Geotechnical Engineering Applications Research Articles

A Review of Particle Packing Models and Their Applications to Characterize Properties of Sand-Silt Mixtures

This paper reviews particle packing models and explores their application in geotechnical engineering, specifically for sand-silt mixtures. The review covers key models, including limiting case, linear, and non-linear packing models, focusing on their mathematical structures, physical principles, assumptions, and limitations through the concept of excess free volume. The application of particle packing models in geotechnical engineering is explored in characterizing the properties of sand-silt mixtures, offering insights into maximum, minimum, and critical void ratios and inter-granular void ratio, and the prediction of mechanical properties.

The effect of biopolymer stabilisation on biostimulated or bioaugmented mine residue for potential technosol production

Mine waste can be transformed into technosol as an ecological strategy. Despite its importance to soil functions, biological activity is often overlooked. Biopolymers can serve as innovative tools for bioremediation, facilitating chemical reactions and creating networks to encapsulate contaminants. This work aims to assess the use of bioleached and stabilised residues from a tungsten mine for technosol production. The first objective was to evaluate mine tailings for their bioleaching potential by biostimulation or bioaugmentation with strain Diaphorobacter polyhydroxybutyrativorans B2A2W2. The second was to evaluate the effect of Portland cement or biopolymers such as Carboxymethyl Cellulose (CMC) or Xanthan Gum (XG) on the stabilisation of bioleached residues. The impact of biopolymers on residues’ characteristics, such as metal leaching, number of cultivable microorganisms, compression strength and ecotoxicity was evaluated using flow systems. Over time, bioleached metallic elements decreased, except for iron (Fe). Biostimulated and stabilised residues exhibited similar trends; both CMC and cement showed low leaching rates and viable microorganisms in the same order (106 CFU × ml−1). However, bioaugmented residue stabilised with XG showed 106 CFU × ml−1 viable microorganisms and increased 2.2-fold Fe leaching than BA_Control. CMC addition to bioaugmented residue reduced 5.9-fold Fe leaching and increased 100-fold viable microorganisms. By utilising both biological and engineering approaches to characterise the technosol, this study contributes to advancing knowledge of technosol production. The residues biostimulated and stabilised with CMC produced a material useful for bio-applications, with low toxicity and metal leaching, useful for bio-applications. XG was the best stabiliser for geotechnical engineering applications, with improved compression strength. In conclusion, the study demonstrates the usefulness of biopolymer treatment for residues and emphasises the importance of selecting the appropriate biopolymer for the intended function of technosols.

Conceptual and Applied Aspects of Water Retention Tests on Tailings Using Columns

The water retention capacity of porous materials is crucial in various geotechnical and environmental engineering applications such as slope stability analysis, landfill management, and mining operations. Filtered tailings stacks are considered an alternative to traditional tailings dams. Nevertheless, the mechanical behaviour and stability of the material under different water content conditions are of concern because these stacks can reach considerable heights. The water behaviour in these structures is poorly understood, particularly the effects of the water content on the stability and potential for liquefaction of the stacks. This study aims to investigate the water retention and flow characteristics of compacted iron ore tailings in high columns to better understand their hydromechanical behaviour. The research used 5 m high columns filled with iron ore tailings from the Quadrilátero Ferrífero region in Minas Gerais, Brazil. The columns were prepared in layers, compacted, and instrumented with moisture content sensors and suction sensors to monitor the water movement during various stages of saturation, drainage, infiltration, and evaporation. The sensors provided consistent data and revealed that the tailings exhibited high drainage capacity. The moisture content and suction profiles were effectively established over time and revealed the dynamic water retention behaviour. The comparison of the data with the theoretical soil water retention curve (SWRC) demonstrated a good correlation which indicates that there was no hysteresis in the material response. The study concludes that the column setup effectively captures the water retention and flow characteristics of compacted tailings and provides valuable insights for the hydromechanical analysis of filtered tailings stacks. These findings can significantly help improve numerical models, calibrate material parameters, and contribute to the safer and more efficient management of tailings storage facilities.

APD: An automated parameter determination system based on in-situ tests

In-situ testing has numerous applications in geotechnical engineering. The interpretation of in-situ test results includes soil stratification and determination of soil parameters. This paper presents an automated parameter determination framework that aims to determine constitutive model parameters based on in-situ tests. The ongoing research project relies on a graph-based approach for determining the parameters. The framework has two main attributes: transparency and adaptability. Transparency is achieved by illustrating how a certain parameter was computed. Adaptability is ensured by allowing users to incorporate their expertise into the framework. The system currently determines parameters based on three main workflows that utilize the results of cone penetration tests, dilatometer tests, and shear wave velocity measurements. This study employs the three main workflows to determine soil parameters for one of the Norwegian GeoTest Sites. Additionally, the connection between the parameter determination system and finite element analysis is discussed, where the parameters for the Modified Cam Clay model are evaluated. The framework is valuable in the early stages of projects, providing detailed soil information when soil data is limited. Ongoing research aims to assess the accuracy of the derived soil and constitutive model parameters and to expand the system’s capabilities by including additional in-situ tests.

Derivation of site-specific soil-water characteristic curve (SWCC) from extremely sparse experimental data by hierarchical Bayesian method with consideration of geotechnical sites similarity

Soil-water characteristic curves (SWCCs) play a crucial role in understanding soil behavior related to water movement and soil moisture effects, rendering them an essential tool in engineering geology and geotechnical engineering applications. Traditionally, SWCCs are determined through labor-intensive laboratory experiments involving varying levels of suction, a process that can take several months. Moreover, obtaining a high-quality SWCC from numerous measurements becomes particularly challenging when immediate site-specific data are required for design and analysis. This paper introduces a hierarchical Bayesian method for deriving site-specific SWCCs by integrating extremely sparse data (e.g., one or two measurements) for the site of interest with existing data from sites with similar geological and sedimentary characteristics. The SWCC parameters are estimated using a Bayesian framework and Markov chain Monte Carlo simulations. This approach not only enables the derivation of accurate SWCCs but also helps quantify the associated uncertainties. The effectiveness of the proposed method is demonstrated using both numerical and real-world data from different types of loess and unsaturated soils in the unsaturated soil database (UNSODA). The results show that site-specific SWCCs of unsaturated soils can be accurately estimated from sparse measurements by incorporating information from similar sites. This work offers an efficient and reasonably accurate approach for deriving SWCCs of unsaturated soils for geotechnical applications, especially when the number of site-specific SWCC measurement is extremely sparse and limited.

Active learning inspired multi-fidelity probabilistic modelling of geomaterial property

The identification of geomaterial properties is costly but pivotal for engineering design. A wide range of approaches perform well with sufficiently measured data but their performance is problematic for sparse data. To address this issue, this study proposes an active learning based multi-fidelity residual Gaussian process (AL-MR-GP) modelling framework. A low-fidelity (LF) prediction model is first trained using extensive LF data collected from worldwide sites to generate preliminary estimations. Subsequent training employs active learning to prioritize high-fidelity data from the specific site of interest with larger information gain for calibrating the LF model to make ultimate predictions. The compression index of clays is selected as an example to examine the capability of the proposed framework. The results indicate that using the same number of site-specific datasets, the compression index of clays can be well captured by AL-MR-GP, exhibiting superior accuracy and reliability than models without incorporating multi-fidelity data or active learning. Based on unified LF data, the proposed framework becomes data-efficient for the model development of three sites and is significantly competitive in extrapolation, compared with site-specific models even with active learning. These promising characteristics indicate substantial potential to be extended to broader applications in geotechnical engineering.

Influence of Foam Content and Concentration on the Physical and Mechanical Properties of Foam Concrete

Foam concrete has been used in various real-life applications for decades. Simple manufacturing methods, lightweight, high flowability, easy transportability, and low cost make it a useful construction material. This study aims to develop foam concrete mixtures for various civil and geotechnical engineering applications, such as in-fill, wall backfill and soil replacement work. A blended binder mix containing cement, fly ash and silica fume was produced for this study. Its compressive strength performance was compared against conventional general purpose (GP) cement-based foam concrete. Polypropylene (PP) fibre was used for both mixtures and the effect of various percentages of foam content on the compressive strength was thoroughly investigated. Additionally, two types of foaming agents were used to examine their impact on density, strength and setting time. One foaming agent was conventional, whereas the second foaming agent type can be used to manufacture permeable foam concrete. Results indicate that an increase in foam content significantly decreases the strength; however, this reduction is higher in GP mixes than in blended mixes. Nevertheless, the GP mixes attained two times higher compressive strength than the blended mix’s compressive strengths at any foam content. It was also found that the foaming agent associated with creating permeable foam concrete lost its strength (reduced by more than half), even though the density is comparable. The compressive stress–deformation behaviour showed that densification occurs in foam concrete due to its low density, and fibres contributed significantly to crack bridging. These two effects resulted in a long plateau in the compressive stress–strain behaviour of the fibre-reinforced foam concrete.

Enhancing deep learning-based slope stability classification using a novel metaheuristic optimization algorithm for feature selection

The evaluation of slope stability is of crucial importance in geotechnical engineering and has significant implications for infrastructure safety, natural hazard mitigation, and environmental protection. This study aimed to identify the most influential factors affecting slope stability and evaluate the performance of various machine learning models for classifying slope stability. Through correlation analysis and feature importance evaluation using a random forest regressor, cohesion, unit weight, slope height, and friction angle were identified as the most critical parameters influencing slope stability. This research assessed the effectiveness of machine learning techniques combined with modern feature selection algorithms and conventional feature analysis methods. The performance of deep learning models, including recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and generative adversarial networks (GANs), in slope stability classification was evaluated. The GAN model demonstrated superior performance, achieving the highest overall accuracy of 0.913 and the highest area under the ROC curve (AUC) of 0.9285. Integration of the binary bGGO technique for feature selection with the GAN model led to significant improvements in classification performance, with the bGGO-GAN model showing enhanced sensitivity, positive predictive value, negative predictive value, and F1 score compared to the classical GAN model. The bGGO-GAN model achieved 95% accuracy on a substantial dataset of 627 samples, demonstrating competitive performance against other models in the literature while offering strong generalizability. This study highlights the potential of advanced machine learning techniques and feature selection methods for improving slope stability classification and provides valuable insights for geotechnical engineering applications.

The Uses of Lightweight Material in Civil Engineering : A Review

A lightweight aggregate (LWA) is a group of aggregates with a relative density lower than normal density aggregates (natural sand, gravel, and crushed stone), sometimes referred to as low density aggregate. It is utilized for its reduced weight and exceptional qualities in sound reduction, fire resistance, insulation, and geotechnical applications LWA, or lightweight aggregate, is a cutting-edge construction material that is employed to decrease the overall weight of tall structures. Despite being lighter, it possesses a strength that is comparable to that of regular concrete. In recent times, there has been a significant surge in the use of lightweight aggregate in the field of building, mostly because of its exceptional performance in seismic situations. In addition, substituting natural aggregate with other industrial by-products and trash enables us to minimize the adverse environmental consequences. Laser wavelength ablation (LWA) has also been employed to address a wide range of geotechnical engineering challenges, including the transformation of soft and unstable soil into a suitable and usable property. Lightweight fillings have a weight that is around 50% less than fills made using typical materials. The load reduction, along with the high internal friction angle of the lightweight aggregate, can decrease vertical and lateral pressures by almost 50%, potentially resulting in less settling. This literature study examines the use of lightweight materials, including LECA, Bonza, and Thermostone, in geotechnical engineering applications. The results demonstrate a significant emphasis on improving slope stability and minimizing stresses on susceptible soils. Nevertheless, several knowledge gaps exist regarding the efficacy of these materials under challenging conditions and when subjected to dynamic loads. A growing inclination towards using these lightweight aggregates is observed because of their advantageous environmental and mechanical properties. Further investigation is needed to examine the extended-term performance and interactions of these materials with different soil types in order to enhance their performance in diverse geotechnical situations.

Three-Dimensional Spectral Element Method Implementation for Evaluating Rooted Soil Behavior in Slope Stability Analysis

Bioengineering techniques are being increasingly adopted as a sustainable solution to soil slope instability. Despite their recognized benefits, however, the mechanistic contribution of vegetation to slope stability remains inadequately understood due to the intricate nature of soil–root interactions and the complexity of root architectures. Most existing research predominantly offers qualitative assessments of vegetation effectiveness. This study aims to numerically substantiate the role of vegetation as a bioengineering technique for soil slope stabilization. Various plant species used commonly for soil stabilization were identified, and undisturbed soil samples were collected to quantify the shear strength parameters of the soils from both barren and vegetated slopes, along with root tensile strengths. A comprehensive topographic survey was conducted to capture the precise topography of the study area. Utilizing these primary data, in this work we develop 3D models for five representative plants within each species category and employ the Spectral Element Method (SEM), an advanced higher-order formulation of the finite element method (FEM), within the 3D domain to evaluate the factor of safety for the soil slopes. The SEM offers superior accuracy and stability in numerical computations. The results obtained through the SEM were corroborated through the FEM modeling and were found to be consistent with other established methodologies. This innovative approach of 3D SEM aims to quantitatively assess the impact of vegetation on soil slope stability and provide a more rigorous understanding of bioengineering applications in geotechnical engineering.

Efficient predictive modeling of resilient modulus in stabilized clayey soil using automated machine learning

Pavement subgrade design relies on the resilient modulus (Mr) to analyze structural response to vehicle-like loading. Adding stabilizers to the soil subgrade makes estimating Mr difficult and resource-intensive. This study uses an automated machine learning (ML) strategy to predict the Mr of stabilized clayey soil using recycled plastic waste. The proposed method automates model selection and hyperparameter tuning, making it a feasible alternative to tedious ML modeling and costly laboratory testing. From extensive laboratory investigation involving 3285 experimental data points, the automated ML model using Bayesian optimization evaluates ensembles, support vector machine (SVM), neural network (NET), decision trees, (TREE), and Gaussian process (GP) regression models, identifying the best model based on cross-validation mean squared error (MSE). Bayesian optimization explores hyperparameter spaces to find optimal configurations, enhancing the accuracy, scalability, and reliability of the prediction model. The optimization process yielded the best results for the ensemble least square boost (LSBoost) model with a cross-validation mean squared error (MSE) value of 6.723×10−29. The optimized ML model’s performance is measured using R2 and adjusted R2. The LSboost model’s R2 and adjusted R2 values of 0.9999 suggested overfitting, prompting further investigations using performance metrics like root mean squared error (RMSE), mean absolute error (MSE), and probability density function (PDF) for normalized absolute error (NAE) for training and testing datasets for the predictive ML model. The small RMSE and MAE (0.0049 and 0.0005) values and symmetrical NAE distribution of the proposed ML model demonstrate its high accuracy and generalization capabilities. The proposed model was subsequently tested on the new, unseen data and achieved predictions with an error rate of 0.24 %. This confirms the proposed ML model’s superiority over conventional deformation models, making it ideal for reliable geotechnical engineering applications.

Characterization of embankment material using sand tire mix as additive and validating the results obtained from large scale direct shear test in Abaqus software

Laboratory direct shear tests often overlook larger particles due to limitations in shear box design, resulting in an inadequate portrayal of real-world soil conditions. To address this gap, the use of large-scale direct shear apparatus capable of accommodating larger particles is essential. However, conducting such tests with varied sizes of tire shreds, mixing ratios, and loads is intricate and resource-intensive. Therefore, the utilization of numerical modeling is suggested to simulate sand-tire mixtures under diverse conditions. This study presents conclusions derived from comparing numerical results with experimental observations, demonstrating a close correspondence between the two. It offers a comprehensive examination of the impact of sand and shredded tires mixtures on shear strength, indicating a consistent pattern: increased quantities of tire shreds correspond to heightened friction angles. Furthermore, specimens containing larger shredded tire particles display elevated friction angles, implying a possible connection between particle size and frictional behavior. This investigation underscores the importance of numerical modeling as a cost-efficient and effective approach to complement experimental inquiries, enriching our comprehension of soil-tire mixture dynamics and guiding practical applications in transportation and geotechnical engineering.

Predicting bentonite swelling pressure: optimized XGBoost versus neural networks

The swelling pressure of bentonite and bentonite mixtures is critical in designing barrier systems for deep geological radioactive waste repositories. Accurately predicting the maximum swelling pressure is essential for ensuring these systems' long-term stability and sealing characteristics. In this study, we developed a constrained machine learning model based on the extreme gradient boosting (XGBoost) algorithm tuned with grey wolf optimization (GWO) to determine the maximum swelling pressure of bentonite and bentonite mixtures. A dataset containing 305 experimental data points was compiled, including relevant soil properties such as montmorillonite content, liquid limit, plastic limit, plasticity index, initial water content, and soil dry density. The GWO-XGBoost model, incorporating a penalty term in the loss function, achieved an R2 value of 0.9832 and an RMSE of 0.5248 MPa in the testing phase, outperforming feed-forward and cascade-forward neural network models. The feature importance analysis revealed that dry density and montmorillonite content were the most influential factors in predicting maximum swelling pressure. While the developed model demonstrates high accuracy and reliability, it may have limitations in capturing extreme values due to the complex nature of bentonite swelling behavior. The proposed approach provides a valuable tool for predicting the maximum swelling pressure of bentonite-based materials under various conditions, supporting the design and analysis of effective barrier systems in geotechnical engineering applications.

Microcrystalline Cellulose-A Green Alternative to Conventional Soil Stabilizers.

Biopolymers are polymers of natural origin and are environmentally friendly, carbon neutral and less energy-intense additives that can be used for various geotechnical applications. Biopolymers like xanthan gum, carrageenan, chitosan, agar, gellan gum and gelatin have shown potential for improving subgrade strength, erosion resistance, and as canal liners and in slope stabilization. But minimal research has been carried out on cellulose-based biopolymers, particularly microcrystalline cellulose (MCC), for their application in geotechnical and geo-environmental engineering. In this study, the effect of MCC on select geotechnical properties of kaolin, a weak, highly compressible clay soil, like its liquid and plastic limits, compaction behavior, deformation behavior, unconfined compression strength (UCS) and aging, was investigated. MCC was used in dosages of 0.5, 1.0, 1.5 and 2% of the dry weight of the soil, and the dry mixing method was adopted for sample preparation. The results show that the liquid limit increased marginally by 11% but the plasticity index was nearly 74% higher than that of untreated kaolin. MCC rendered the treated soil stiffer, which is reflected in the deformation modulus, which increased with both dosage and age of the treated sample. The UCS of kaolin increased with dosage and curing period. The maximum UCS was observed for a dosage of 2% MCC at a 90-day curing period. The increase in stiffness and strength of the treated kaolin with aging points out that MCC can be a potential soil stabilizer.

Compilation of Consolidation Properties Data of Champlain Sea Clay from Ottawa Region

Estimation of consolidation settlements in fine-grained soils due to various civil infrastructure loads is traditionally based on results derived from consolidation tests performed on undisturbed soil samples, combined with the data of other soil properties. In many geotechnical engineering applications, consolidation settlements are also estimated using empirical consolidation parameters derived from basic soil properties. This approach relies on correlations from the literature to bypass the time-consuming and expensive sampling techniques, laboratory testing, and other associated expenses. However, these correlations may not provide reasonable consolidation settlement estimations as these correlations are typically developed without considering the influence of stress history, geology, salinity of pore water, gradation, soil fabric, and chemical properties of the soils. This is especially true for Champlain Sea clay deposits from Eastern Ontario region of Canada that are typically with heterogeneous site conditions and exhibit spatial variability of soil properties. In this paper, data from the published literature and industrial and government reports on sensitive Champlain Sea clays were gathered for the Ottawa region. The data collection and clean-up methodology towards enhancing the reliability of the gathered data is comprehensively discussed. The summarized data from this study can be used with a greater degree of confidence towards developing reliable correlations in the estimation of consolidation settlements in geotechnical engineering practice.

Assessment and development of empirical correlations between flat plate dilatometer and cone penetration tests for Auckland soils

ABSTRACT Based on a database of over 2900 cone penetration test (CPT) and flat plate dilatometer test (DMT) data pairs obtained from Auckland, New Zealand, this paper assesses the applicability of several empirical CPT-DMT correlations and presents newly developed regional correlations for use in geotechnical engineering applications. Empirical correlations using the DMT-based material index are assessed. As both the DMT-based material index (ID ) and the soil behaviour type index (Ic ) from CPT data are often used to estimate soil behaviour characteristics when no samples are retrieved, these were used to develop subset databases. In general, the correlation used to estimate the material index (ID ) tended to slightly overestimate the measured values for Auckland soils, while better performance could be observed after removing data points where Ic and ID soil classifications were incompatible. In contrast, the correlations used to estimate horizontal stress index (KD ), and dilatometer modulus (ED ) underestimated the measured values for the database used herein, with the largest underestimates for low cone tip resistance values. This paper presents new correlations for estimating KD and ED based on CPT parameters for Auckland soils, providing improved estimates, with stable bias values across the range of input parameters considered.

The Effect of Oil Contaminated on Collapse Pattern in Gypseous Soil Using Particle Image Velocimetry and Simulation

Gypseous soil covers approximately 30% of Iraqi lands and is widely used in geotechnical and construction engineering as it is. The demand for residential complexes has increased, so one of the significant challenges in studying gypsum soil due to its unique behavior is understanding its interaction with foundations, such as strip and square footing. This is because there is a lack of experiments that provide total displacement diagrams or failure envelopes, which are well-considered for non-problematic soil. The aim is to address a comprehensive understanding of the micromechanical properties of dry, saturated, and treated gypseous sandy soils and to analyze the interaction of strip base with this type of soil using particle image velocimetry (PIV) measurement and Plaxis 3D simulation. The results showed that high-resolution digital cameras captured soil deformation using PIV, displacement fields, and velocity vectors were generated, which helped identify different sand movement zones. Further, PIV showed punching and general shear failure in uncontaminated and soaked contaminated gypsum soils, respectively. Moreover, the Plaxis results corresponded well with the PIV, as material behavior models are essentially simplified representations of the actual behavior of footing and soil. Understanding soil deformation behavior is crucial for accurate engineering calculations and designs, making these findings valuable for geotechnical and construction engineering applications. Doi: 10.28991/CEJ-2024-010-07-016 Full Text: PDF

Retraction: The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering

Retraction: The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering

Biopolymers in geotechnical engineering for soil improvement

AbstractSeveral biopolymer applications in geotechnical engineering have been adopted in recent years, notably dust control, soil strengthening, and erosion control. Although biopolymer soil treatment approaches can assure engineering efficiency while satisfying environmental protection standards, this technology requires more validation regarding site adaptability, durability, and economic feasibility. The influence of biopolymers on soil behavior is discussed within geotechnical engineering applications and practices, including soil consistency limits, strength and deformation parameters, hydraulic conductivity, soil-water properties, and erosion prevention.Laboratory studies were performed to confirm the behavior of the treated soil, including Atterberg limits, proctor, and direct shear tests utilizing two types of biopolymers: guar gum and xanthan gum.

Comprehensive Review of Geotechnical Engineering Properties of Recycled Polyethylene Terephthalate Fibers and Strips for Soil Stabilization.

The waste management of plastic has become a pressing environmental issue, with polyethylene terephthalate (PET) being one of the major contributors. To address this challenge, the utilization of recycled PET fibers and strips in geotechnical engineering applications for soil stabilization has gained considerable attention. This review aims to provide a comprehensive study of the geotechnical engineering properties of recycled-PET-reinforced soils. The review examines various factors influencing the performance of PET-reinforced soils, including PET percent content, fiber length, and aspect ratio. It evaluates the mechanical properties, like shear strength, compressibility, bearing capacity, hydraulic behavior, and durability of recycled-PET-reinforced soils. The findings reveal PET reinforcement enhances shear strength, reduces settlement, and increases the bearing capacity and stability of the soil. However, it is observed that the incorporation of recycled PET fibers and strips does not lead to a significant impact on the dry density of the soil. Finally, an environmental and cost comparison analysis of recycled PET fibers and strips was conducted. This review serves as a valuable resource for researchers, engineers, and practitioners involved in the field, offering insights into the geotechnical properties of PET-reinforced soils and outlining future research directions to maximize their effectiveness and sustainability.