-day period for preparation, execution, and parameter calculation, leading to significant delays in civil engineering projects. Therefore, there is an urgent need for effective methodologies to determine consolidation parameters within a shorter timeframe. Although various empirical formulas have been proposed over the years to correlate compressibility with soil parameters, none have reliably predicted the Cc across different datasets. In this study, to overcome this challenge, an alternative approach using artificial neural network (ANN) methodology to predict the compression index of fine-grained soils based on index properties is proposed. For this purpose, an ANN was trained and validated using a dataset consisting of 560 high and low- plasticity soil samples obtained from construction sites in various regions of Turkey over the last forty years, as well as soil borings in Istanbul. The modeling of artificial neural networks was performed using the Regression Learner program, which integrates with the Matlab 2023a software package and offers a user-friendly graphical interface for AI model development without coding. The data set, which was structured as a matrix with dimensions of 458 × 6, included input parameters such as the natural water content, liquid limit, plastic limit, plastic index and initial void ratio, as well as information on the compression index, which was the output variable. The developed ANN model showed an outstanding predictive performance when predicting the output of the test data, achieving an outstanding R2 score of 0.81. This underlines the potential of ANN methodologies to efficiently extract important data with fewer experiments and in less time, and offers promising applications in the field of geotechnical engineering.

Offshore geotechnical challenges of the energy transition

Offshore wind is the most mature of the offshore renewable energy technologies and has a significant role to play in the energy transition. 2000 GW of offshore wind capacity is anticipated globally by 2050 in order meet the targets of the Paris Agreement; 35 times the current installed capacity. The pace and scale of offshore wind ambitions to support the energy transition present a range of challenges for the offshore geotechnical sector and the broader offshore wind sector. Challenges extend across the life-cycle of projects from marine spatial planning, site investigation, design, manufacturing, installation, operation and decommissioning, and across the supply chain regarding availability of raw materials for foundations, anchors and mooring systems, vessels and equipment for site investigation and installation, and trained geotechnical personnel. This paper identifies five key challenges and sets out the necessary shifts in technology, culture and practice in geotechnical engineering to achieve the ambitious targets to deliver offshore wind at the pace and scale required for the energy transition. The paper closes with a reflection on the consequence of delaying or not meeting net-zero targets, and thus identifying the urgency for these shifts in technology, culture and practice to be developed and adopted.

An open-source MATLAB toolbox for 3D block cutting and 3D mesh cutting in geotechnical engineering

This study presents a simple approach and its associated MATLAB toolbox for 3D block cutting and mesh cutting. The approach is suitable for the meshes of numerical methods including the Key Block Theory (KBT), the Discontinuous Deformation Analysis (DDA), the Numerical Manifold Method (NMM), and the cut Finite Element Method (Cut-FEM). The strategy is based on calculations on convex bodies. It uses two different forms of representation: the geometric representation which includes vertices and faces, and the algebraic representation which consists of inequalities. The cutting was implemented on the algebraic representation, and the resulting inequalities were converted into a geometric representation. The above strategy turned out to be robust and straightforward to execute, at the cost of a general body being regarded as a combination of convex bodies. The efficiency guarantee was considered through pre-checking algorithms. The source code was provided, as well as some simple examples.

Experimental Study on Static and Dynamic Characteristics of Sand–Clay Mixtures with Different Mass Ratios

The alteration of soil static and dynamic characteristics induced by clay content constitutes a crucial issue in the realm of disaster prevention and mitigation within geotechnical engineering. The static and dynamic characteristics of mixed soils with varying sand–clay contents were investigated through the design and implementation of static and dynamic triaxial tests. The relationship between clay content and soil resistance to liquefaction was investigated, with an analysis of the influence of clay content on soil strength and static liquefaction performance. Furthermore, the study examined the soil’s resistance to liquefaction under dynamic constitutive and cyclic loading conditions for soils with varying clay content. Results indicate that stress–strain curves for samples with varying clay content exhibit a consistent trend, with the lowest tangent modulus and peak strength observed in samples containing 30% clay. Increasing clay content diminishes soil’s resistance to liquefaction under static loading conditions. Higher confining pressures correspond to larger tangent moduli and peak deviating stresses in triaxial shear tests. Dynamic shear modulus decreases as clay content increases, whereas damping ratio decreases accordingly. Soil gradation significantly affects liquefaction-induced deformation, with the sample containing 30% clay experiencing the fastest increase in pore water pressure during testing failure, accompanied by fewer cyclic loading cycles until failure occurs. Improving soil gradation and adjusting the sand–clay ratio are beneficial for enhancing both soil strength and its resistance to liquefaction.

Effect of cross-correlated hydraulic conductivity and compression index on nonlinear consolidation in randomly heterogeneous highly compressible aquitards

The problem of land subsidence due to groundwater pumping in highly compressible, randomly heterogenous aquitards with stress-dependent parameters is analyzed through one-dimensional Monte Carlo numerical simulations of nonlinear groundwater flow and consolidation. The natural logarithm of hydraulic conductivity (Y) and compression index (Cc) are considered spatially cross-correlated random variables. A stochastic simulation method based on a bivariate normal copula was applied to generate positive, negative, and uncorrelated realizations of the two random variables. The results show that when Y and Cc are correlated random parameters, the ensemble average total settlement is consistently larger (between 13% and 40% for the synthetic cases tested) than the total settlement obtained when the parameters are deterministic (perfectly known). In addition, the results of negative and no correlation between Y and Cc are similar in terms of the ensemble average and variance of total settlement, while positive correlation leads to larger values of total settlement and its variance. The stochastic approach employed here allows incorporating the uncertainty in the parameters in the simulation of the subsidence process, which is relevant for geotechnical engineering, design of structures and groundwater management.

A reliability analysis framework coupled with statistical uncertainty characterization for geotechnical engineering

Reliability analysis plays an important role in the risk management of geotechnical engineering. For the random field-based method, it is expected that the uncertainty characterization of geo-material parameters and the realization of random field can be integrated effectively. Moreover, as the increase in measured data size is generally difficult in the field investigation of geotechnical engineering due to limitation of budget and time etc., the statistical uncertainty resulting from sparse data should be paid great attention. Therefore, taking the determination of hyper-parameters for Bayesian-based conditional random field as the breakthrough, this study proposed a reliability analysis framework to achieve the expectation above. In this proposed reliability analysis framework, the present characterization method of statistical uncertainty is improved by setting the lognormal distribution as the prior distribution of scale of fluctuation (SOF). Subsequently, the performance of statistical uncertainty characterization method is tested by a set of unconfined compressive strength (UCS) database about rocks. Then, a case study about the stability analysis of slope is employed to demonstrate the beneficial effect of the proposed reliability analysis framework. It is found that the uncertainty in both the realization of random field and the reliability analysis results can be significantly mitigated by the proposed reliability analysis framework.

Mechanical and hydraulic characteristics of unvegetated or vegetated loess soils amended with xanthan gum

With the development of western and central China, the increasing construction of transportation infrastructures (e.g., roads, railways, etc.) has been producing a large number of man-made slopes in loess regions. Biopolymer as an eco-friendly stabilizing material, has a great potential to amend soils for the purpose of ground improvement and slope stabilization, with its application in vegetated soils particularly at the initial growing stage to support vegetation growth as well as offer reinforcement to unprotected soils being reported recently. In the current study, the contribution of xanthan gum (XG) to both mechanical and hydraulic behaviors of the unvegetated and vegetated loess soils subjected to climatic wetting–drying cycles under the impacts of degree of compaction and biopolymer content are explored, whilst the effectiveness of the two XG-based soil reinforcing methods (with and without vegetation) are compared. Results indicate that XG treatment improved the water-retention capacity of loess soils to an extent that even the degradation of water retention upon initial wetting–drying cycles could be mitigated particularly at higher degrees of compaction. The strengthened shear resistance of loess soils was observed, mainly attributed to the increased soil cohesion, whilst the friction angle remained almost constant. The unvegetated loess soils basically had an increased cohesion proportional to the XG content up to 1.00% of the dry soil mass, whilst the vegetated soils had the highest soil cohesion at a fixed XG content of 0.50% which corresponded to the mostly promoted vegetation growth. Although a dense soil structure inhibited the seed germination and growth of sprouts and roots, it contributed to the overall shear strength of the vegetated soils similar to its effect on the unvegetated soils. XG amendment outperformed planting vegetation at enhancing soil shear strength at a degree of compaction of 95% (comparable to those adopted for design of highway subgrade), whilst providing XG in combination with vegetation was able to result in a more sizable strength increment over the summation of gains in strength by utilizing XG and vegetation separately, suggesting the considerable potential of XG in assisting vegetated soils for ecological slope stabilization. The results obtained from the current research support the broader usage of biopolymers (either used alone or in combination with vegetation) as reliable geotechnical engineering materials in practical implementations.

Reliability‐based state parameter liquefaction probability prediction using soft computing techniques

The state parameter (ѱ) accounts for both relative density and effective stress, which influence the cyclic stress or liquefaction characteristic of the soil significantly. This study presents a ѱ‐based probabilistic liquefaction evaluation method using six soft computing (SC) techniques. The liquefaction probability of failure (PL) is calculated using the first‐order second moment (FOSM) method based on the cone penetration test (CPT) database. Then, six SC techniques, such as Gaussian process regression (GPR), relevance vector machine (RVM), functional network (FN), genetic programming (GP), minimax probability machine regression (MPMR) and multivariate adaptive regression splines (MARS), are used to predict PL. The performance of these models is examined using nine statistical indices. Additionally, plots such as regression plots, Taylor diagrams, error matrix and rank analysis are shown to assess the SC model's performance. Finally, sensitivity analysis is performed using the cosine amplitude method (CAM) to assess the influence of input parameters on output. The current study demonstrates that SC models based on state parameter predict PL effectively. RVM and MPMR models closely follow the GPR model in terms of performance, which is superior to the other models. Notably, two equations are generated using GP and MARS models to predict PL. The results of the sensitivity analysis reveal the magnitude of earthquake (Mw) as the most sensitive parameter. The outcomes of this research will offer risk evaluations for geotechnical engineering designs and expand the use of state parameter‐based SC models in liquefaction analysis.

Comparison of elements and state-variable transfer methods for quasi-incompressible material behaviour in the particle finite element method

AbstractThe Particle Finite Element Method (PFEM) is attractive for the simulation of large deformation problems, e.g. in free-surface fluid flows, fluid–structure interaction and in solid mechanics for geotechnical engineering and production processes. During cutting, forming or melting of metal, quasi-incompressible material behaviour is often considered. To circumvent the associated volumetric locking in finite element simulations, different approaches have been proposed in the literature and a stabilised low-order mixed formulation (P1P1) is state-of-the-art. The present paper compares the established mixed formulation with a higher order pure displacement element (TRI6) under 2d plane strain conditions. The TRI6 element requires specialized handling, involving the deletion and re-addition of edge-mid-nodes during triangulation remeshing. The robustness of both element formulations is analysed along with different state-variable transfer schemes, which are not yet widely discussed in the literature. The influence of the stabilisation factor in the P1P1 element formulation is investigated, and an equation linking this factor to the Poisson ratio for hyperelastic materials is proposed.

Stress-dependent instantaneous cohesion and friction angle for the Mohr–Coulomb criterion

The strength criterion of rock is essential for stability control and safety design of geotechnical engineering constructions. Due to its widespread adoption, the Mohr–Coulomb (M-C) criterion is prominent among strength criteria. However, the M-C criterion is constrained by three significant limitations: it fails to capture the nonlinear strength response, overlooks the critical state, and disregards σ2. This study introduces a novel Stress-dependent Instantaneous Friction angle and Cohesion (SIFC) model for the M-C criterion to represent the convex strength envelope of intact rock, covering the spectrum from non-critical to critical states. In pursuit of this objective, an innovative approach for calculating these instantaneous shear parameters at each corresponding σ3 is initially introduced. By examining the confining pressure dependency of the instantaneous friction angle and cohesion, the SIFC model is derived and introduced to the M-C criterion. The SIFC-enhanced M-C criterion, utilizing parameters obtained from triaxial tests under lower σ3, delineates the complete non-linear strength envelope in (σ1, σ3) space, covering brittle to ductile behavior. This criterion is then extended to polyaxial stress conditions. Validation through triaxial test data confirms that the SIFC-enhanced M-C criterion accurately reflects the strength characteristics of the tested rocks.

Advancing earth science in geotechnical engineering: A data-driven soft computing technique for unconfined compressive strength prediction in soft soil

Advancing earth science in geotechnical engineering: A data-driven soft computing technique for unconfined compressive strength prediction in soft soil

Effect of leachate on the geotechnical properties of soils at Gbagede Dumpsite Ilorin, Kwara State Nigeria

The increasing request for space for private buildings was brought about by the utilization of the previous dumpsites. If the issue of leachate infiltration into the soil isn't legitimately controlled it'll lead to future harm in construction works. The objectives are to compare the geotechnical properties of soil of the contaminated and uncontaminated regions region of the dump site and evaluate if the effect of leachate on the geotechnical properties of soil changes with depth. Laboratory soil tests were conducted on the soil samples obtained and compared the effect of these leachates at the dump site. These methods are Natural water content, Bulk Density, Specific Gravity, Shear strength, and Consolidation tests. The soil samples were obtained from the contaminated region, and the uncontaminated region (i.e. at 100 m away from the dumpsite). All soil samples were obtained at depths 0.5m, 1.0m, and 1.5m below the ground level, to know the effect of leachate on the soil at the dumpsite and also to know if the effects of leachate changes with depth as it goes down the soil. The results obtained show that samples at 0.5m and 1m depth have been affected by leachates but the effects are not so significant at 1.5m depth, thereby making the soil at depths 0.5m and 1m unfit for construction purposes. This result was useful to check the land requirement in urban areas and guide the geotechnical engineers when designing and constructing foundations for buildings and other related structures on these types of soils.

Evaluating the Value of a Geology Degree: An Economic and Societal Analysis

In an era where the economic returns on investment in higher education are increasingly scrutinized, the question arises: Is a geology degree worth it? This paper tends to provide answers to this inquiry through comprehensive economic analysis to evaluate the financial viability and broader benefits of obtaining a geology degree. The analysis begins by examining the economic landscape for geologists, including their earning potential, employment opportunities, and career advancement trajectories. Data from national labor statistics, industry reports, and academic studies are synthesized to provide a nuanced understanding of the financial outcomes associated with a geology degree. Factors such as geographic location, industry specialization, and level of education are considered to capture the variability in economic outcomes among geology graduates. This paper also explores the non-monetary benefits of a geology degree, such as job satisfaction, intellectual fulfilment, and contributions to societal challenges like natural resource management, environmental conservation, and hazard mitigation. These aspects contribute to a holistic evaluation of the degree's value proposition, balancing economic returns with intrinsic rewards. Furthermore, the analysis addresses trends in the geology job market, including the impacts of technological advancements, environmental regulations, and global economic shifts on career opportunities for geologists. Insights into emerging fields within geology, such as geotechnical engineering, hydrogeology, and environmental consulting, are also discussed to illuminate evolving career pathways and specialization opportunities. Through this multidimensional examination, the paper aims to provide valuable insights to prospective students contemplating a geology degree, educators shaping curricula, and policymakers concerned with higher education and workforce development. By integrating economic principles with empirical data and qualitative perspectives, this study offers a robust framework for understanding a geology degree's economic implications and broader societal contributions in contemporary contexts.

Synthesizing realistic sand assemblies with denoising diffusion in latent space

AbstractThe shapes and morphological features of grains in sand assemblies have far‐reaching implications in many engineering applications, such as geotechnical engineering, computer animations, petroleum engineering, and concentrated solar power. Yet, our understanding of the influence of grain geometries on macroscopic response is often only qualitative, due to the limited availability of high‐quality 3D grain geometry data. In this paper, we introduce a denoising diffusion algorithm that uses a set of point clouds collected from the surface of individual sand grains to generate grains in the latent space. By employing a point cloud autoencoder, the three‐dimensional point cloud structures of sand grains are first encoded into a lower‐dimensional latent space. A generative denoising diffusion probabilistic model is trained to produce synthetic sand that maximizes the log‐likelihood of the generated samples belonging to the original data distribution measured by a Kullback‐Leibler divergence. Numerical experiments suggest that the proposed method is capable of generating realistic grains with morphology, shapes and sizes consistent with the training data inferred from an F50 sand database. We then use a rigid contact dynamic simulator to pour the synthetic sand in a confined volume to form granular assemblies in a static equilibrium state with targeted distribution properties. To ensure third‐party validation, 50,000 synthetic sand grains and the 1542 real synchrotron microcomputed tomography (SMT) scans of the F50 sand, as well as the granular assemblies composed of synthetic sand grains are made available in an open‐source repository.

Modeling the impact of terrain surface deformation on drag force using discrete element method and empirical formulation

Understanding motion-induced terrain deformation is essential for improving predictive models in geotechnical engineering, infrastructure development, and robotics. The existing analysis assumes a uniform and unchanged environment and lacks a description of the terrain deformation induced by motion, which is particularly significant when the grains are loosely packed. To address this gap, we performed numerical investigations to mathematically model the changes in the surrounding environment and their corresponding effects on resistance. Specifically, we examined the response of granular materials and the energy variation of the particles during motion. Increased energy in the direction of motion was categorized as influences above or below the medium surface. Aligned with the influencing factors, two variables were considered to quantify the impact on the drag force. Building upon the energy analysis and the framework of Resistive Force Theory, we proposed a drag force model for buried motion in loose granular terrain. Compared with the experimental data, the average predictive error decreased from 17.8% to less than 2.7%. Our study provides a feasible approach for incorporating the effects of terrain deformation into a drag force model, thereby enhancing the accuracy and contributing to the development of granular locomotion.

Technological advancements and sustainable practices in rock slope stability – Critical review

Rock slope stability assessment is a vital component of geotechnical engineering, with significant implications for sustainability and environmental management. This review evaluates methodologies and tools used in large-scale rock modeling, analyzing their effectiveness and limitations. By examining recent advancements and commonly used software, the study aims to identify research gaps and enhance current practices. It emphasizes the connection between rock slope stability and sustainable development goals (SDGs), water resource management and climate action. The review critically assesses how existing methodologies integrate sustainability considerations, revealing both strengths and shortcomings in current approaches. It identifies a substantial research gap: the need for more holistic assessment frameworks that balance engineering requirements with ecological and societal impacts. The study also highlights opportunities for innovation in rock slope modeling techniques, particularly in incorporating climate change projections and ecosystem dynamics. By synthesizing findings from diverse case studies, the review offers valuable insights for researchers, practitioners, and policymakers aiming to improve the sustainable slope management. The critical evaluation serves as a foundation for future research directions, stressing the importance of interdisciplinary approaches in addressing complex geotechnical challenges. Ultimately, this review aims to catalyze the development of more sustainable and resilient rock slope assessment methodologies, contributing to the broader goal of harmonizing engineering interventions with environmental stewardship and societal well-being.

Correlation between shear wave velocity (Vs) and penetration resistance along with the stress condition of Eskisehir(Turkey) case

AbstractShear wave velocity (Vs) is an important characteristic in geotechnical earthquake engineering practices for estimating site response. Seismic refraction and well seismicity are the common ways to determine the shear wave velocity. However, these methods require on-site land studies. For this reason, there are empirical approaches that have been proposed to calculate Vs depending on the number of SPT-N obtained from the Standard Penetration experiment data. Studies in the literature have different aspects and correspond to varied empirical approaches. The study aims to establish empirical correlations between the Shear Wave Velocity (Vs), Standard Penetration Test results (SPT-N), and stress conditions of soils considering the soil types of the local sites in Eskişehir, Turkey. The Vs values of the soil from the data set we used in this study were obtained from seismic refraction-reflection studies in 22 different locations in Eskişehir. SPT-N values obtained from 42 boreholes representing the Eskişehir ground were used. The Vs values calculated from the empirical approaches given depending on the SPT-N values and the Vs values measured on the site were compared. In addition, regression analyses were performed between SPT- N and Vs. As a result of the regression analyses performed, new empirical correlations were developed according to soil types. Finally, new empirical correlations are established that can predict Vs at different soil depths and conditions taking the soil type and overburden pressure into account and contributing valuable insights for geotechnical earthquake engineering purpose. The regressions show that there is a good correlation between the Vs-SPT-N along with the total stress with high R2s and soil types are effective on predicting the shear wave velocity.

Identification of damage states of load-bearing rocks using infrared radiation monitoring methods

The online identification of rock damage states is crucial for safety monitoring in geotechnical and mining engineering. By analyzing spatiotemporal evolution patterns of infrared radiation in various rock damage states, we established the first infrared temperature field dataset for rock damage state identification. We then constructed a deep convolutional neural network, RESD-CNN, and performed its training and optimization. Results showed that infrared radiation patterns of different rock samples exhibit similarities. RESD-CNN achieved outstanding performance in identifying rock damage states with metrics of ACC 99.04%, Precision 99.39%, Recall 99.52%, and F1-score 99.46% on the validation set. Generalization tests on datasets of different rock types revealed that RESD-CNN significantly outperformed traditional classification methods, demonstrating the feasibility of infrared radiation technology for intelligent coal rock damage identification. This research provides a crucial foundation for developing online identification and early warning systems for rock damage evolution in engineering.

Enhancing geotechnical damage detection with deep learning: a convolutional neural network approach.

Most natural disasters result from geodynamic events such as landslides and slope collapse. These failures cause catastrophes that directly impact the environment and cause financial and human losses. Visual inspection is the primary method for detecting failures in geotechnical structures, but on-site visits can be risky due to unstable soil. In addition, the body design and hostile and remote installation conditions make monitoring these structures inviable. When a fast and secure evaluation is required, analysis by computational methods becomes feasible. In this study, a convolutional neural network (CNN) approach to computer vision is applied to identify defects in the surface of geotechnical structures aided by unmanned aerial vehicle (UAV) and mobile devices, aiming to reduce the reliance on human-led on-site inspections. However, studies in computer vision algorithms still need to be explored in this field due to particularities of geotechnical engineering, such as limited public datasets and redundant images. Thus, this study obtained images of surface failure indicators from slopes near a Brazilian national road, assisted by UAV and mobile devices. We then proposed a custom CNN and low complexity model architecture to build a binary classifier image-aided to detect faults in geotechnical surfaces. The model achieved a satisfactory average accuracy rate of 94.26%. An AUC metric score of 0.99 from the receiver operator characteristic (ROC) curve and matrix confusion with a testing dataset show satisfactory results. The results suggest that the capability of the model to distinguish between the classes 'damage' and 'intact' is excellent. It enables the identification of failure indicators. Early failure indicator detection on the surface of slopes can facilitate proper maintenance and alarms and prevent disasters, as the integrity of the soil directly affects the structures built around and above it.