Geotechnical Engineering Applications Research Articles

Geotechnical Challenges in Slope Stability: Experimental Investigation of Coconut Fiber Reinforcement Under Seismic Loading

Abstract Geotechnical challenges in contemporary engineering necessitate the construction of structures on poor soils and vulnerable slopes due to limited land availability. The stability of soil slopes under seismic conditions is crucial for designing safe infrastructures. Conventional slope reinforcement techniques, such as geosynthetics, are effective but costly. This study explores the use of coconut fibers as a cost-effective, temporary reinforcement for improving slope stability under seismic loading. The research involved horizontal and vertical shake table tests on slopes of varying heights and angles, both with and without surcharge loads. Coconut fibers were mixed into the soil at different percentages to assess their effectiveness in reducing displacements and settlements during seismic events. Linear Variable Differential Transformers (LVDTs) measured displacements at various heights on the slopes. Results indicate that slopes reinforced with coconut fibers exhibit significantly reduced vertical and horizontal displacements compared to unreinforced slopes under both vertical and horizontal seismic loading. The reinforced slopes showed improved stability, particularly under higher surcharge loads and increased shaking frequencies. This study demonstrates that coconut fibers can serve as an effective, eco-friendly reinforcement method for temporary slope stabilization, offering a sustainable alternative to synthetic materials. The findings contribute to the broader understanding of natural fiber applications in geotechnical engineering, especially for regions prone to seismic activities.

Developing a High‐Power Metal‐Plate Lens Antenna for Microwave Fracturing of Rocks

ABSTRACTThis study presents the design and experimental validation of a high‐power metal‐plate lens antenna for efficient microwave fracturing of rocks in the near‐field. The antenna integrates a horn feed with a biconcave metal‐plate lens to achieve extended working distances and enhanced power focusing. Through numerical simulations, the antenna demonstrated a maximum microwave power density of 22 W/cm² on diabase rock surfaces at 6 kW input power with a 36 cm irradiation distance. The reflection coefficient remained below −10 dB across irradiation distances of 20–50 cm, confirming robust energy transmission. Rock fracturing tests revealed that the antenna effectively induced thermal cracking at 36 cm, achieving a peak surface temperature of 270°C after 4 min of irradiation. The results highlight the antenna's ability to maintain focused microwave energy over practical working distances, offering significant potential for deployment in tunneling and mechanical excavation where conventional antennas face limitations. This work advances high‐power microwave applications in geotechnical engineering by addressing critical challenges in power delivery and operational range.

Overview of MICP Geotechnical Engineering Applications and Development Prospects

This study examines the research directions and potential of geotechnical engineering applications utilizing MICP. Due to the effective application of this technology in various geological reconstruction and engineering projects, coupled with the controllable and universal microbial induction process, it serves as an alternative green technology to a significant degree. Current research indicates that the urea hydrolysis reaction is extensively utilized due to its high efficiency and ease of control, yet its by-product ammonia may pose environmental pressures; meanwhile, the sulfate reduction reaction encounters issues related to the generation of toxic gases. In comparison, although iron reduction and denitrifying bacteria are more environmentally friendly, there remains room for improvement in sedimentation efficiency and gas production control. At the practical application level, MICP has been employed in soil remediation, soil reinforcement, and pollution control, demonstrating notable engineering value. However, technical challenges arise, including limited improvement effects on fine-grained soil, significant discrepancies between laboratory research outcomes and actual environmental adaptability, as well as research difficulties stemming from the complexity of microbial behavior. Nevertheless, MICP holds potential in achieving the carbon peak goal, thanks to its environmental friendliness, low energy consumption, and high efficiency. Through innovative approaches such as multi-material composite improvement, the technical adaptability and application effectiveness can be further enhanced. Future research should integrate interdisciplinary strengths to optimize bacterial selection and process design, thereby promoting the widespread application of MICP in geotechnical engineering.

Performance characterisation of machine learning models for geotechnical axial pile load capacity estimation: an enhanced GPR-based approach

ABSTRACT Traditional methods for predicting axial pile capacity often rely on simplified assumptions, leading to potential inaccuracies in diverse geotechnical conditions. This study investigates the key parameters influencing axial pile capacity through comprehensive statistical and machine learning analyses in Kano, Nigeria. Six models–Bayesian additive regression trees (BART), explainable boosting machine (EBM), gradient boosting machine (GBM), Gaussian process regression (GPR), improved GPR, and multivariate adaptive regression splines (MARS)–were evaluated using a dataset of 100 training and 25 testing samples from reinforced concrete piles. Statistical analysis revealed strong correlations between pile length and embedment depth (r=0.94) and moderate correlations between axial capacity and length (r=0.78). The improved GPR model demonstrated superior performance with the highest R2 (0.9873) and lowest MARE (0.0678) during training, maintaining robust performance during testing (R2=0.9812). Feature importance analysis identified the uncorrected number of blows at the pile tip (N) as the most influential parameter (35.2–42.3%), followed by embedment depth (17.97–32.8%). Partial dependence analysis revealed non-linear relationships between design parameters and axial capacity, with diminishing returns observed beyond certain thresholds. These findings provide valuable insights for optimising pile foundation design in geotechnical engineering applications.

Stabilising highly expansive soil by using Nano-Clay additive

ABSTRACT This study aims to investigate the efficacy of hydrophilic bentonite Nano-Clay in improving the geotechnical characteristics of expansive soil sourced from the Al-Mushaqar region in Jordan. In this study sonic wave-assisted and manual mixing techniques applied to expansive soil specimens. Subsequently, a comparative analysis was conducted to explain the disparities in outcomes between these two mixing methods. Various proportions of Nano-Clay were introduced into the soil, ranging from 0% to 2.5% by dry weight of the soil. The study focused on evaluating their influence on key parameters, including compaction, Unconfined Compressive Strength (UCS), and Free Swell Index (FSI). The introduction of Nano-Clay into the soil exhibited a noteworthy enhancement in its strength characteristics, accompanied with a reduction in its swelling tendencies. Notably, the application of 0.5% Nano-Clay during sonication led to a remarkable 53.4% augmentation in UCS and a simultaneous 41% reduction in FSI. In contrast, non-sonicated samples treated with 1% Nano-Clay exhibited a notable 35.21% increase in UCS and a substantial 52.4% reduction in FSI. These findings underscore the significant potential of Nano-Clay in fortifying expansive soils, thereby offering promising prospects for geotechnical engineering applications.

Can ChatGPT Implement Finite Element Models for Geotechnical Engineering Applications?

Can ChatGPT Implement Finite Element Models for Geotechnical Engineering Applications?

Enhanced bearing capacity prediction using hybrid tree-based ensemble learning with advanced meta-heuristic optimization

Abstract The accurate prediction of soil bearing capacity remains a critical challenge in geotechnical engineering, particularly given the complex non-linear relationships between soil properties and foundation performance. Traditional analytical methods often struggle to capture these complexities, leading to potential overestimation or underestimation of bearing capacity across different footing types. This study investigates the application of machine learning techniques for predicting soil bearing capacity across different footing types. The research utilized 200 datasets, comprising 175 institutional sources and 25 laboratory Direct Shear Test experiments, with an 80-20 split ratio for model development and validation. A Hybrid Tree-Based Ensemble Learning (HTBEL) methodology was developed and compared against conventional models (M5P, CatBoost, AdaBoost, SVR, and Decision Tree) and Terzaghi analytical equation. The HTBEL model demonstrated superior predictive accuracy with R² values exceeding 0.96 across all footing types, maintaining errors below 5% throughout the sample range. Square footings showed the highest bearing capacity (median ~3,400 kN/m²) due to favorable area-to-depth ratio, followed by circular footings (~3,200 kN/m²) benefiting from symmetrical stress transmission, while strip footings (~2,000 kN/m²) showed lower performance due to concentrated stress distribution along their length. Clustering analysis identified optimal configurations at 3 clusters (Silhouette Score: 0.5236) and 10 clusters (0.5315). This research establishes HTBEL as a robust methodology for bearing capacity prediction in geotechnical engineering applications.

Dissolution and recrystallization behavior of microbially induced calcium carbonate: influencing factors, kinetics and cementation effect

Microbially induced calcium carbonate precipitation (MICP) is a nature-based biomineralization method with significant potential in geotechnical engineering. Despite the extensive previous studies on this technology, the stability of calcium carbonate crystals formed during the MICP process and its impact on cementation effectiveness remains unclear. This study investigates the effects of varying curing conditions and durations on the stability of microbially induced calcium carbonate crystals. Throughout the curing period, pH levels of the solutions and the mass of calcium carbonate samples were monitored. Crystal morphology, crystalline composition, and cementation properties were examined using scanning electron microscopy, X-ray diffraction, and ultrasonic oscillation tests. The findings reveal that vaterite remained stable in MICP or bacterial solution but quickly dissolved in deionized water. While most vaterite underwent dissolution and recrystallization within the first day of curing, the presence of organic matter in the crystals led to 10-20% of the vaterite remaining undissolved after 28 days. The dissolution of vaterite tended to promote the growth of pre-existing calcite polymorph rather than forming new ones. Extended curing periods in deionized water increased the proportion of dissolved vaterite, resulting in higher calcite content and enhanced cementation properties, which provides new insights for optimizing MICP applications in geotechnical engineering.

Optimizing Soil Stabilization with Chitosan: Investigating Acid Concentration, Temperature, and Long-Term Strength.

Civil and geotechnical researchers are searching for economical alternatives to replace traditional soil stabilizers such as cement, which have negative impacts on the environment. Chitosan biopolymer has shown its capacity to efficiently minimize soil erosion, reduce hydraulic conductivity, and adsorb heavy metals in soil that is contaminated. This research used unconfined compression strength (UCS) to investigate the impact of chitosan content, long-term strength assessment, acid concentration, and temperature on the improvement of soil strength. Static triaxial testing was employed to evaluate the shear strength of the treated soil. Overall, the goal was to identify the optimum values for the mentioned variables so that the highest potential for chitosan-treated soil can be obtained and applied in future research as well as large-scale applications in geotechnical engineering. The UCS results show that chitosan increased soil strength over time and at high temperatures. Depending on the soil type, a curing temperature between 45 to 65 °C can be considered optimal. Chitosan biopolymer is not soluble in water, and an acid solution is needed to dissolve the biopolymer. Different ranges of acid solution were investigated to find the appropriate amount. The strength of the treated soil increased when the acid concentration reached its optimal level, which is 0.5-1%. A detailed chemical model was developed to express how acid concentration and temperature affect the properties of the biopolymer-treated soil. The SEM examination findings demonstrate that chitosan efficiently covered the soil particles and filled the void spaces. The soil was strengthened by the formation of hydrogen bonds and electrostatic interactions with the soil particles.

Prediction and parametric assessment of soil one-dimensional vertical free swelling potential using ensemble machine learning models

Investigating soil swelling potential is indeed a critical research area in geotechnical engineering, given its significant influence on the stability and longevity of civil structures. This study aims to predict and assess the one-dimensional vertical free swelling potential of soils using ensemble machine learning models. Within the study context, a large dataset encompassing a wide array of soil parameters from 210 soil samples, including moisture content, unit weight, plasticity, and clay content, will be used. These parameters are critical in understanding the swelling behavior of soils under varying environmental and load conditions. The novel approach of this research lies in the application of ensemble machine learning techniques, which offer a robust framework to analyze complex, nonlinear relationships within soil properties. Another key aspect of this research is the parametric assessment, where the influence of individual soil properties on swelling potential is investigated using feature importance and partial dependence analyses. These analyses provide valuable insights into the relative importance of different soil parameters on soil behavior. The outcomes of this study contribute to soil mechanics and machine learning applications in geotechnical engineering and offer practical implications for engineers and practitioners. Besides, the predictive models developed in this study aid in more informed decision-making in the design and construction of civil structures, particularly in swelling-prone areas.

On nonexistence of some SP head waves

Theoretical study of “geometric” SP head waves propagating in an isotropic homogeneous traction-free halfspace or halfplane, reveals that these waves can satisfy the traction-free condition at the boundary plane if and only if the Lame parameter ? vanishes, which makes the existence of this type of head waves almost impossible. The analysis is based in the Helmholtz decomposition of the displacement field, coupled with stress and strain tensor decomposition into spherical and deviatoric parts. It can be anticipated that the obtained result on nonexistence of this type of head waves, can have applications in theoretical geophysics, geotechnical and earthquake engineering.#xD;

Optimization of Inputs for the Application of ANN to Rail Track Granular Materials

Machine Learning (ML) models such as Artificial Neural Networks (ANN) have gained increasing popularity in geotechnical engineering applications as an alternative to conventional empirical and computational models. At present, very few ML models exist for predicting the mechanical responses of track granular materials such as ballast and subballast which may even comprise of composite mixtures of blended granular materials. Moreover, the performance of any ML model depends not only on the quality and quantity of available data but also on the selection process for input parameters, which often lacks adequate justification in the past literature. In this context, the current study introduces ANN models for track granular materials based on published laboratory data with special emphasis on the selection of an optimal set of input parameters. Two applications of ANN are considered to: (i) predict the peak friction angle (ϕpeak') of a variety of granular mixtures under static loading and (ii) predict ballast breakage under cyclic loading. The selection process involves prudent analysis of key influential parameters in a geotechnical perspective, while also ensuring that they are conveniently measurable. Performance evaluation of these models with various input combinations is carried out, while proposing optimal input parameters for both applications.

Lagrangian approach for in-depth evaluation of subsurface enhancement: analytical insights and 3D numerical investigation featuring Sand Compaction Piles installed beneath shallow foundations

This study investigates the performance of sand compaction piles (SCPs) using numerical modeling techniques. Initially, the widely used composite cell approach was employed to simulate the behavior of SCPs within a finite element study (FEM). However, a comparison of the FEM-predicted vertical displacements with in-situ measurements revealed significant underestimations, highlighting the limitations of this simplified approach. To address these limitations, a more advanced Lagrangian finite-difference approach, implemented using the FLAC3D code, was developed. This model was calibrated against the analytical Greenwood solution, demonstrating strong agreement of predicted vertical displacements. The numerical simulations enabled a better highlight of improvement in soil settlement behavior after SCPs installation, attributed to increased soil stiffness and shear strength parameters. The study delved into various aspects of SCP behavior, including lateral deformation, stress distribution, and the effects of pile length, material properties, and loading conditions. The results provide valuable insights into the mechanisms underlying the performance of SCPs and the factors influencing their effectiveness in improving soil resistance and reducing settlement. By understanding these mechanisms, engineers can optimize the design and implementation of SCPs in geotechnical engineering applications.

Evaluation of Dynamic Properties of Sand Mixed with Expanded Glass Granules by Energy Concept Under Cyclic Torsional Loadings

In recent years, lightweight materials have gained attention for their numerous benefits, including cost reduction, insulation, and bearing capacity. Expanded glass granules (EGG) stand out among these materials, showing significant potential in geotechnical engineering applications. This study examines the potential use and feasibility of a SAND-EGG mixture in geotechnical applications through cyclic torsional shear test for measuring shear strength, modulus reduction, and energy dissipation. This is conducted on the unmixed dry specimens (Reference Sand, EGG) and the mixed dry specimens by mass (1, 2%) and volume (5, 10, 15%) under 100 kPa effective pressure. The test also conducted along 0.02%–0.5% shear strain amplitudes for both samples group. An energy-based approach is used to analyze mixed material's capacity for energy dissipation, a crucial factor in determining seismic resistance during cyclic loading. The impact of employing EGG on modulus reduction behavior was also explored concerning energy dissipation. Considering the results, it is show that the use of EGG increases the resilience with high friction capacity and by taking up a significant portion of the available void ratio and provides lightness in geotechnical applications in examined shear strain amplitude range (0.02%–0.5%).

Bio-cementations: A Review on Enzyme and Microbially Induced Calcite Precipitation Mechanisms and Applications in Geotechnical Engineering

Modern methods to improve soil must deal with sustainable and green building purposes, and precipitated calcite is the most modern bio-cementation method to improve soil sustainably. Enzyme-induced calcite precipitation and microbially-induced calcite precipitation are precipitated calcite's best branches in bio-cemented soil improvement. EICP and MICP contain calcium chloride, urea, water, urease enzyme and other chemical materials (for MICP As nutrients for bacteria) that are mixed together to precipitate calcium carbonate in the soil. The precipitated calcium carbonate was worked as a binder between soil particles, filling the void and improving the soil's properties. By utilizing this technique in the field, several researchers were able to achieve favorable outcomes in the areas of soil improvement and other engineering applications, including concrete repair and other applications, while also taking into consideration the objectives of sustainability. treatment carried out on soils with one or many cycles of the treatment solution. The treatment revealed a significant improvement in the value of unconfined compression strength, compared to untreated soils. During certain experiments, the unconfined compression strength revealed that soils treated with the enzyme approach exhibited higher values compared to soils treated with a mixture containing a certain proportion of regular Portland cement. The encouraging outcomes that were achieved through the utilization of this test in both the laboratory and the field can be utilized in large-scale operations. Till now, this method has been considered under study. In Iraq there are no any research in term of EICP treatment while there are several research about using MICP in soil treatment.

Study on the statistical damage constitutive model and experiment of hot dry rock cyclic heating-cooling based on the octahedral strength criterion

Study on the statistical damage constitutive model and experiment of hot dry rock cyclic heating-cooling based on the octahedral strength criterion

Polyurethane foam adhesive with fly ash as a sustainable stabilizer

ABSTRACT This experimental research work investigates the role of polyurethane foam adhesive (PFA) in evaluating the unconfined compressive strength (UCS) of three different types of sands with varying maximum particle sizes (Dmax = 4.00 mm, 2.00 mm, and 0.63 mm) mixed with different proportions of fly ash (FA = 0%, 5%, 10%, and 15%). Moreover, this study includes the influence of the polymer percentages (PFc = 2%, 4%, 6%, and 8%) and particle size characteristics on the UCS of the tested materials. The obtained results indicate a significant increase in the UCS and ductility at a particular fly ash content (FA = 5%), where the increment of the polyurethane foam content from PFc = 2% to 8% induces an increasing of the UCS from 1.64 MPa to 5.28 MPa (a 3.2% increase). It is found that higher values of PFc = 8% and FA = 15% exhibit noticeable improvements and stabilization of the strength properties in the tested mixtures. Additionally, the UCS of the enhanced mixtures can be predicted using a newly introduced parameter called the polymer factor index (IPF), which considers the porosity and PFA content properties. The PFA-enhanced sand-fly ash mixtures offer a promising and innovative solution in many geotechnical engineering applications due to their reliable mechanical performances.

The impact of bedding planes on microwave-induced damage in rock at the field scale: a numerical model

The feasibility of microwave heating-assisted rock breakage technology in geotechnical engineering has been widely explored. However, the limited knowledge of the impact of the contrast properties between different bedding planes and outer formation stresses limits its application at the field scale. In this work, the mechanics of mineral processing, rock breakage, and unconventional gas and oil exploitation were first characterized. A coupled model is subsequently proposed to govern the coupling process of microwave heating and induce inhomogeneous deformation and stress in various bedding planes. Three stress forms are considered in this approach: horizontal stress, normal stress, and shear stress. With the assistance of the finite element method, the stress conditions of bedding planes composed of rock under free swelling, constant stress, and constant volume were addressed. A bedding plane characterized by high microwave adsorption ability is usually compressed by a bedding plane characterized by low microwave adsorption ability. The bedding ore is under the condition of free swelling during mineral processing, whereas in the remaining two scenarios, the bedding rock is stress controlled. The tensile and compressive stresses can reach their maximum values under the condition of free swelling. The outer formation stress reduces the values of tensile and compressive stresses but has little impact on the shearing stresses. Additionally, this work provides criteria for defining an a priori evaluation of the effectiveness of microwave treatment of bedded ore and rock for different geotechnical engineering applications.

Utilization of recycled rubber crumbs and tile powder as additives to enhance clayey soil performance

Clayey soils present challenges in engineering applications due to their inherent properties, such as low shear strength, which usually limits their use in engineering applications. Stabilization of clayey soils is crucial to enhance their performance and suitability for various purposes. Utilizing waste materials like discarded tyres and tile powder as soil stabilizers presents a sustainable solution to mitigate environmental concerns while enhancing soil properties. While previous studies have explored using either crumbled rubber or tile powder independently for soil improvement, their combined effect remained largely unexplored. This study addresses this gap by investigating the synergistic potential of blending crumbled rubber and tile powder to enhance the strength and ductility of clayey soils. A series of laboratory tests were conducted to investigate the combined effect of crumbled rubber and tile powder. First, varying percentages of crumbled rubber (2.5%, 5.0%, 7.5%, and 10%) were mixed with the soil, and standard proctor tests, California bearing ratio (CBR) tests, and unconfined compressive strength (UCS) tests were performed. Results showed optimal performance at 5.0% crumbled rubber, exhibiting the highest values for maximum dry density, CBR, and UCS. Subsequently, different percentages of tile powder (5%, 10%, 15%, and 20%) were added to the soil-rubber mixture (with 5% crumbled rubber). The addition of 15% tile powder to the 5% crumbled rubber mixture yielded the most significant improvements as maximum dry density (MDD) increased from 1.842 g/cm3 (raw soil) to 1.963 g/cm3, UCS increased from 0.5176 kg/cm2 (raw soil) to 2.606 kg/cm2, and CBR increased from 1.757% (raw soil) to 7.84%. The addition of crumbled rubber was found to shift the failure behaviour of the clayey soil from brittle to more ductile, indicating an enhanced ability to undergo deformation before failure. This study’s findings highlight the potential of combining crumbled rubber and tile powder as a sustainable solution for enhancing clayey soil properties, paving the way for further research into optimized mixture designs and expanded applications in geotechnical engineering.

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.