Geotechnical Engineering Applications Research Articles

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.

Macro-micro mechanical behavior of saturated cemented sands during drained triaxial shearing

To enhance the understanding of the macro-micro mechanical behavior of cemented sands, a series of drained triaxial shearing tests were conducted on saturated cemented sands with varying cement contents and confining stresses. These tests incorporated high-performance acoustic emission (AE) monitoring and an environmental scanning electron microscope (ESEM) technique. The results yielded quantitative interpretations of macro-mechanical response, microstructural variation, and AE behaviors. Specifically, increasing cement content led to an exponential reduction in porosity (or pore area ratio) and a power increase in pore number, possibly due to the production of hydration products that segment large pores. Relationships were established among peak strength (or AE hits), cement content, and confining stress. However, AE trends diverged from those observed in peak strength due to microstructural modifications under different cement contents and their resulting differences in micromechanical behavior during shearing. General relationships between AE rate and mechanical parameters were established during the initial shearing stage, regardless of cement content and confining stress. Moreover, AE evolution efficiently characterized subtle variations in failure patterns due to its high sensitivity to failure-induced structural modifications. These contributions have the potential to establish a non-destructive field evaluation framework and provide guidelines for the efficient utilization of cemented soils in geological and geotechnical engineering applications.

Utilizing Machine Learning for Cone Penetration Test-Based Soil Classification

The cone penetration test (CPT) is widely used in geotechnical engineering to assess soil properties. Traditional methods of interpreting CPT data and classifying soils have limitations and are time-consuming. Machine learning (ML) algorithms offer a data-driven approach to automate and improve soil classification based on CPT data. In this study, the applicability of ML techniques was investigated to measure the reliability of soil classification prediction using raw CPT data. A dataset comprising raw CPT data and corresponding soil classifications derived from the adjacent boreholes was prepared for training and testing the selected ML techniques. Five ML algorithms, namely logistic regression, the support vector machine, the random forest (RF), K-nearest neighbors (KNN), and extreme gradient boosting (XGBoost), were applied. The results showed that the RF algorithm outperformed other ML methods, achieving an F1-score of 0.896. Comparing the performance of different algorithms, the RF consistently showed the best results, followed by XGBoost and KNN. These findings highlight the potential of ML algorithms, particularly the RF, in accurately predicting soil classification based on CPT data, thus improving the efficiency and reliability of geotechnical engineering applications.

Mechanical strength investigation of chemically reinforced sandy soil using organic copolymers for geotechnical engineering applications

The chemical reinforcement of sandy soils is usually carried out to improve their properties and meet specific engineering requirements. Nevertheless, conventional reinforcement agents are often expensive; the process is energy-intensive and causes serious environmental issues. Therefore, developing a cost-effective, room-temperature-based method that uses recyclable chemicals is necessary. In the current study, poly (styrene-co-methyl methacrylate) (PS-PMMA) is used as a stabilizer to reinforce sandy soil. The copolymer-reinforced sand samples were prepared using the one-step bulk polymerization method at room temperature. The mechanical strength of the copolymer-reinforced sand samples depends on the ratio of the PS-PMMA copolymer to sand. The higher the copolymer-to-sand ratio, the higher the sample’s compressive strength. Sand (70 wt.%)-PS-PMMA (30 wt.%) sample exhibited the highest compressive strength of 1900 psi. The copolymer matrix enwraps the sand particles to form a stable structure with high compressive strengths.

Comparative study on convolutional neural network and regression analysis to evaluate uniaxial compressive strength of Sandy Dolomite

Sandy Dolomite is a kind of widely distributed rock. The uniaxial compressive strength (UCS) of Sandy Dolomite is an important metric in the application in civil engineering, geotechnical engineering, and underground engineering. Direct measurement of UCS is costly, time-consuming, and even infeasible in some cases. To address this problem, we establish an indirect measuring method based on the convolutional neural network (CNN) and regression analysis (RA). The new method is straightforward and effective for UCS prediction, and has significant practical implications. To evaluate the performance of the new method, 158 dolomite samples of different sandification grades are collected for testing their UCS along and near the Yuxi section of the Central Yunnan Water Diversion (CYWD) Project in Yunnan Province, Southwest of China. Two regression equations with high correlation coefficients are established according to the RA results, to predict the UCS of Sandy Dolomites. Moreover, the minimum thickness of Sandy Dolomite was determined by the Schmidt hammer rebound test. Results show that CNN outperforms RA in terms of prediction the precision of Sandy Dolomite UCS. In addition, CNN can effectively deal with uncertainty in test results, making it one of the most effective tools for predicting the UCS of Sandy Dolomite.

Model Studies on Settlement of an Expansive Soil Slope Stabilized with GGBS

Expansive soils present significant challenges in civil engineering due to their tendency to undergo volumetric changes with variations in moisture content, often resulting in slope instability and settlement issues. This study investigates the effectiveness of stabilizing expansive soil slopes using Ground Granulated Blast Furnace Slag (GGBS) as an additive. The research employs laboratory-scale model studies to simulate the behaviour of expansive soil slopes subjected to varying levels of GGBS stabilization. The settlement characteristics of the stabilized slopes are analysed under different loading conditions and moisture contents. Experimental results indicate that the incorporation of GGBS enhances the properties of expansive soils, reducing settlement and improving slope stability. The findings contribute to the understanding of sustainable solutions for mitigating settlement issues in expansive soil slopes, offering insights for practical applications in geotechnical engineering.

Evaluation of the Effectiveness of Geogrids Manufactured from Recycled Plastics for Slope Stabilization-A Case Study.

This study aimed to investigate the sustainable use of recycled plastics, specifically polypropylene (PP) and high-density polyethylene (HDPE), in the manufacture of geogrids for geotechnical and civil engineering applications. Plastics were collected from a recycling center, specifically targeting containers used for food, cleaning products, and other domestic packaging items. These plastics were sorted according to the Möbius triangle classification system, with HDPE (#2) and PP (#5) being the primary categories of interest. The research methodologically evaluates the mechanical properties of PP/HDPE (0/100, 25/75, 50/50, 75/25 and 100/0% w/w) composites through tensile and flexural tests, exploring various compositions and configurations of geogrids. The results highlight the superiority of pure recycled HDPE processed into 1.3 mm thick laminated yarns and hot air welded for 20 to 30 s, exhibiting a deformation exceeding 60% in comparison to the PP/HDPE composites. Through SolidWorks® Simulation, it was shown that the adoption of a trigonal geogrid geometry optimizes force distribution and tensile strength, significantly improving slope stabilization efficiency. Based on the results obtained, a laboratory-scale prototype geogrid was developed using an extrusion process. The results underscore the importance of careful composite design and yarn configuration selection to achieve the desired mechanical properties and performance in geogrid applications. It emphasizes the potential of recycled plastics as a viable and environmentally friendly solution for stabilizing slopes, contributing to the reduction in plastic waste and promoting sustainable construction practices.

Thermodynamically consistent numerical modeling of immiscible two‐phase flow in poro‐viscoelastic media

AbstractNumerical modeling of immiscible two‐phase flow in deformable porous media has become increasingly significant due to its applications in oil reservoir engineering, geotechnical engineering and many others. The coupling between two‐phase flow and geomechanics gives rise to a major challenge to the development of physically consistent mathematical models and effective numerical methods. In this article, based on the concept of free energies and guided by the second law of thermodynamics, we derive a thermodynamically consistent mathematical model for immiscible two‐phase flow in poro‐viscoelastic media. The model uses the fluid and solid free energies to characterize the fluid capillarity and solid skeleton elasticity, so that it rigorously follows an energy dissipation law. The thermodynamically consistent formulation of the pore fluid pressure is naturally derived for the solid mechanical equilibrium equation. Additionally, the model ensures the mass conservation law for both fluids and solids. For numerical approximation of the model, we propose an energy stable and mass conservative numerical method. The method herein inherits the energy dissipation law through appropriate energy approaches and subtle treatments for the coupling between two phase saturations, the effective pore pressure and porosity. Using the locally conservative cell‐centered finite difference methods on staggered grids with the upwind strategies for saturations and porosity, we construct the fully discrete scheme, which has the ability to conserve the masses of both fluids and solids as well as preserve the energy dissipation law at the fully discrete level. In particular, the proposed method is an unbiased algorithm, that is, treating the wetting phase, the non‐wetting phase and the solid phase in the same way. Numerical results are also given to validate and verify the features of the proposed model and numerical method.

Application of Electrical Resistivity Tomography in Geotechnical and Geoenvironmental Engineering Aspect

Electrical resistivity tomography (ERT) has turned out to be one of the most applied and user-friendly geophysical methods in geotechnical and geoenvironmental research. ERT is an emerging technology that is becoming popular nowadays for investigating subsurface conditions. Multiple attributes of the technology using various electrode configurations significantly reduce measurement time and are suitable for applications even in hardly accessible mountain areas. It is a noninvasive test for subsurface characterization and a very sensitive method used to determine geophysical properties, i.e., structural integrity, water content, fluid composition, etc. This paper aimed to elucidate the ERT technique’s main features and applications in geotechnical and geoenvironmental engineering through four case studies. The first case study investigated the possible flow paths and areas of moisture accumulation after leachate recirculation in a bioreactor landfill. The second case study attempted to determine the moisture variation along highway pavement. The third case study explored the slope failure investigation by ERT. The fourth case study demonstrated the efficiency of the ERT method in the landfill evapotranspiration (ET) cover to investigate moisture variation on a broader scale and performance monitoring. In all of the four cases, ERT exhibited promising performance.

A ChatGPT-MATLAB framework for numerical modeling in geotechnical engineering applications

ChatGPT has recently emerged as a representative of Large Language Models (LLMs) that have brought evolutionary changes to our society, and the effectiveness of ChatGPT in various applications has been increasingly reported. This study aimed to explore the potential of employing programming performance driven by ChatGPT responses to conversational prompts in the field of geotechnical engineering. The tested examples included the analysis of seepage flow and slope stability, and the image processing of X-ray computed tomographic image for partially saturated sand. For each case, the prompt was initially fed by a narrative explanation of the problem attributes such as geometry, initial conditions, and boundary conditions to generate the MATLAB code that was in turn executed to evaluate the correctness and functionality. Any errors and unanticipated results were further refined by additional prompts until the correct outcome was achieved. ChatGPT was able to generate the numerical code at a considerable level, demonstrating creditable awareness of the refining process, when meticulous prompts were provided based on a comprehensive understanding of given problems. While ChatGPT may not be able to replace the entire process of programming, it can help minimize sloppy syntax errors and assist in designing a basic framework for logical programming.

Forks in the road: decisions that have shaped and will shape the teaching and practice of geotechnical engineering

Geotechnical engineering spans a wide range of applications, including tunnels, foundations, dams, and retaining structures. It deals with a material known to be difficult to model: a particulate material whose mechanical response is affected by all three invariants of the stress tensor, by loading rate, by density and by fabric. New problems and greater complexity in old problems have come about with the effects of climate change. Progress in certain technologies—notably artificial intelligence—also defines the new landscape in which geotechnical engineers must operate. This paper focuses on mechanics-based geotechnical engineering applications. The paper reviews some of the major decisions that were made by the engineers and researchers who developed geotechnical engineering to the point at which it was an identifiable separate discipline and the consequences that these decisions have had on the development of the discipline and on its teaching. The paper identifies some key modelling choices that were made that have had an undeservedly disproportionate impact on the teaching and practice of geotechnical engineering. The focus of the paper is therefore on these decisions and choices, and what should be taught in their place today. Challenges that future geotechnical engineers may face, as well tools that will be available to them, are also discussed in the context of what should be taught in undergraduate and graduate courses.

Physical, Chemical and Compaction Characteristics of Slightly Weathered Tephras of New Zealand

AbstractThe North Island of New Zealand is a region of high volcanic activity, with significant eruptions over the past. Analogous to past events, future volcanic eruptions would produce a considerable volume of ash and granular soils, covering widespread areas and raising concerns for their disposal and storage. Such deposits, primarily airfall tephra, could be potentially used in geotechnical engineering applications such as foundations, roadway embankments and land reclamations. However, before their use as structural fills can be recommended, detailed laboratory investigations of their physical, chemical, compaction, and geotechnical engineering properties (strength, compressibility, collapsibility, liquefaction potential, etc.) must be conducted. Different tephra deposits can be products of different eruptions, so chemical composition analyses can be combined with the physical, compaction, and engineering properties to characterize such deposits. Accordingly, this paper provides useful insights from physical (grain size, specific gravity, and morphology), chemical (elemental and mineralogy using X-ray fluorescence and X-ray diffraction), and compaction tests (maximum dry density, optimum water content, and particle breakage) for eleven selected volcanic tephra samples sourced from the North Island of New Zealand in the Rotorua, Taupo, and Auckland regions.

Observations on tensile testing of intact and slitted geotextiles through image analysis

Geotextiles are widely used as reinforcing elements in many geotechnical engineering applications such as mechanically stabilized earth walls, reinforced soil slopes, capping of high-water content clays in landfills, reinforced embankments for railways and roadways, and so on. The ultimate strength of geotextile is obtained from the load-displacement plot; however, the failure mechanism evaluation of local displacement is crucial to understand the serviceability state. In the present study, the image-based deformation measurement technique is used to evaluate the local displacement within geotextile at different locations during the wide-width tensile test. Further, the load-displacement plots are obtained for different geotextiles and a comparison is made between the measured displacements from the instruments and through an image-based deformation measurement technique. Additionally, the strength of geotextiles with imbibed defects at different orientations and corresponding failure patterns is investigated. The imbibed defect in geotextile is in the form of slits having orientations varied as vertical, horizontal, and inclined with respect to the loading direction. It has been observed that geotextile with vertical slit has the highest strength, followed by geotextile with inclined and horizontal slits. The present study results provide insight into the variation in the strength and displacement field of geotextiles having defects.

Deep Learning Approach on Prediction of Soil Consolidation Characteristics

Artificial neural network models, crucial for accurate predictions, should be meticulously designed for specific problems using deep learning-based algorithms. In this study, we compare four distinct deep learning-based artificial neural network architectures to evaluate their performance in predicting soil consolidation characteristics. The consolidation features of fine-grained soil have a significant impact on the stability of structures, particularly in terms of long-term stability. Precise prediction of soil consolidation under planned structures is vital for effective foundation design. The compression index (Cc) is an important parameter used in predicting consolidation settlement in soils. Therefore, this study examines the use of deep learning techniques, which are types of artificial neural network algorithms with deep layers, in predicting compression index (Cc) in geotechnical engineering. Four neural network models with different architectures and hyperparameters were modeled and evaluated using performance metrics such as mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2). The dataset contains 916 samples with variables such as natural water content (w), liquid limit (LL), plasticity index (PI), and compression index (Cc). This approach allows the results of soil consolidation tests to be seen more quickly at less cost, although predictively. The findings demonstrate that deep learning models are an effective tool in predicting consolidation of fine-grained soil and offering significant opportunities for applications in geotechnical engineering. This study contributes to a more accurate prediction of soil consolidation, which is critical for the long-term stability of structural designs.

An efficient IoT-based soil image recognition system using hybrid deep learning for smart geotechnical and geological engineering applications

An efficient IoT-based soil image recognition system using hybrid deep learning for smart geotechnical and geological engineering applications

Rheological study of the effect of clay mineral composition on non-linear viscoelasticity

Due to cyclic loading, clays composed of different mineral compositions exhibit non-linear viscoelastic characteristics. This study quantified the effect of the mineral composition on the non-linear viscoelastic properties of clays. Large-amplitude oscillatory shear rheological tests and cryo-electron microscopy tests were conducted on clays containing quartz, feldspar, montmorillonite, and kaolinite (Q, F, Mt., and Kaol). The results indicated that as the clay content in the soil sample increased, so did the value of relative intensity(I3/1), the Lissajous curves shifted from elliptical to rectangular, and the absolute values of the overall non-linear viscoelasticity parameters (NEand NV) were also larger. In addition, its non-linear viscoelasticity was more prominent, supported by the cryo-electron microscopy test outcomes. Montmorillonite played a more significant role than kaolinite in increasing the nonlinear viscoelasticity of the soil samples. The study findings have significant academic and practical value, facilitating an in-depth understanding of the viscoelastic behavior of clays, rheological studies of other types of clay soils, and geotechnical engineering applications.

Evolution of water content and suction of Opalinus Clay from recovery at the drilling site to handling in the laboratory

Advanced geotechnical engineering applications, such as shale gas extraction, CO2 geological sequestration, and geological radioactive waste storage, often involve various types of shales located at significant depths. Shales exhibit mechanical properties that are highly influenced by their hydration state and are exposed to substantial stress relief during extraction from considerable depths. This results in the development of elevated total suction (free energy per unit volume of pore water). While water content measurements are conventionally employed for characterizing these materials, ongoing discussions and uncertainties persist regarding the relevance and representativeness of laboratory suction measurements, particularly in light of potential influences stemming from core extraction and conditioning processes. A recent extensive borehole drilling campaign has provided a unique opportunity to offer scientific insights into specimens of Opalinus Clay shale, extracted from various locations and depths. These specimens were examined in their freshly extracted on-site condition and their freshly opened condition in the laboratory. Notably, it has been observed that suction and water content measurements acquired on-site immediately after core extraction differ from those obtained in the laboratory. The evolution of suction and water content from the field to the laboratory is closely linked to the main drying water retention behavior of the geomaterial.

Influence of morphological features on mechanical properties of granular soils from different depositional environments

The behavior of granular soils is intricately linked to their origin and sedimentation mechanisms, as evidenced by their unique morphological characteristics shaped by depositional environments. This study investigates into the critical relationship between these morphological attributes, including sphericity, angularity, and roundness, and the mechanical properties of granular soils. Such understanding is pivotal for applications like piled foundations, large dam filters, and pavements in geotechnical engineering. To assess this relationship, three granular soils from distinct depositional environments were selected, and their morphological features were meticulously examined using scanning electron microscopy (SEM) and analyzed using ImageJ software. Additionally, surface roughness parameters were quantified through an optical profilometer. The mechanical response of these soils was comprehensively investigated through direct shear tests, with a specific focus on the impact of shape factors. The results of this study unveiled striking differences between river sand deposits and coastal sand in terms of morphological attributes. River sand exhibited higher angularity, reduced roundness, and a greater number of surface asperities, contributing to heightened frictional resistance and pronounced dilatancy effects when subjected to shear loads. This paper underscores the significance of morphological features in influencing the macroscale properties of granular soils and provides valuable insights for geotechnical engineering applications.

A Framework for Monitoring Stability of Tailings Dams in Realtime Using Digital Twin Simulation and Machine Learning

Tailings dam failures cause catastrophic impact on the environment and surrounding communities. Incidences of failure in the recent past have caused industrialists and researchers to seek innovative ways for proactively managing their safety and disaster mitigation. Given Industry 4.0 technologies now available, researchers are looking to develop digital tools for cost-effective, realtime monitoring of tailings dams. However, published literature indicates that a reliable framework is still lacking. This paper proposes a framework for developing a data-driven system for monitoring tailings dam stability and early warning detection. The framework relies upon digital twin simulation and machine-learning (ML) techniques, and comprises four main components: realtime data collection, digital twin modelling, ML-based early detection and prediction, and intelligence-driven decision-support. Sensors gather real-time geophysical data from monitored structure, and the digital twin uses this data to simulate dam behaviour. ML algorithms analyse the data and simulations to enable early detection of instability and failure prediction. Literature suggests that digital twin and ML-based approaches may have advantages over traditional monitoring techniques and other AI-based methods. The paper concludes with a discussion of the framework's limitations, opportunities for improvement, and potential for application in mining and geotechnical engineering. The paper serves as a basis for model development and future research.

Isolation and screening of urease-producing bacteria from natural sources

Urease is an enzyme made by ureolytic microorganisms that hydrolyzes urea into ammonia and carbon dioxide. Microbial urease has wide applications in biotechnology, agriculture, construction, and geotechnical engineering. Urease-producing microbes can be isolated from different ecosystems such as soil, oceans, and various geological formations. The aim of this study was to isolate and characterize urease-producing bacteria from different natural sources. Ten urease-producing bacterial isolates were screened and selected using a qualitative urease activity assay. Among these, four isolates showed rapid urease activity. The isolates were further characterized with respect to their morphological, characteristics