Reliable interpretation of bored pile capacity from full-scale load-test data is essential for safe, economical design, yet evaluations across sand, clay and layered soils remain limited. A database comprising 75 full-scale axial compression tests on bored concrete piles which evaluated twelve interpretation criteria. All tests reached plunging failure or a large-displacement response, defined by pile-head settlement ≥ 10

This study delivers the first comprehensive microzonation-based seismic risk assessment for the Al-Seeb region in Muscat, Oman. It integrates geotechnical, structural, environmental, and socio-economic vulnerability factors using a Geographical Information System (GIS) based multi-criteria framework. The Analytic Hierarchy Process (AHP) was employed to systematically weight and integrate these indicators, ensuring an objective evaluation of their relative influence on overall seismic vulnerability. Moving beyond traditional methods, it translates microzonation data into spatially distributed Average Annual Loss (AAL) zones and estimates economic impacts through Loss Exceedance Curves (LEC) and Probable Maximum Loss (PML). By coupling detailed hazard zonation with building typologies and infrastructure exposure, the study develops an asset-level risk profile, revealing sharp spatial variations in seismic risk driven by site conditions and construction quality. The results serve as a vital planning tool for civil authorities, aligning with Oman Vision 2040 and United Nations Sustainable Development Goal (UN SDG) 11. The approach offers a scalable model for rapidly urbanizing Gulf cities such as Doha, Riyadh, Dubai, and Kuwait, providing a data-informed foundation for enhancing infrastructure resilience and strengthening disaster risk management strategies.

This study presents a novel, sustainable stabilisation method that alkaline-activates two industrial wastes, rice husk ash (RHA) and silica fume (SF), using low-carbon magnesium hydroxide (Mg(OH)(2)). The aim is to transform these waste materials into effective binders for enhancing the strength and durability of expansive clay. The soil was treated with optimal dosages of additives (11% RHA and 7% SF, each with 3% magnesium hydroxide) and subjected to a series of geotechnical tests including unconfined compressive strength (UCS), swell potential, consolidation, and Atterberg limits after 7 and 28 days of curing. Microstructural changes were analysed using X-ray-diffraction, X-ray-fluorescence and scanning-electron-microscopy. The results demonstrated a remarkable improvement in soil properties: UCS increased by 320% and 210% for RHA and SF mixtures, respectively, after 28 days. The plasticity index was reduced by 54% (RHA) and 48% (SF), while swelling potential was nearly eliminated (up to 99.5% reduction). Compressibility also decreased significantly by 71% and 89%. Microstructural analysis confirmed that these enhancements are due to the formation of magnesium silicate hydrate and magnesium aluminate hydrate gels, which densify the soil matrix and refine its pore structure. This research successfully establishes a low-carbon, waste-valorising approach for transforming expansive soils into a stable construction medium.

Monopile foundations are widely used in offshore wind turbines (OWTs) due to their structural simplicity and cost-effectiveness. A comprehensive understanding of the dynamic behavior and seismic resilience of monopile foundations is crucial for optimizing their design and ensuring long-term operational stability in seismically active regions. This study investigates the seismic response of monopile foundation for OWTs, especially considering the pile-soil interactions and the corresponding influence on structural natural frequency. A simplified monopile model is developed and validated through the finite-element analysis. By varying physical properties of typical marine soils and the upper mass, variations in the natural frequency of offshore wind turbine systems are quantified. The subsequent seismic fragility analysis reveals pronounced differences in the fragility between short piles and long piles in both soil conditions. The internal friction angle of sand and the undrained shear modulus of clay are the most influential parameters affecting the natural frequency. The fragility responses of short piles and long piles are also distinct. This study offers insights for the seismic design of monopile foundations for OWTs, mitigating resonance risks while enhancing operational efficiency and safety.

Precise estimation of seepage discharge through heterogeneous earth-fill dams on permeable foundations is essential for dam safety and sustainable water-resources management. This study evaluated the performance of CatBoost for predicting seepage discharge using seven geometric and hydraulic input variables. Three advanced metaheuristic algorithms were employed to optimize the base model. A total of 4374 numerically generated samples were adopted and partitioned into 70% training data and 30% testing data. To reduce dependence on a single hold-out split, a 10-fold cross-validation analysis was also performed. Comprehensive assessment included regression metrics, residual error curves, uncertainty analysis, rank analysis, SHAP-based interpretability, and partial dependence plots (PDPs). The optimized models substantially improved predictive performance over the default CatBoost model. Among them, ASO-CatBoost achieved the best overall performance, reaching nearperfect training accuracy (R2 approximate to 1.000, RMSE = 7.46 & times; 10_ 8 m3/s/m) and excellent testing robustness (R2 = 0.9975, RMSE = 2.49 & times; 10_6 m3/s/m). SHAP and PDP analyses identified upstream slope length, reservoir water depth, and downstream slope length as the dominant variables affecting seepage behavior, whereas crest width and permeability-related variables exerted secondary effects. In addition, desktop and web-based graphical user interfaces were developed to enable real-time prediction, batch evaluation, and reproducible exports. Overall, the ASO-CatBoost model provides a reliable, interpretable, and deployable framework for seepage prediction in heterogeneous earth-fill dams on permeable foundations.

The growing demand for sustainable and lightweight geotechnical fill materials has intensified the need for environmentally responsible alternatives to conventional soil stabilization techniques. This study presents a cost-effective and sustainable approach for producing lightweight geotechnical fills through the waste-derived stabilization of high-plasticity, volume-sensitive clays using expanded polystyrene (EPS) and calcium carbide residue (CCR). A systematic laboratory investigation was carried out to identify the optimal combination of EPS and CCR that achieves a balance between reduced unit weight and adequate mechanical performance. Results demonstrate that a composite comprising 2% EPS and 7.5% CCR produces a lightweight EPS–CCR soil composite (ECSC) with a 14% reduction in maximum dry density relative to untreated clay. Despite the reduction in density, substantial improvements in engineering properties were achieved, with unconfined compressive strength increasing by up to sixfold and soaked California Bearing Ratio increasing by up to 27-fold after 28 days of curing. The stabilized composite also exhibited enhanced consistency characteristics and improved shear strength parameters, indicating effective control of swelling behavior. Microstructural analyses using X-ray diffraction, and scanning electron microscopy (SEM) confirmed the formation of calcium silicate hydrate (C–S–H) and calcium aluminate hydrate (C-A-H) gels, supported by higher Ca, Si, and Al concentrations. These hydration and polymerization processes enhanced the soil structure within the alkaline environment. The findings establish EPS–CCR-treated high-plasticity clays as a viable material for sustainable, lightweight, and cost-effective geotechnical fill and subgrade applications, thereby addressing a critical gap in the reuse of waste materials for fine-grained expansive soils.

Predicting seepage discharge through heterogeneous earthfill dams founded on permeable foundations is essential for dam safety and long-term water-resources sustainability. Accordingly, this study evaluated five machine learning (ML) models viz Decision Tree (DT), Random Forest (RF), Stochastic Gradient Boosting (SGB), Light Gradient Boosting (LGB), and Categorical Gradient Boosting (CGB), to estimate seepage discharge using seven geometric and hydraulic input variables. The dataset was partitioned into training (80%), validation (10%), and testing (10%) subsets. To improve predictive capability, Bayesian Optimization (BO) was applied for hyperparameter tuning. Subsequently, model performance was examined using multiple error metrics, predicted–actual scatter plots, SHapley Additive exPlanations (SHAP) for interpretability, and k-fold cross-validation; finally, a rank-based analysis was used to consolidate the results across evaluation criteria. Overall, the tuned models exhibited substantial performance gains. In particular, the boosting-based methods consistently outperformed the DT and RF baselines. Among all candidates, the CGB model achieved the best overall performance, delivering near-perfect accuracy (R² = 0.9981 on validation and R² = 0.996 on testing). Moreover, cross-validation confirmed its robustness, as it produced the lowest RMSE values across the 10 folds. SHAP-based analysis further indicated that reservoir water depth and the core-to-shell hydraulic conductivity ratio are the dominant drivers of seepage behavior, whereas dam crest width and foundation depth exert secondary influence. To support practical use, a standalone desktop GUI was also developed to enable instant predictions with flexible input formats, batch evaluation, and reproducible export functions. Collectively, these results demonstrate that the CGB model provides a reliable and deployable framework for seepage prediction, maintaining prediction differences below 10% relative to prior numerical and empirical references across varying water depths.

Purpose. This study integrates unmanned aerial vehicles (UAVs) and Magnetic and 2D Electrical Resistivity Tomography (2D ERT) surveys to delineate subsurface laterite ore mineralization zones while reducing exploration costs. UAV imagery enabled the reconstruction of high-resolution digital elevation models and orthomosaics, providing detailed topographic information for survey planning. Methods. Magnetic survey integrated with 2D electrical resistivity tomography (ERT) profiles were applied to characterize subsurface lithology and identify layers such as topsoil, shale, and laterite. Findings. The ERT survey reveals that the topsoil is 3 meters thick and has a resistivity range of 10-50 Omega.m. The resistivity of shale varied between 50 and 150 Omega.m, with a thickness of two meters. The laterite ore was identified with resistivity values between 150 and 1200 Omega & centerdot;m and a thickness of 5 m. Magnetic surveys identified magnetic anomalies of 200-600 nT, estimated at a depth of 3-5 m using forward modeling. Regional-scale interpretations from total magnetic intensity (TMI), reduced-topole (RTP), and continuation maps highlighted the detailed distribution of the magnetic anomalies throughout the study area, lithological variations, fault systems, and deep-seated magnetized bodies. Originality. This study demonstrates an integrated, low-cost workflow for lateritic mineralization that uses detailed geophysical data, including magnetic methods, 2D ERT, and UAV photogrammetry. Conventional techniques are quite expensive relative to the value of the ore. Practical implications. The results demonstrate that the integration of aerial photogrammetry, magnetic surveys, and 2D electrical resistivity tomography provides an efficient and cost-effective approach for delineating laterite ore mineralization zones and can serve as a viable alternative to conventional exploration methods.

Biopolymer stabilization potentially offers a low-carbon alternative to traditional binders, yet mineral-specific soil–biopolymer interaction mechanisms remain unclear. This study examines four mineralogically distinct soils montmorillonite (M), kaolinite (K), their composite (KM), and silty sand (SM) treated with xanthan gum (XG), polyhydroxyalkanoate (PHA), and their binary blend (XG_PHA). To isolate mineralogical effects, biopolymer dosage, compaction energy, and curing regime were kept constant. Mechanical, thermal, spectroscopic, and microstructural analyses were conducted to resolve mineral-specific interaction pathways. Strength responses followed intrinsic mineral reactivity (M > KM > K), whereas SM exhibited the largest relative gain because of its initially loose fabric. Ductility decreased in mineral-reactive soils but increased in SM owing to fabric modification. XG_PHA consistently provided the greatest strength, stiffness, and energy absorption. Thermal analysis showed a distinct PHA degradation peak (≈250–300 °C) in M and KM, but a weak or absent peak in K and SM, indicating mineral-dependent polymer retention. BET, PSD, and FTIR confirmed mineral-specific interactions, with reactive soils showing pronounced surface area reduction, mesopore constriction, and stronger adsorption-related spectral features, whereas low-reactivity soils showed moderate surface modification and weaker chemical signatures. XRD indicated non-intercalative, surface-controlled interactions, while SEM revealed densified polymer-associated fabrics, consistent with adsorption-derived gels in reactive soils and surface film encapsulation in low-reactivity soils. Three stabilization pathways emerged: adsorption-derived gel network bridging and pore densification (M, KM), adsorption-limited surface gel formation and film encapsulation (K, SM), and mineralogy-dependent synergistic dual-polymer stabilization in XG_PHA. These findings identify XG_PHA as a mineral-responsive stabilizer for sustainable ground improvement.

The time-dependent changes in the mechanical properties of poorly graded sandy soil from the banks of the Soan River, Pakistan, treated with microbially induced calcite precipitation (MICP) using indigenous Bacillus species, have been evaluated. MICP treatment has been applied over a period of six weeks. The results reveal that the critical time of two weeks of MICP treatment is needed to induce a dramatic change in the mechanical properties of treated soil. At this point, calcite content exceeds 17% and the cementation transitions from non-effective to effective. The angle of internal friction increases drastically during the first two weeks of treatment while the drained cohesion, undrained shear strength, and unconfined compressive strength (UCS) increase significantly after two weeks' time. A consistent reduction trend is observed in both void ratio and permeability, indicating improved soil densification and reduced flow paths. The drained and undrained cohesion of the soil increased from 0 to 11.32 and 60 kN/m(2), respectively, over a period of six weeks. The drained angle of friction increased from 33 degrees to 36.16 degrees while UCS achieved the final value of 120 kN/m(2). The findings provide insights into the critical timing and calcite content required for achieving optimal mechanical performance.

The growing demand for cementitious materials, driven by rapid urban and industrial growth, is accelerating the depletion of natural resources. Simultaneously, the increased use of timber-based products generates large-scale waste, commonly incinerated and producing hazardous biomass residue, raising disposal and environmental concerns. These challenges are prompting research into sustainable, waste-derived alternatives to traditional cement and lime. This study explores the innovative use of thermally activated sawdust ash (SDA), to stabilize Na-contaminated clay for construction purposes while promoting sustainable reuse. Macro-micro-scale experiments including double hydrometer, cation analysis, pH/EC, Atterberg's limits, Unconfined compression (UCS), wetting/drying cycles, XRD and SEM were conducted, accompanied by an in-depth analysis of the associated environmental and economic implications. Results showed that SDA retains substantial amounts of exchangeable cations, like K+/Ca-2(+), which can effectively replace Na+ in clay. The 10 % SDA was the optimum content, reducing dispersion, Na, and plasticity by 81 %, 58 %, and 56 % over 28 days. Moreover, UCS increased by 574 %. SDA-treated soil met the accumulated mass loss (AML) criterion, showing < 6 % mass loss after 12 wetting-drying cycles. A rapid pH and EC increase indicates strongly alkaline conditions, which reduces the thickness of the diffuse double layer (DDL). XRD/SEM/EDS analyses confirmed that SDA induced particle flocculation and promoted the formation of cementitious compounds like C-S-H/C-A-H, enhancing soil stability and durability. Replacing cement/lime with SDA waste can save approximately 82,768 kg/km of carbon emissions. This research contributes to sustainable construction practices by repurposing SDA, reducing environmental impacts, and supporting circular economy principles.

This study presents a novel intelligent approach for predicting the unconfined compressive strength (UCS) of fine-grained natural soils by utilizing machine learning (ML) techniques such as Gradient Boost (GB), random forest (RF), and Extreme Gradient Boost (XGB) on a large dataset obtained from multiple sources. A comprehensive testing initiative was conducted to assess the UCS, sieve analysis, Atterberg limits, and specific gravity of natural soils. To overcome the limitations of existing UCS predictive models in covering output variability for the fine-grained natural soil deposit, a diversity of input parameters defining natural soil attributes, such as the percentage of fines, sand, plasticity index (PI), specific gravity (Gs), and liquid limit (LL), were employed. Multiple ML models were developed through Python code with varying algorithm inputs, and the models with the best predicting abilities were analyzed. The ability of the ML models to predict based on the number of statistical performance indices (SPIs) such as correlation indices, i.e., coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and Pearson correlation coefficient (PCC); and error indices, i.e., root mean square error (RMSE), Willmott index (WI), and mean absolute error (MAE), were analyzed and found to be reasonable based on SPIs. Based on the rank analysis of SPIs, the XGB model was proposed to predict the UCS value of natural soils. Sensitivity and parametric analyses revealed that LL has the most significant effect on prediction in the proposed model, pursued by PI, fines, sand, and Gs. The proposed XGB approach is a potentially effective asset to geologists and engineers to predict the UCS for new datasets of natural soils and liquid limits ranging between 20 and 40.

This article proposes Styrene-Butadiene Rubber (SBR) and Chem-lite CR Powder (CCP) as a sustainable solution for dispersive clays, which cause infrastructure damage due to high sodium ions. Traditionally utilized stabilizers like lime/cement raise environmental concerns due to their high carbon footprints. Regarding this, SBR/CCP has been used in concrete technology for several functions; nevertheless, its effectiveness for stabilizing dispersive clay remains uncertain. Therefore, this study investigated how SBR/CCP improved sodium-rich dispersive soil's dispersion, index, mechanical characteristics, and associated mechanism. Multiple tests, including double hydrometer, cation analysis, compression strength (UCS), physio-chemical, Atterberg's limits, California Bearing Ratio (CBR), X-Ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive X-Ray spectroscopy (EDS) were performed at different mixing ratios up to curing of 60-d. The results showed a significant reduction in dispersion (61.7%), sodium (38%), and plasticity (50.4%) with an optimal 1.5% SBR-3% CCP mix after 28-d, converting the clay to a non-dispersive type. UCS and soaked CBR improved by 283% and 579%, respectively. Micro analyses revealed soil enhancement through CCP's flocculation, ion exchange, and pozzolanic reactions, while SBR-coated particles and filled pores formed reticulated membrane systems. SBR/CCP offers a sustainable/eco-friendly alternative for stabilizing dispersive clays with a lower carbon footprint. (c) 2025 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).

The exposure of soil-geomembrane interfaces to environmental temperature variations poses potential failure risks in geomembrane-lined structures, such as landfill side walls and water reservoirs. Despite recent studies, the behavior of these interfaces under monotonic temperature cycles remains unclear. This study aims to address this gap by examining the mechanical response, including interfacial properties and shear strength, of a soilgeomembrane interface subjected to stable (20 degrees C), low (5-20 degrees C), and high (50-20 degrees C) temperature cycles under various stress conditions for long-term performance. A series of interface direct shear tests were conducted on sand and high-density polyethylene (HDPE) geomembrane samples across four consecutive temperature cycles. Results indicated a reduction in shear strength at stable temperature cycles, ranging from 14.51 % after the first cycle to 18.57 % after the fourth cycle under 100 kPa normal stress, due to changes in the contact zone and failure mechanisms like sliding and plowing. Conversely, low-temperature cycles had minimal impact on shear strength, as the geomembrane's mechanical properties remained stable. The most significant reductions in shear strength, ranging from 21.10 % (after the first cycle) to 27.19 % (after the fourth cycle), were observed at higher temperature cycles, highlighting that elevated temperature cycles substantially compromise the shear strength of the soil-geomembrane interface due to the micro-degradation of geomembrane. This study highlights the necessity of considering temperature cycling in the design of geomembrane-lined infrastructure. It also recommends employing a geomembrane with high thermal resistance, typically associated with higher thickness, to ensure the infrastructure's durability and long-term performance.

Numerous studies have utilized geotextiles with only 2–3 tensile strengths, despite the availability of non-woven geotextiles (NWG) with over 10 tensile strengths in the market. Identifying the most cost-efficient option for optimal performance is crucial. This study conducted a series of California Bearing Ratio (CBR) tests to measure the strength of subgrade soil reinforced with NWG of 10 different tensile strengths (10, 25, 35, 50, 65, 80, 100, 115, 123, and 140 kN/m) under both unsoaked and soaked conditions. The aim was to determine the optimal geotextile tensile strength for maximum benefits. Experimental results showed significant improvements in subgrade CBR, ranging from 15.13 to 110.18

Excessive and uncontrolled seepage through an earth-fill dam is detrimental to its stability. Numerous researchers have developed empirical equations based on the finite element method (FEM) to predict the seepage rate through a specific type of earth-fill dam resting on an impervious foundation. However, the empirical correlation for seepage calculation across a non-homogenous earth dam lying on a pervious foundation is yet to be investigated. This study formulated an empirical equation to compute the seepage discharge through a non-homogenous earth-fill dam resting on a permeable foundation. Seepage analysis was performed on the SEEP/W program with three different values of each physical and geometrical parameter of the embankment (upstream slope, dam’s height, downstream slope, crest width, core’s slope, depth of pervious foundation, free board, permeability of foundation material, and permeability ratio of core-to-shell material). A total of 4374 dam models were analyzed to obtain the seepage datasets. The comparison of seepage values obtained from the SEEP/W program with the analytical equations revealed an average percentage error of less than 15

The interpretation methods used to predict ultimate pile capacity from load-settlement curves yield varied results due to their unique assumptions and limitations. Misinterpreting ultimate pile capacity can negatively impact the safety and economy of deep foundations, which often consist of numerous piles. This study examines a database of 93 static pile load tests on axially loaded bored concrete piles to identify suitable methods for predicting ultimate pile capacity. Specifically, 43 static pile load tests from Pile Database A were analyzed to compare ultimate capacities derived from theoretical methods, twelve pile load test interpretation techniques, and numerical analysis using PLAXIS 3D. For the numerical analysis, all bored piles in Database A were loaded until a pile head settlement equal to 8

The disposal and recycling of expanded polystyrene (EPS) waste pose significant challenges. Although previous research has explored the engineering characteristics of coarse-grained soils mixed with EPS waste for lightweight fill materials, limited attention has been given to fine-grained clayey soils. This study investigates the compaction, strength, and deformation behavior of three types of clayey soils with varying plasticity, mixed with 0.5% to 3.0% shredded EPS content. Experimental results indicate that the addition of shredded EPS to all three clayey soils significantly reduced the maximum dry density (MDD), making them suitable for lightweight fill applications. Direct shear tests revealed a decrease in cohesion and an increase in the angle of internal friction as the shredded EPS content increased. The unconfined compressive strength (UCS) initially increased with EPS content, then decreased after reaching an optimal EPS proportion. The addition of shredded EPS also enhanced the workability of the clayey soils, as evidenced by a reduction in their plasticity indices. Based on these findings, a framework is proposed to determine the optimum shredded EPS content, which provides the best balance between strength and reduced unit weight for different clayey soils. This research highlights the potential of EPS waste as a sustainable additive for improving the properties of clayey soils, making them more efficient and eco-friendly for construction purposes, particularly in transportation geotechnics for road embankments and subgrade stabilization.

This study developed artificial intelligence models to predict Atterberg's limits, specifically the liquid (LL) and plastic limits (PL), based on #200 sieve analysis, which is a laborious and challenging task in the laboratory. Conventional methods use #40 (0.425 mm) sieve material, which contains fine sand that causes discrepancies, whereas using #200 (0.075 mm) sieve material is essential for accurate LL/PL determination. This study introduces novel Artificial Neural Network (ANN) and Multivariate Regression (MLR) based models for LL/PL to mitigate these constraints. For this study, soil samples were collected from 120 locations, and tests such as sieve analysis, hydrometer analysis, and Atterberg’s limits using #40 and #200 sieves were conducted. Then, AI-based predictive models were developed. The results showed significantly higher LL/PL values for #200 sieve material than #40, with about 45

Seepage analysis of earth dams is essential for design and efficient control measures. An earth dam can be rendered ineffective if it exhibits uncontrolled seepage through foundations and if subjected to uplift. A variety of methods have been developed to determine seepage and uplift pressures under embankments, but results exhibit differences. In this study an overview of graphical, numerical, analytical, and physical model methods to determine seepage and uplift pressure has been provided. The underlying methods i.e., D. Forchheimer flow nets, Element Methods Finite (FEM) solutions from SLIDE software, Velocity Hodographic and Schwartz Christoffel transformations, and seepage tank model respectively have been applied on Sukian Dyke of Mangla Dam as a case study. It is conclusive that all methods provide overestimates for seepage values, however FEM provides results closer to actual field values. Statistical analysis was performed to rank the most suitable method for determining uplift pressure. D. Forchheimer flow nets and transformations predict uplift pressures closer to field values. The crucial aspect of the study is to quantify the accuracy of the prediction methods.