Soil-rock mixtures (SRMs) are crucial in assessing foundation bearing capacity and slope stability in mountain substation projects, yet their complex mechanical properties pose significant challenges. Current research primarily focuses on the influence of individual factors on shear strength using the controlled variable method, offering limited insight into the interactions and quantitative analysis of multiple influencing factors. This limitation hinders accurate prediction and effective control of shear strength, complicating post-construction settlement management. To address this challenge, this study introduces an analysis method employing a feedforward neural network (FNN) to evaluate changes in SRM shear strength under the influence of multiple factors. By integrating SRM physical properties with shear strength test data, the method facilitates identification and quantitative analysis of key factors, including moisture content, dry density, void ratio, and the liquid-plastic limits of fillers with varying particle gradations. A correlation model is developed, demonstrating high reliability and predictive accuracy. Among the analyzed factors, moisture content and plastic limit exhibit the most significant influence on SRM shear strength, with importance levels ranging from 23.9% to 32.8%. The primary contribution of this research is the integration of machine learning with traditional geotechnical analysis, offering a practical framework for the identification and evaluation of factors influencing SRM shear strength. The proposed correlation model provides valuable insights into the intrinsic factors governing SRM shear strength variability and offers practical guidance for the design, construction, and operational safety of fill engineering in substations across southwestern China. Moreover, it serves as a useful reference for the quantitative analysis of influencing factors in SRMs for other regions.

In Northern Tunisia, where complicated geological and hydrological conditions increase slope instability, landslides constitute a significant geohazard. The purpose of this study is to use an integrated approach that combines GIS-based analysis, geotechnical investigations and Electrical Resistivity Tomography (ERT) and Vertical Electrical Sounding (VES) to characterize landslide-prone areas. ERT and VES reveal low-resistivity zones that correspond to clay, water-saturated layers that strongly correlate with unstable slopes. Geotechnical tests confirmed this susceptibility, indicating reduced compressibility. Field observations, geophysical and geotechnical data, integrated with topographic information in GIS, revealed slope deformation and areas of moderate to high instability. The results show that the landslide evolved into a complex form, with a failure surface reaching approximately 25 m. This study confirms that combining geophysical, geotechnical and GIS techniques is effective for monitoring landslide processes. This integrated methodology improves hazard assessment and guides risk mitigation and land-use planning.

ABSTRACT Random field modelling is widely used in geotechnical reliability analysis, as the relevant properties of soil and rock often exhibit significant spatial variability. The Karhunen–Loève expansion (K–L) has gained popularity as a method for generating random fields involving the decomposition of covariance structures into an infinite series of orthogonal eigenfunctions. In practice, this series needs to be truncated after a finite number of terms due to computational constraints. K–L truncation is directly controlled by the domain size, correlation length and spatial grid size on which the random field is to be generated. Truncation results in discretisation errors in the random field generation, which must be carefully managed. This paper reviews the principles of K–L, its implementation for random field generation, and its use in a geotechnical context. The presence of streakiness and checkerboard effects as observed by some investigators using K–L is critically examined. An example of a finite element geotechnical stability problem using K–L generated random fields is presented, highlighting the possibility of distinctly different failure mechanisms depending on the level of truncation employed in the K–L random field generation.

Abstract-This project analyzes soil–structure interaction (SSI) of strip footings using PLAXIS 2D. Various soil types—soft clay, stiff clay, sandy clay, sand, and dense sand—are modeled to study their effects on bearing capacity and load–settlement behavior under central and eccentric loading. Results show dense sand provides the highest capacity with minimal settlement, while soft clay performs the weakest. Comparisons with Terzaghi, Brinch Hansen, Meyerhof, and IS Code methods show close agreement, with PLAXIS giving slightly conservative values. Eccentric loading (e/B = 0.2) reduces bearing capacity by up to 30%, emphasizing its importance in design. The study demonstrates the usefulness of PLAXIS 2D in realistic geotechnical analysis. Additionally, the modeling highlights the nonlinear response of soils under increasing stress. Interface elements capture slip and separation at the footing–soil boundary effectively. The study also shows that settlement patterns change significantly with soil stiffness. Numerical stress contours help visualize failure mechanisms clearly. Overall, the findings support using advanced FEM tools for accurate, safe, and economical foundation design. Index Terms-T-Beam; IRC-codes; Bourbon’s method; Deflection; Crack Soil–Structure Interaction, PLAXIS 2D, Strip Footing, Bearing Capacity, Load–Settlement, Eccentric Loading, Soil Types. width etc.

Abstract Climate change impacts are affecting and will more and more alter the already precarious hydrogeological equilibrium, with immeasurable consequences on geo-structures, such as embankments and slopes. Despite the impacts of climate change on geo-structures are clear, what is less straightforward is how to deal with them. Among the possible strategies, adaptation of geo-structures to climate change is essential. The aim of this paper is to provide a modular conceptual and operational framework to support best-practice quantitative geotechnical analysis and design of climate-adaptive geo-structures. To this aim, the paper preliminarily introduces the correlations and causal relationships between climate change signals, climate change effects, and climate change impacts. Such correlations are integrated in the framework which explicitly accounts for climate change into the geotechnical analysis and design process. It can be used by geotechnical engineers to evaluate and assess the climate-adaptivity of both existing and newly planned geo-structures, through a structured insight into their interaction with temporally variable climate change signals. An example application of the framework is provided in relation to a real slope stability problem. This case study is used for validation, then the slope’s climate adaptivity is assessed considering different climate scenarios. Results show that by accounting for a remediation measure, the performance of the slope is compliant with design requirements at all temporal scenarios considered, i.e. the geo-structure will be climate-adaptive throughout its service life. This study is part of the research work carried out within the European Large Geotechnical Institutes Platform Working Group on Climate Change Adaptation.

The Bontang-Kuala road in East Kalimantan Province faces major problems due to daily tidal flooding and significant road subsidence. The objective of this study is to conduct a geotechnical analysis of the current conditions and develop more efficient solutions. Field surveys, soil investigations, and numerical analysis using the finite element method were employed in this study. To validate soil parameters, a back-analysis was conducted using ten years of subsidence data, which indicated a subsidence of 39.58 cm. The analysis results showed that conventional embankment reinforcement cannot withstand the required traffic load and may collapse. There are three treatment options: Pile slab structure, pile embankment with soil fill, and pile embankment with mortar foam fill. It was proven that each option meets the stability and technical design settlement requirements.

The present study focuses on the geotechnical characterization of a rock mass in Covilhã (Portugal) using in situ testing techniques. The research aims to enhance the understanding of subsurface conditions by integrating multiple geotechnical investigation methods, including the seismic piezocone penetration test with pore pressure measurements (SCPTu), without seismic (CPTu), dynamic penetration tests (DPM and DPSH). These tests provide critical data on soil mechanical resistance, internal structure, and deformation properties, supporting more accurate foundation and infrastructure design decisions. The results indicate that the studied soil primarily consists of sand to silty sand with high and increasing over consolidated ratio and relative density, showing a trend towards cementation. The SCPTu test proved highly effective in assessing initial stiffness and computing deformation moduli, significantly contributing to geotechnical engineering applications. Additionally, DPM and DPSH have provided complementary data, allowing for a more comprehensive evaluation of subsurface variability. The integration of these methods enhanced the reliability of soil classification, ensuring robust datasets for geotechnical analysis and infrastructure development. This research aligns with the themes of in - situ testing for geotechnical development. The findings emphasize the importance of multidisciplinary approaches in advancing infrastructure resilience and sustainability through accurate subsurface characterization. The combination of SCPTu, CPTu, and dynamic penetration tests supports the development of safer and more efficient geotechnical engineering solutions, reinforcing the role of geotechnics in modern civil engineering challenges.

Geotechnical analysis of gravity anchors for floating solar energy systems

Comprehensive geotechnical analysis for urban underground construction in Jakarta

Global Positioning System (GPS) technology proposals an accurate and efficient method to measure and record topographic data, exclusively slope data needed for various practices. The use of GPS allows faster and more accurate data collection, even in difficult areas, as well as allowing the integration of real-time data into digital mapping systems. A 3D model of a slope is a digital representation of an inclined surface in three dimensions, commonly used in engineering, architecture, geography, and gaming. It accurately depicts variations in elevation, gradient, and terrain features, making it useful for various applications. The main objectives of this research are to display 3D models of several areas in UiTM Seri Iskandar and to calculate the volume of each model. The research is conducted through topographic data collection by using GPS with integrated MyRTKnet technique which comprises the measurement of slope coordinate points with GPS devices to obtain accurate elevation and slope angle data. Additionally, used the software tools to generate models from the collected data, which involves GIS based software or specialized 3D modeling tools known for terrain and volumetric modeling. As well as calculated the volume by comparing plane heights above and below specific elevation levels, identifying changes in terrain displacement and landform structure. It is expected to contribute significantly in providing valuable insights to geotechnical analysis, urban planning and environmental management by understanding slope stability is crucial for preventing landslides, optimizing construction projects, and ensuring sustainable land use. Lastly, with determined the volume differences between reference heights, the study provides understandings into terrain differences, elevation changes, and surface crinkles.

Abstract This research presents a user-friendly Python tool to automate single pile settlement predictions, making advanced machine learning ( ML ) techniques more accessible to geotechnical experts. Leveraging a comprehensive dataset of 656 records from 41 full-scale bored pile load tests conducted under various Egyptian subsoil conditions, we rigorously trained and evaluated six prominent ML models: Gaussian Process Regression ( GPR ), Extreme Gradient Boosting ( XGBoost ), Gradient Boosting Machine ( GBM ), Random Forest ( RF ), K-Nearest Neighbors ( KNN ), and Support Vector Regression ( SVR ). Among these, GPR emerged as the top-performing model, showcasing exceptional predictive accuracy and robustness, evidenced by consistently high coefficient of determination values and low error metrics on unseen test data, as well as tight clustering of predicted versus actual settlement values. A key feature of this study was the integration of Monte Carlo simulations to quantify uncertainties associated with input parameters. Results were visually represented through load-settlement curves with 95% confidence intervals, providing a comprehensive assessment of prediction reliability. Furthermore, a detailed SHAP feature importance analysis identified the most influential factors in the GPR model’s predictions, aligning with established geotechnical principles. Finally, this work offers a reliable and efficient framework for forecasting single pile load-settlement behavior, enhancing the accuracy and speed of geotechnical analysis and contributing to the development of more dependable prediction tools for civil engineering infrastructure.

This research presents an evaluation of the design and stability of a seawall at Laompo Beach, Sulawesi, Indonesia, to protect a nearby highway from coastal erosion. Preliminary dimensions with some secondary data on wind, tidal, and soil bathymetric data have been carried out. This includes a wall height of 4 m, foundation width of 2.8 m, foundation height of 0.7 m, and top embankment width of 1 m. The stability analyses using the limit equilibrium method indicate that the embankment satisfies the safety criteria under static, dynamic, and scour conditions. The research highlights the importance of precise design considerations, including wave forecasting and stability analysis, for constructing durable seawalls. This paper emphasizes overall coastal engineering to reduce the effects of erosion on critical infrastructure and the need for high-quality construction and maintenance to ensure the long-term efficacy of these structures, providing valuable guidance for developing safe and effective coastal protection measures. This work is singular, as the advanced geotechnical analysis tools have been implemented in the context of a real-world coastal protection scenario in a developing region

Abstract: This paper highlights the extended applications of the Cone Penetrometer Test (CPT) for geotechnical decision support in the determination of depth of foundation embedment, optimum depth of excavation, vertical settlement distribution, determination of total consolidation settlement and the assessment of deep shear failure risk with the use of case studies in the Niger delta. These applications are more critical in site conditions where retrieval of soil sample is difficult due to extremely weak or where soil layers are very thin such that are missed during regular boring programmes. In these instances, the (CPT) serves as a vital geotechnical analysis for better and informed decisions, offering economical, high-resolution, nearly continuous data for accurate identification of soil layers and boundaries, efficient and reliable data for various applications, including pavement, swamp rig and offshore foundation design. The CPT data can often be integrated with data from other sources, processed through various analytical and modelling techniques to unravel findings that facilitate strategic and operational choices. Its advantages of rapid data acquisition, cost-effectiveness, and the ability to characterize soil conditions over large areas, which aids in foundation design and risk assessments, provide the enablement for geotechnical analysis and decision support.

Clay soil is a type of soil with high plasticity whose properties are strongly influenced by water content. The consistency of clay can be represented by the Liquid Limit (LL) and Liquidity Index (LI), while its strength is commonly evaluated using the Unconfined Compression Test (UCT), which is widely applied in geotechnical analysis. This study aims to analyze the relationship between UCT values and soil consistency index parameters (LL and LI). Soil samples were taken from three different locations and compacted according to the Standard Proctor test as a reference for preparing unconfined compression test specimens. The test results show that the LL values at the three locations were 56.37%, 60.91%, and 42.43%, while LI values ranged from -0.47 to -0.04, indicating soil conditions from dense to slightly plastic. The unconfined compressive strength (qu) at the optimum water content was 14.91 kg/cm² at Location 1, 12.50 kg/cm² at Location 2, and 13.20 kg/cm² at Location 3. Correlation analysis indicates a weak relationship between qu and LL, while qu and LI exhibit a relatively high R² value. However, due to the very limited amount of data, the relationship between these parameters cannot yet be conclusively determined.Keywords: Clay soil, Unconfined Compression Test (UCT), Liquid Limit (LL), Liquidity Index (LI), Compaction

Indonesia’s mining sector, particularly in areas like Morowali Regency, Central Sulawesi, faces significant geotechnical challenges due to its location in the Pacific Ring of Fire. One of the key concerns in open-pit mining operations is slope instability, which can lead to landslides, threaten worker safety, damage infrastructure, and disrupt production. This study evaluates the slope stability in the Sambalagi site of PT. Wosindo Berkat Abadi using the Morgenstern-Price method, a limit equilibrium approach known for its accuracy in heterogeneous slope conditions. Field data were collected, including slope geometry, geological conditions, material strength, and hydrogeological factors. The safety factor (FK) was calculated based on geotechnical parameters such as cohesion, internal friction angle, and unit weight of the slope materials primarily saprolite and limonite. The actual slope FK value at PIT D was found to be 0.974, below the standard requirement (≥1.3) set by the Ministerial Decree No. 1827K/30/MEM/2018. To improve stability, a revised slope design was proposed, including reducing slope angles to 35°, increasing bench widths to 2 meters, and decreasing slope height per bench to 4 meters. The simulation of this revised geometry showed that it could achieve the required FK value. The study contributes to safer and more efficient mine planning by demonstrating the importance of integrating detailed geotechnical analysis in slope design, especially in tropical high-rainfall mining regions.

Accurate prediction of soil–structure interface shear strength (τmax) is critical for reliable geotechnical design. This study combines experimental testing with interpretable machine learning to overcome the limitations of traditional empirical models and black-box approaches. Ninety large-displacement ring shear tests were performed on five sands and three interface materials (steel, PVC, and stone) under normal stresses of 25–100 kPa. The results showed that particle morphology, quantified by the regularity index (RI), and surface roughness (Rt) are dominant factors. Irregular grains and rougher interfaces mobilised higher τmax through enhanced interlocking, while smoother particles reduced this benefit. Harder surfaces resisted asperity crushing and maintained higher shear strength, whereas softer materials such as PVC showed localised deformation and lower resistance. These experimental findings formed the basis for a hybrid symbolic regression framework integrating Genetic Programming (GP) with Shapley Additive Explanations (SHAP), Fourier feature augmentation, and physics-informed constraints. Compared with multiple linear regression and other hybrid GP variants, the Physics-Informed Neural Fourier GP (PIN-FGP) model achieved the best performance (R2 = 0.9866, RMSE = 2.0 kPa). The outcome is a set of five interpretable and physics-consistent formulas linking measurable soil and interface properties to τmax. The study provides both new experimental insights and transparent predictive tools, supporting safer and more defensible geotechnical design and analysis.

Expansive soils are highly susceptible to volumetric changes due to moisture fluctuations, which can significantly affect the stability and durability of structures. Therefore, their presence must be carefully considered during the planning and foundation design stages. Survey, field investigations, and lab tests show that soil up to 8 m deep has a plasticity index of 30%–65%. Swelling tests on samples from 1 m–3.5 m depths revealed swelling percentages of 0.545%–0.715% and pressures of 11.7 kPa–12.5 kPa, which are high for near-surface soil. XRD tests identified montmorillonite minerals, known for high activity and shrinkage, contributing to slope cracks and movement. Geotechnical analysis using finite element method shows that slope stability safety factors of 0.84 (static) and 0.62 (earthquake), below required thresholds of 1.5 and 1.1, respectively. The proposed reinforcement includes double-row soldier piles, connected by a capping beam. The slope surface will be graded downstream and reinforced with 1 m thick stone masonry. These measures are expected to increase safety factors to 1.72 (static) and 1.1 (earthquake), meeting safety standards.

This research is aim at determining the geochemistry and geotechnical properties for clay deposits from the Yolde Formation in Demsa and Environs. The research locality falls within latitudes 9'15'0 "to 9'27 "N and longitude 12' 06′0"to 12'20' 0"E, with aerial extent of about 144 km². The research was carried out in two phases; field mapping, and laboratory analysis. The field exercise encompasses; logging and taking measurement of bedding attitude of sedimentary rock from the outcrop sections, snapping the rock exposures, taking samples, and recording GPS cordinatess of sampling points. This was followed by laboratory work, the samples were analyzed with the aid of X-ray fluorescence (XRF) for understanding the geochemical composition of the clay samples, while the physical parameters of the clay were determined with the aid of Atterberg limit method. the average concentration of Silica in theclay of the Yolde Formation is 69.11%, and that of iron oxide is 4.78%, while that of Alumina is low with average concentration of 21.21%. Further, the remaining major oxide; CaO, MgO, MnO, TiO2, etc. were also observed as well. However, the result ofthe geotechnical analysis revealed that the average concentration of silica to be 69.11% while iron oxide 4.78% and low water absorption, low plasticity index respectively the concentration of the iron oxides unsuitability of the clay to be use as raw material for refractory fired clays and manufacturing of high-grade ceramic products, but the clays can be used for burnt bricks and traditional ceramics production.

The integration of artificial intelligence (AI) and machine learning (ML) has revolutionised civil engineering, enhancing predictive accuracy, decision-making, and sustainability across domains such as structural health monitoring, geotechnical analysis, transportation systems, water management, and sustainable construction. This paper presents a detailed review of peer-reviewed publications from the past decade, employing bibliometric mapping and critical evaluation to analyse methodological advances, practical applications, and limitations. A novel taxonomy is introduced, classifying AI/ML approaches by civil engineering domain, learning paradigm, and adoption maturity to guide future development. Key applications include pavement condition assessment, slope stability prediction, traffic flow forecasting, smart water management, and flood forecasting, leveraging techniques such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Support Vector Machines (SVMs), and hybrid physics-informed neural networks (PINNs). The review highlights challenges, including limited high-quality datasets, absence of AI provisions in design codes, integration barriers with IoT-based infrastructure, and computational complexity. While explainable AI tools like SHAP and LIME improve interpretability, their practical feasibility in safety-critical contexts remains constrained. Ethical considerations, including bias in training datasets and regulatory compliance, are also addressed. Promising directions include federated learning for data privacy, transfer learning for data-scarce regions, digital twins, and adherence to FAIR data principles. This study underscores AI as a complementary tool, not a replacement, for traditional methods, fostering a data-driven, resilient, and sustainable built environment through interdisciplinary collaboration and transparent, explainable systems.

The term ‘toughness’ refers to a rock's resistance to excavation and indentation by boring or cutting tools. It is evaluated in relation to the ductile properties of rocks, which show visible plastic deformation. Various indices exist for evaluating toughness, which is the opposite of brittleness. In this study, 24 samples from three types of rocks (igneous, metamorphic and sedimentary) were selected to evaluate the toughness indices. The toughness indices were assessed based on existing methods such as strain, energy and strength, and specialized tests such as the brittleness value (S 20 ), Sievers’ J value (S J ), punch penetration test (BI), and porosity (n). The best toughness index found by using entropy and Gini split from the Iterative Dichotomiser 3 algorithm decision tree was S J = 18. By using the Sievers’ J value and rock tensile strength a new rock toughness index is proposed, which is related to specific energy. A new toughness index is presented as the S J drillability coefficient toughness (T SDC ). The value of T SDC = 5 N mm –3 was found to be a good threshold for indicating the energy consumed during drilling and rock excavation. The results showed that the energy required to excavate tough rocks is greater than for brittle rocks.