Geotechnical Engineering Research Articles

Bioremediation of contaminated soil by crude oil of Baiji refinery by extraction of the local dominant bacteria

Abstract In most communities, soil contamination is a problem as it affects people and the environment. Because oil spills on soil substantially impact the environment, accidental infusions and spills of ore oils frequently result in a complete or partial exchange of the soil pore fluid by oil-contaminated soils, altering the geotechnical engineering characteristics. Therefore, efficiently removing petroleum hydrocarbon pollutants from contaminated soil is urgently needed. A novel technique that is gaining popularity worldwide to clean up places polluted with petroleum hydrocarbons is known as bioremediation. This study browser the fundamental processes involved in bioremediation and removal efficiency of TPH (Total Petroleum Hydrocarbon) within different periods for (the Baiji refinery) which is polluted with crude oil as a result of numerous oil wells, oil drilling, pipeline, and storage tank damage, natural seepage, and spills during the conflict and Noory channel which content crude oil, waste of refinery process and sludge. The research aims to extract the dominant bacteria in the contaminated soil and use the last in bioremediation and find out the differences in its effectiveness in treating total petroleum hydrocarbons (TPH) later. The result indicates that the dominant bacteria are Stutzerimonas balearica and Bacillus subtilis which are used later in bioremediation and can digest TPH. The removal efficiency of TPH during the study period was 55, 65, 68, 78, 82, 85, and 92% for spilled samples and 50, 62, 66, 70, 80, 84, and 90% for the Noory channel, respectively.

Experimental Investigation on Effect of Cementitious Materials on Strength of Sandy Soils

Abstract Weak soil characteristics pose a significant challenge for geotechnical engineers when designing the foundations of a structure. Consequently, researchers resort to improving the properties of weak sandy soil by mixing it with pozzolanic or chemical additives. This study aims to assess the effects of using cementitious materials to enhance the strength and stiffness of loose sand soils. To achieve this goal, experimental work was conducted, involving several laboratory tests to determine the soil properties. Three different curing times were utilized (one day, three days, and seven days), and four various percentages of the added cementitious material were employed (5%, 10%, 15%, and 20%) as a replacement by the weight of the treated soil. The results revealed that 15% is optimum gp content must be added to the mixture for optimum strength and stiffness. And three days is the optimum curing time.

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.

Bio-Electrokinetic Improvement of Deltaic Soil

In addressing problematic soils, geotechnical engineers employ two key strategies: compatibility and improvement. This study focuses on soft and CL deltaic sediments, and seeks to enhance cementation by investigating microbially-induced calcium carbonate precipitation (MICP). Sporosarcina pasteurii bacteria, together with a cementation solution (urea and calcium-containing salt), were electrokinetically injected into deltaic clay soil from the Telar River in Iran. The initial samples, with a dry unit weight (γd) of 12.75 kN/m³, underwent injections in two modes: simultaneous injection of the bacterial and cementation solutions and individual injection in a sequential order. Unconfined compression strength tests and laboratory vane shear tests were conducted to assess changes in soil strength parameters, while a consolidation test was performed to investigate alterations in soil settlement parameters. A comparative analysis with an electroosmosis control sample revealed a remarkable increase in compressive strength and undrained shear strength for MICP bio-electrokinetic improvement. Moreover, the consolidation test demonstrated that the compression index (Cc) and recompression index (Cr) exhibited a more pronounced decline in the simultaneous injection than individual injection. This highlights the dual impact of the bio-electrokinetic method, namely the enhancement of shear strength and the mitigation of settlement in deltaic clay soil. The calcium carbonate content was measured for the samples, and the results indicated a higher degree of participation for the samples subjected to simultaneous injection. Microstructure analyses were conducted on samples, and calcite and vaterite were observed in bio-cemented samples.

Proposing a new sustainable approach for sand improvement using biologically-derived calcium phosphate cement

Bio-mediated soil improvement methods keep on gaining the attention of geotechnical engineers and researchers globally due to their nature-based elegance and eco-friendliness. Most prevalent bio-mediated soil improvement methods include microbial induced carbonate precipitation (MICP) and enzyme induced carbonate precipitation (EICP). During their processes, the bacteria/ free urease hydrolyzes the urea into ammonium and carbonic acid, which is accompanied by a considerable increase of alkalinity (about pH 9.0). The major problem associated with the above techniques is the release of gaseous ammonia that is extremely detrimental. Therefore, this study aims to propose a new sustainable approach involving lactic acid bacteria to facilitate the calcium phosphate mineralization for the strengthening of sand matrix. The major objectives of this investigation are (i) to evaluate the urease activity of the lactic acid bacteria under different temperatures, pH conditions and additions of metal ions, (ii) to assess the treated sand matrix, (iii) to perform cost analysis. The outcomes indicated that Limosilactobacillus sp. could effectively facilitate the urea hydrolysis, hence increase in pH from acidic to neutral, and provided a desirable environment for the calcium phosphate to mineralize within the voids of the sand. The addition of 0.01% Ni2+ in culture media was found to enhance the urease activity by 38.8% and compressive strength over 40%. A combined formation of amorphous- and whisker-like precipitates could bridge a larger area at particle-particle contact points, facilitated a strong force-network in sand matrix. The mineralized calcium phosphate compound was found to be brushite. The cost herein for producing 1L treatment solution was estimated to be about 2.5-folds and 11.8-folds lower compared to that of MICP and EICP treatment solutions, respectively. Moreover, since the treatment pH could potentially be regulated between acidic – neural range, it would greatly control the release of gaseous ammonia. With several environmental and economical benefits, the study has disclosed a new sustainable direction for sand improvement via the use of lactic acid bacteria.

Ecological and geological security risk assessment of underground space development in cold and arid canyon cities-Taking Ping'an District, Haidong City, Qinghai Province, as an example.

The ecological and geological problems caused by the rise of groundwater level due to the development of underground space in cold and arid canyon cities are particularly typical. Reasonably assessing the ecological and geological security risks of utilizing underground space is conducive to reducing the occurrence of ecological and geological problems during the construction and operation of underground engineering projects. Taking Ping'an District of Haidong City as an example, the topography and geomorphology of the research area were investigated in the field, and the distribution of topography and geomorphology in the research area was understood; through geological drilling and geotechnical engineering testing, the distribution of different strata in the research area was obtained; through pumping and seepage experiments, the recharge, runoff, and discharge relationship between surface water and groundwater in the research area and the water abundance of different strata are obtained, and the causes and mechanisms of geological safety risks in forest and grassland ecosystems, farmland ecosystems, and human settlements ecosystems were analyzed based on literature. Corresponding ecological geological safety risk assessment index systems and methods were established, and the ecological geological safety risks before and after the development of underground rail transit projects along both banks of the Huangshui River in the study area were evaluated. PRACTITIONER POINTS: The development of underground space in canyon type cities can easily lead to ecological and geological problems. Taking Haidong City, Qinghai Province, as an example, this study investigates the causes of ecological and geological problems caused by the development of underground spaces in canyon type cities. An ecological geological security risk assessment index system and method for canyon-type cities were established. An evaluation was conducted on the ecological and geological safety risks before and after the development of the underground rail transit projects on both sides of the research area.

Influence of adding gypsum on the properties of lime-stabilized expansive soil

Abstract Expansive soils present major structural and geotechnical engineering problems. Expansive soil undergoes considerable changes in volume because of variations in water content. These variations in volume can occur as either swelling or shrinking and are commonly referred to as swell-shrink soils. Considering key aspects, such as the characteristics of the soil, is essential when dealing with swelling soils. There are expansive soils in both humid areas, where they pose problems with a high plasticity index, and in semi-arid or arid areas, where even moderately swelling soils can cause considerable damage. In order to solve problems related to expansive soil, it is important to stabilize it using various additives like gypsum, lime, and so on. The effects of adding three percentages of gypsum (2%, 4%, and 8%) to 6% lime-stabilized expansive soil to improve its engineering properties are investigated through several tests. The results of tests displayed that adding gypsum has improved properties of prepared soil with 6% lime. The optimum mixture for (gypsum-lime) that gets the highest improvement in soil properties is (6% lime + 4% gypsum). This mixture shows reducing in the free swell, swelling pressure, and plasticity index (from 8.51% to 2.81%, from 715.68 Kpa to 89.18 Kpa, from 33% to 29% respectively). Also, this mixture shows increasing in CBR (from 13.69% to 16.17%) and unconfined compressive strength (UCS) for the curing time of 0, 7, and 14 days (from 264.318 Kpa to 492.131 Kpa, from 380.584 Kpa to 736.318 Kpa, and from 480.192 Kpa to 828.590 Kpa respectively). The maximum dry density (MDD) and optimum moisture content (OMC) increase with increasing gypsum content, While the specific gravity decreases.

Collapse Pattern in Gypseous Soil using Particle Image Velocimetry

Abstract Gypseous soil is prevalent in arid and semi-arid areas, is from collapsible soil, which contains the mineral gypsum, and has variable properties, including moisture-induced volume changes and solubility. Construction on these soils necessitates meticulous assessment and unique designs due to the possibility of foundation damage from soil collapse. The stability and durability of structures situated on gypseous soils necessitate close collaboration with specialists and careful, methodical preparation. It had not been done to find the pattern of failure in the micromechanical behavior of gypseous sandy soil through particle image velocity (PIV) analysis. This adopted recently in geotechnical engineering to track the motion of soil grains and using tracer particles by applying digital particle image analysis. It has also been used to study the displacement distribution in some cases of granular materials. Therefore, the goal of this study is to find out how gypseous sand medium moves when in contact with a rigid strip foundation that is under static stress and plane strain conditions. The experimental model would focus on two common types of wetting, namely water table rise and dry conditions. The PIV showed that the collapse pattern under the footing is of the type of punching shear failure. The predominant mechanism of soil deformation was the vertical compression of the gypseous granular soil. The results showed that understanding gypseous sandy grain displacement and failure patterns at the local scale is crucial for enhancing the design of foundations under static stress conditions.

Hybrid soil strength prediction model for geotechnical ground investigation using convolutional neural network and ensemble learning

Abstract A hybrid model of soil strength prediction from the image data and feature data of soil was proposed, which is aimed at providing an efficient solution in geotechnical ground investigation by leveraging the power of AI. In this model, CNN purely with the encoder was used to establish the relationship between image data and strength parameters of soil, while ensemble learning stacked of three base learners, i.e., KNN, LASSO, and LSVM, was applied to the feature data. Then, the CNN part and the ensemble learning part were integrated into the loss function. Results show that the convergence of the hybrid model was slower than that of the ensemble learner. However, with the aid of soil image which has more features of soil in essence, the predicted soil strengths by the hybrid model better matched with the actual ones, compared with the ensemble learner and base learners. This hybrid model offers an effective framework of multi-modal data fusion for geotechnical engineering, by leveraging the synergy of the high-dimensional feature extraction capabilities of CNN and the generalization abilities of ensemble learning.

A two‐phase volume of fluid approach to model rigid‐perfectly plastic granular materials

AbstractGranular flow problems characterized by large deformations are widespread in various applications, including coastal and geotechnical engineering. The paper deals with the application of a rigid‐perfectly plastic two‐phase model extended by the Drucker–Prager yield criterion to simulate granular media with a finite volume flow solver (FV). The model refers to the combination of a Bingham fluid and an Eulerian strain measure to assess the failure region of granular dam slides. A monolithic volume‐of‐fluid (VoF) method is used to distinguish between the air and granular phases, both governed by the incompressible Navier–Stokes equations. The numerical framework enables modeling of large displacements and arbitrary shapes for large‐scale applications. The displayed validation and verification focuses on the rigid‐perfectly plastic material model for noncohesive and cohesive materials with varying angles of repose. Results indicate a good agreement of the predicted soil surface and strain results with experimental and numerical data.

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.

Enhancing soil liquefaction risk assessment with metaheuristics and hybrid learning techniques

ABSTRACT Soil liquefaction has garnered significant research attention over several decades due to its profound impact on infrastructure systems in earthquake-prone regions. Given the limitations inherent in the assumptions and approximations of conventional methods, machine learning has emerged as a robust and efficient approach for predicting soil liquefaction potential. This study aims to develop a synergistic JS-CNN-XGB model, unifying the Jellyfish Search (JS) optimizer with a hybrid deep/machine learning model. This amalgamation leverages the feature extraction capabilities of the Convolutional Neural Network (CNN) alongside the classification prowess of the eXtreme Gradient Boosting (XGB) algorithm. The proposed model seamlessly integrates into a user-friendly prediction system, streamlining the liquefaction prediction process. Validation is achieved through case studies involving historical earthquake recordings with diverse classification ratios and varying input attribute quantities. Compared to existing studies, our proposed system showcases a notable 6.2% enhancement in overall liquefaction assessment accuracy, demonstrating improved detection capabilities in imbalanced and balanced datasets. In conclusion, this study underscores an automated system that presents a robust solution to the challenges posed by liquefaction potential assessment in geotechnical engineering.

Deep Learning and Histogram-Based Grain Size Analysis of Images.

Grain size analysis is used to study grain size and distribution. It is a critical indicator in sedimentary simulation experiments (SSEs), which aids in understanding hydrodynamic conditions and identifying the features of sedimentary environments. Existing methods for grain size analysis based on images primarily focus on scenarios where grain edges are distinct or grain arrangements are regular. However, these methods are not suitable for images from SSEs. We proposed a deep learning model incorporating histogram layers for the analysis of SSE images with fuzzy grain edges and irregular arrangements. Firstly, ResNet18 was used to extract features from SSE images. These features were then input into the histogram layer to obtain local histogram features, which were concatenated to form comprehensive histogram features for the entire image. Finally, the histogram features were connected to a fully connected layer to estimate the grain size corresponding to the cumulative volume percentage. In addition, an applied workflow was developed. The results demonstrate that the proposed method achieved higher accuracy than the eight other models and was highly consistent with manual results in practice. The proposed method enhances the efficiency and accuracy of grain size analysis for images with irregular grain distribution and improves the quantification and automation of grain size analysis in SSEs. It can also be applied for grain size analysis in fields such as soil and geotechnical engineering.

Calculation Method for Particle Self-Regulation Servo Based on Discrete Element Method

Particle Flow Code, as noncontinuous media calculation software, has found extensive applications in geotechnical engineering research. To simulate real physical processes, the commonly utilized servo-computing methods involve adjusting the boundaries to modify the model’s stress state. However, this approach alters the model’s initial boundaries, thereby compromising computational accuracy. Addressing this issue, this paper introduces a particle self-regulation servo-computing method. This method controls the internal particle self-adjustment and completes the servo through error analysis. On the basis of the error analysis of the average stress within the particle system and the servo stress during the iterative process, the method automatically calculates the change in particle size within the system, achieving rapid convergence to servo stress tolerance after finite number of iterations. Validation through uniaxial compression tests and complex slope cases indicates that this particle self-regulation servo method yields an error of less than 5% compared with conventional rigid servo methods. That is, it accurately achieves numerical simulation state control without altering the outer contours of the boundary model. The proposed computational method is applicable to servo calculations for irregular models in two or three dimensions, providing a viable approach for stress control in models under complex conditions.

Stress-dependent Mohr–Coulomb shear strength parameters for intact rock

Rock strength is imperative for the design and stability analysis of engineering structures. The Mohr–Coulomb (M-C) criterion holds significant prominence in geotechnical engineering. However, the M-C criterion fails to accurately capture the nonlinear strength response and neglects the critical state of rocks, potentially leading to inaccuracies in the design phase of deep engineering projects. This study introduces an innovative stress-dependent friction angle and cohesion (SFC) for the M-C criterion to capture the nonlinear strength responses of intact rocks, spanning from non-critical to critical states (brittle to ductile regions). A novel method for determining these stress-dependent parameters at each corresponding σ3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma_{3}$$\\end{document} is initially introduced. Subsequently, an examination of the confinement dependency of the friction angle and cohesion is conducted, leading to the derivation of the SFC model. The SFC-enhanced M-C criterion, utilizing parameters obtained from triaxial tests under lower σ3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma_{3}$$\\end{document}, demonstrates the capability to delineate the complete non-linear strength envelope from brittle to ductile regions. Validation through triaxial test data confirms that the predictions of the SFC-enhanced M-C criterion accurately correspond to the strength characteristics of the tested rocks.

Efficient prediction of compressive strength in geotechnical engineering using artificial neural networks

Efficient prediction of compressive strength in geotechnical engineering using artificial neural networks

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.

Future-proofing geotechnics workflows: accelerating problem-solving with large language models

ABSTRACT The integration of Large Language Models (LLMs), such as ChatGPT, into the workflows of geotechnical engineering has a high potential to transform how the discipline approaches problem-solving and decision-making. This paper investigates the practical uses of LLMs in addressing geotechnical challenges based on opinions from a diverse group, including students, researchers, and professionals from academia, industry, and government sectors gathered from a workshop dedicated to this study. After introducing the key concepts of LLMs, we present preliminary LLM solutions for four distinct practical geotechnical problems as illustrative examples. In addition to the basic text generation ability, each problem is designed to cover different extended functionalities of LLMs that cannot be achieved by conventional machine learning tools, including multimodal modelling under a unified framework, programming ability, knowledge extraction, and text embedding. We also address the potentials and challenges in implementing LLMs, particularly in achieving high precision and accuracy in specialised tasks, and underscore the need for expert oversight. The findings demonstrate the effectiveness of LLMs in enhancing efficiency, data processing, and decision-making in geotechnical engineering, suggesting a paradigm shift towards more integrated, data-driven approaches in this field.

Evaluating ground vibration attenuation through leca‐filled trenches: A support vector machine approach

AbstractThis paper investigates the effect of Leca‐filled barriers, both single and double‐walled trenches, on mitigating ground vibrations due to harmonic loads. A three‐dimensional finite element program, validated in comparison by aforementioned studies, was used alongside automated models created via Plaxis and Python integration. This approach facilitated the evaluation of trench effectiveness in both active and passive design scenarios. Our findings suggest that optimal trench dimensions for effective vibration reduction in active designs are a depth and width of approximately 1λr and 0.2λr, respectively. In passive designs, while trench depth becomes less significant, width plays a crucial role in both single and double‐wall systems. Additionally, a support vector machine algorithm was developed to forecast the performance of single‐wall trenches, showing a high correlation with numerical model outcomes. This underscores the algorithm's utility in predicting trench efficiency, highlighting the practical application of machine learning in geotechnical engineering.

Reassessment of natural expansive materials and their impact on freeze-thaw cycles in geotechnical engineering: a review

Reassessment of natural expansive materials and their impact on freeze-thaw cycles in geotechnical engineering: a review