Search Results (277)

The constitutive modelling of granular soils has been a long-standing research subject in geotechnical engineering, and machine learning (ML) has recently emerged as a promising tool for achieving this goal. This paper proposes two recurrent neural networks, namely, the Gated Recurrent Unit Neural Network (GRU-NN) and the Long Short-Term Memory Neural Network (LSTM-NN), which utilize input parameters such as the initial void ratio, initial fabric anisotropy, uniformity coefficient, mean particle size, and confining pressure to establish the high-dimensional relationships of granular soils from micro to macro levels subjected to triaxial shearing. The research methodology consists of several steps. Firstly, 200 numerical triaxial tests on idealized granular soils comprising polydisperse spherical particles are performed using the discrete element method (DEM) simulation to generate datasets and to train and test the proposed neural networks. Secondly, LSTM-NN and GRU-NN are constructed and trained, and their prediction performance is evaluated by the mean absolute percentage error (MAPE) and R-square against the DEM-based datasets. The extremely low error values obtained by both LSTM-NN and GRU-NN indicate their outstanding capability in predicting the constitutive behaviour of idealized granular soils. Finally, the trained ML-based models are applied to predict the constitutive behaviour of a miniature glass bead sample subjected to triaxial shearing with in situ micro-CT, as well as to two extrapolated test sets with different initial parameters. The results show that both methods perform well in capturing the mechanical responses of the idealized granular soils. Full article

Vertical low-permeability barriers are widely used to improve the stability and seepage resistance of flood embankments. The present study evaluates three barrier technologies—vibrating beam slurry walls (VBSWs), deep soil mixing (DSM), and low-pressure grout injection (LPG)—through a series of consolidated drained triaxial tests and permeability coefficient tests on soil samples collected from the sites where different barrier installation technologies were used. All three barrier installation methods produced substantial improvements in both mechanical and hydraulic performance: the effective angle of internal friction (φ′) increased by 3–6° in samples with a plasticity index near 3.5%, and coefficients of permeability dropped from 10⁻⁸–10⁻⁷ m/s in untreated soils to below 10⁻⁹ m/s in treated specimens. The key finding of the study is that the barrier performance varies by the technology and the soil type. According to the result, DSM is the most effective technology used in clay-rich soils (φ′ increased up to 4°); LPG achieved the lowest permeability (7 × 10⁻¹¹ m/s) in granular soils; and VBSWs balanced strength and impermeability, most effective in silty sands. Flow-pump tests further demonstrated that treated soils required much longer to stabilize under a constant flow rate and could sustain higher hydraulic gradients before reaching equilibrium. These findings show the importance of matching barrier technology to soil plasticity and liquidity characteristics and highlight saturation as essential for reliable laboratory evaluation. The results provide a scientific basis for selecting and designing vertical barriers in flood-preventing infrastructure, offering performance benchmarks for improving hydraulic and geotechnical structures. Full article

This study investigates the effectiveness of combining helical piles (HPs) with geogrid reinforcement compared to conventional piles in improving pullout performance in dense sand, addressing a key challenge in reinforced foundation design. A comprehensive experimental program was conducted to evaluate the pullout behavior of HPs embedded in Shahriyar sand reinforced with geogrid layers. The research focused on quantifying the effects of critical parameters—pile configuration, helix pitch, and geogrid placement depth—on ultimate pullout capacity and displacement response to better understand hybrid reinforcement mechanisms. Pullout tests were performed using a Zwick/Roell Z150 universal testing machine with automated data acquisition via TestXpert11 V3.2 software. The experimental program assessed the following influences: (1) pile configurations—plain, single-helix, and double-helix; (2) helix pitch ratios of 1.00, 1.54, and 1.92 (pitch-to-shaft diameter); and (3) geogrid placement depths of 7.69, 11.54, and 15.38 (depth-to-shaft diameter) on pullout behavior. Results demonstrate that geogrid reinforcement substantially enhances pullout resistance, with single-helix HPs achieving up to a 518% increase over plain piles. Pullout resistance is highly sensitive to geogrid spacing, with optimal performance at a non-dimensional distance of 0.47 from the pile–soil interface. Additionally, double-blade HPs with geogrid placed at 0.35 exhibit a 62% reduction in displacement ratio, underscoring the role of geogrid in improving pile stiffness and load-bearing capacity. These findings provide new insights into the synergistic effects of helical pile geometry and geogrid placement for designing efficient reinforced granular foundations. Full article

Shallow foundations are widely used in both terrestrial and marine environments, supporting critical structures such as buildings, offshore wind turbines, subsea platforms, and infrastructure in coastal zones, including piers, seawalls, and coastal defense systems. Accurately determining the soil bearing capacity for shallow foundations presents a significant challenge, as it necessitates considerable resources in terms of materials and testing equipment, as well as a substantial amount of time to perform the necessary evaluations. Consequently, our research was designed to approximate the forecasting of soil bearing capacity for shallow foundations using machine learning algorithms. In our research, four ensemble machine learning algorithms were employed for the prediction process, benefiting from previous experimental tests. Those four models were AdaBoost, Extreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRTs), and Light Gradient Boosting Machine (LightGBM). To enhance the model’s efficacy and identify the optimal hyperparameters, grid search was conducted in conjunction with k-fold cross-validation for each model. The models were evaluated using the R² value, MAE, and RMSE. After evaluation, the R² values were between 0.817 and 0.849, where the GBRT model predicted more accurately than other models in training, testing, and combined datasets. Moreover, variable importance was analyzed to check which parameter is more important. Foundation width was the most important parameter affecting the shallow foundation bearing capacity. The findings obtained from the refined machine learning approach were compared with the well-known empirical and modern machine learning equations. In the end, the study designed a web application that helps geotechnical engineers from all over the world determine the ultimate bearing capacity of shallow foundations. Full article

This study investigates the mechanisms of gas-containing tectonic coal outbursts by modeling tectonic coal and gas as analogous to soil and pore water. Analytical methods from soil mechanics, specifically those related to quicksand and seismic liquefaction, are employed to classify these outbursts into two types: “quicksand type” and “fluidization type.” Their formation mechanisms are elucidated based on a fracture network model and a one-dimensional seepage failure criterion developed for tectonic coal. The findings indicate that “quicksand type” outbursts result from the continuous detachment of tectonic coal slices within the pressure relief zone under gas seepage pressure. The thickness-to-radius ratio of these coal slices increases with rising gas pressure but decreases with increasing coal strength and normal geostress. A larger thickness-to-radius ratio signifies a more pronounced granular characteristic and accelerates the development of coal and gas outbursts. “Fluidization type” outbursts occur when the effective stress drops to zero, resulting in a complete loss of coal strength. These outbursts represent a specific case of “quicksand type” outbursts and can be triggered by vibrations. The susceptibility of tectonic coal to outbursts is attributed to its low mechanical strength and the presence of dense fractures, which increase the acting area of seepage pressure and, consequently, raise the overall seepage force. According to this analysis, the depth of outburst cavities is generally less than the width of the pressure relief zone, which can result in delayed outbursts. This study enhances the understanding of quicksand and seismic liquefaction theories in soil mechanics and provides valuable guidance for predicting and mitigating coal and gas outbursts. Full article

Because artificially cemented granular (ACG) materials employ diverse combinations of aggregates and binders—including cemented soil, low-cement-content cemented sand and gravel (LCSG), and concrete—their stress–strain responses vary widely. In LCSG, the binder dosage is typically limited to 40–80 kg/m³ and the sand–gravel skeleton is often obtained directly from on-site or nearby excavation spoil, endowing the material with a markedly lower embodied carbon footprint and strong alignment with current low-carbon, green-construction objectives. Yet, such heterogeneity makes a single material-specific constitutive model inadequate for predicting the mechanical behavior of other ACG variants, thereby constraining broader applications in dam construction and foundation reinforcement. This study systematically summarizes and analyzes the stress–strain and volumetric strain–axial strain characteristics of ACG materials under conventional triaxial conditions. Generalized hyperbolic and parabolic equations are employed to describe these two families of curves, and closed-form expressions are proposed for key mechanical indices—peak strength, elastic modulus, and shear dilation behavior. Building on generalized plasticity theory, we derive the plastic flow direction vector, loading direction vector, and plastic modulus, and develop a concise, transferable elastoplastic model suitable for the full spectrum of ACG materials. Validation against triaxial data for rock-fill materials, LCSG, and cemented coal–gangue backfill shows that the model reproduces the stress and deformation paths of each material class with high accuracy. Quantitative evaluation of the peak values indicates that the proposed constitutive model predicts peak deviatoric stress with an error of 1.36% and peak volumetric strain with an error of 3.78%. The corresponding coefficients of determination R² between the predicted and measured values are 0.997 for peak stress and 0.987 for peak volumetric strain, demonstrating the excellent engineering accuracy of the proposed model. The results provide a unified theoretical basis for deploying ACG—particularly its low-cement, locally sourced variants—in low-carbon dam construction, foundation rehabilitation, and other sustainable civil engineering projects. Full article

In various applications, IoT temporal data play a crucial role in accurately predicting future trends. Traditional models, including Rolling Window, SVR-RBF, and ARIMA, suffer from a potential accuracy decrease because they generally use all available data or the most recent data window during training, which can result in the inclusion of noisy data. To address this critical issue, this paper proposes a new forecasting technique called Adaptive Holt–Winters (AHW). The AHW approach utilizes two models grounded in an exponential smoothing methodology. The first model is trained on the most current data window, whereas the second extracts information from a historical data segment exhibiting patterns most analogous to the present. The outputs of the two models are then combined, demonstrating enhanced prediction precision since the focus is on the relevant data patterns. The effectiveness of the AHW model is evaluated against well-known models (Rolling Window, SVR-RBF, ARIMA, LSTM, CNN, RNN, and Holt–Winters), utilizing various metrics, such as RMSE, MAE, p-value, and time performance. A comprehensive evaluation covers various real-world datasets at different granularities (daily and monthly), including temperature from the National Climatic Data Center (NCDC), humidity and soil moisture measurements from the Basel City environmental system, and global intensity and global reactive power from the Individual Household Electric Power Consumption (IHEPC) dataset. The evaluation results demonstrate that AHW constantly attains higher forecasting accuracy across the tested datasets compared to other models. This indicates the efficacy of AHW in leveraging pertinent data patterns for enhanced predictive precision, offering a robust solution for temporal IoT data forecasting. Full article

The present study implements novel innovative geostatistical imaging using precision agriculture (PA) under sugarbeet field conditions. Two driplines layout designs (d.l.d.) and soil water content (SWC)–irrigation treatments (A: d.l.d. = 1.00 m driplines spacing × 0.50 m emitters inline spacing; B: d.l.d. = 1.50 m driplines spacing × 0.50 m emitters inline spacing) were applied, with two subfactors of clay loam and clay soils (laboratory soil analysis) for modeling (evaluation of seven models) TDR multi-sensor network measurements. Different sensor calibration methods [method 1(M1) = according to factory; method 2 (M2) = according to Hook and Livingston] were applied for the geospatial two-dimensional (2D) imaging of accurate GIS maps of rootzone soil moisture profiles, soil apparent dielectric Ka profiles, and granular and hydraulic parameters profiles, in multiple soil layers (0–75 cm depth). The modeling results revealed that the best-fitted geostatistical model for soil apparent dielectric Ka was the Gaussian model, while spherical and exponential models were identified to be the most appropriate for kriging modelling, and spatial and temporal imaging was used for accurate profile SWC ${\theta v}_{TDR}$(m³·m⁻³) M1 and M2 maps using TDR sensors. The resulting PA profile map images depict the spatio-temporal soil water and apparent dielectric Ka variability at very high resolutions on a centimeter scale. The best geostatistical validation measures for the PA profile SWC ${\theta v}_{TDR}$ maps obtained were MPE = −0.00248 (m³·m⁻³), RMSE = 0.0395 (m³·m⁻³), MSPE = −0.0288, RMSSE = 2.5424, ASE = 0.0433, Nash–Sutcliffe model efficiency NSE = 0.6229, and MSDR = 0.9937. Based on the results, we recommend d.l.d. A and sensor calibration method 2 for the geospatial 2D imaging of PA GIS maps because these were found to be more accurate, with the lowest statistical and geostatistical errors, and the best validation measures for accurate profile SWC imaging were obtained for clay loam over clay soils. Visualizing sensors’ soil moisture results via geostatistical maps of rootzone profiles have practical implications that assist farmers and scientists in making informed, better and timely environmental irrigation engineering decisions, to save irrigation water, increase water use efficiency and crop production, optimize energy, reduce crop costs, and manage water resources sustainably. Full article

Accurate measurement and understanding of the spatiotemporal distribution of soil water content (SWC) are crucial in various environmental and agricultural sectors. The present study implements a novel precision agriculture (PA) approach under sugarbeet field conditions of two moisture-irrigation treatments with two subfactors, clay loam (CL) and clay (C) soils, for geostatistics modeling (seven models’ evaluation) of time domain reflectometry (TDR) multisensor network measurements. Two different sensor calibration methods (M1 and M2) were trialed, as well as the results of laboratory soil analysis for geospatial two-dimensional (2D) imaging for accurate GIS maps of root zone moisture profiles, granular, and hydraulic profiles in multiple soil layers (0–75 cm depth). Modeling results revealed that the best-fitted semi-variogram models for the granular attributes were circular, exponential, pentaspherical, and spherical, while for hydraulic attributes were found to be exponential, circular, and spherical models. The results showed that kriging modeling, spatial and temporal imaging for accurate profile SWC ${\theta v}_{TDR}$ (m³·m⁻³) maps, the exponential model was identified as the most appropriate with TDR sensors using calibration M1, and the exponential and spherical models were the most appropriate when using calibration M2. The resulting PA profile maps depict spatiotemporal soil water variability with very high resolutions at the centimeter scale. The best validation measures of PA profile SWC ${\theta v}_{TDR}$ maps obtained were Nash-Sutcliffe model efficiency NSE = 0.6657, MPE = 0.00013, RMSE = 0.0385, MSPE = −0.0022, RMSSE = 1.6907, ASE = 0.0418, and MSDR = 0.9695. The sensor results using calibration M2 were found to be more valuable in environmental irrigation decision-making for a more accurate and timely decision on actual crop irrigation, with the lowest statistical and geostatistical errors. The best validation measures for accurate profile SWC ${\theta v}_{TDR}$ (m³·m⁻³) maps obtained for clay loam over clay soils. Visualizing the SWC results and their temporal changes via root zone profile geostatistical maps assists farmers and scientists in making informed and timely environmental irrigation decisions, optimizing energy, saving water, increasing water-use efficiency and crop production, reducing costs, and managing water–soil resources sustainably. Full article

Stormwater carries significant amounts of pollutants—including metals, microorganisms, organic micropollutants, and nutrients—from land surfaces into nearby water bodies, leading to water quality deterioration and threats to both human health and ecosystems. The removal of these contaminants is essential not only for environmental protection, but also to enable the reuse of treated water for various beneficial applications. Common treatment methods include bioretention systems, biofiltration, constructed wetlands, rain gardens, swales, and permeable pavements. To improve pollutant removal efficiency, adsorbent materials are often incorporated into the soil substrate of these treatment devices. However, most research on adsorbents has focused on their effectiveness against one or two specific pollutants and has been conducted under static, short-term laboratory conditions rather than dynamic, field-relevant scenarios. Column-based dynamic filtration type studies, which are more informative for field applications, are limited. In one study, a combination of two or more adsorbents with contrasting properties that matched the affinity preferences of the different pollutants to the substrate media removed 77–100% of several heavy metals that occur in real stormwater compared to 38–73% removal with only one adsorbent. In another study, polycyclic aromatic hydrocarbon removal with zeolite was only 30–50%, but increased to >99% with 0.3% granular activated carbon addition. Long-term dynamic column-based filtration experiments and field studies using real stormwater, which contains a wide range of pollutants, are recommended to better evaluate the performances of the combined adsorbent systems. Full article

Internal soil erosion caused by water infiltration around defective buried pipes poses a significant threat to the long-term stability of underground infrastructures such as pipelines and highway culverts. This study employs a coupled computational fluid dynamics–discrete element method (CFD–DEM) framework to simulate the detachment, transport, and redistribution of soil particles under varying infiltration pressures and pipe defect geometries. Using ANSYS Fluent (CFD) and Rocky (DEM), the simulation resolves both the fluid flow field and granular particle dynamics, capturing erosion cavity formation, void evolution, and soil particle transport in three dimensions. The results reveal that increased infiltration pressure and defect size in the buried pipe significantly accelerate the process of erosion and sinkhole formation, leading to potentially unstable subsurface conditions. Visualization of particle migration, sinkhole development, and soil velocity distributions provides insight into the mechanisms driving localized failure. The findings highlight the importance of considering fluid–particle interactions and defect characteristics in the design and maintenance of buried structures, offering a predictive basis for assessing erosion risk and infrastructure vulnerability. Full article

This study proposes an advanced thermodynamic energy equation to accurately simulate the stress–dilatancy relationship in granular soils for both uncrushed and crushed sands. Traditional energy formulations primarily consider dissipation energy and often neglect the role of free energy. Recent developments have introduced free energy components to account for plastic energy contributions from dilation and particle crushing. However, significant discrepancies between theoretical predictions and experimental observations remain, largely due to the omission of complex mechanisms such as contact network rearrangement, force-chain buckling, grain rolling, rotation without slip, and particle crushing. To address these gaps, the proposed model incorporates dual exponential decay functions into the free energy framework. Rather than explicitly modeling each mechanism, this formulation aims to phenomenologically capture the interplay between fundamentally opposing thermodynamic forces arising from complex mechanisms during granular microstructure evolution. The model’s applicability is validated using the experimental results from both uncrushed silica sand and crushed calcareous sand. Through extensive comparison with over 100 drained triaxial tests on various sands, the proposed model shows substantial improvement in reproducing stress–dilatancy behavior. The average discrepancy between predicted and measured $\eta –D$ relationships is reduced to below 15%, compared to over 60% using conventional models. This enhanced energy equation provides a robust and practical tool for predicting granular soil behavior, supporting a wide range of geotechnical engineering applications. Full article

Quantitative greenhouse gas (GHG) budgets for Mediterranean pepper cultivation are still missing, limiting evidence-based nitrogen management. Furthermore, mitigation value of fertigation respect to granular fertilization in vegetable systems remains uncertain. This study therefore compared the GHG footprint and productivity of ‘papaccella’ pepper under two nitrogen fertilization methods: granular fertilization versus low-frequency fertigation with urea, each supplying about 63 kg N ha⁻¹. Eight automated static chambers coupled to a cavity ring-down spectrometer monitored soil CO₂ and N₂O fluxes throughout the season. Cumulative emissions did not differ between treatments (CO₂: 811 ± 6 g m⁻² vs. 881 ± 4 g m⁻²; N₂O: 0.038 ± 0.008 g m⁻² vs. 0.041 ± 0.015 g m⁻², fertigation vs. granular), and marketable yield remained at ~11 t ha⁻¹, leaving product-scaled global warming potential (GWP) unchanged. Although representing less than 2% of measured fluxes, “hot moments,” burst emissions exceeding four standard deviations (SD) from the mean, accounted for up to 4% of seasonal CO₂ and 19% of N₂O. Fertigation doubled the frequency of these events but reduced their peak magnitude, whereas granular application produced fewer but more extreme bursts (>11 SD). Results showed that fertigation did not mitigate GHGs emission nor improve productivity for Mediterranean pepper, mainly due to the low application frequency and the use of a urea fertilizer. Moreover, we can highlight that in horticultural systems, omitting ‘hot moments’ leads to systematic underestimation of emissions. Full article

The accurate modeling of dynamic soil–structure interaction processes under impact loading is critical for advancing the design of soil-embedded barrier systems. Full-scale crash testing remains the benchmark for evaluating barrier performance; however, such tests are costly, logistically demanding, and subject to variability that limits repeatability. Recent advancements in computational methods, particularly the development of large-deformation numerical schemes, such as the multi-material arbitrary Lagrangian–Eulerian (MM-ALE) and smoothed particle hydrodynamics (SPH) approaches, offer viable alternatives for simulating soil behavior under impact loading. These methods have enabled a more realistic representation of granular soil dynamics, particularly that of the Manual for Assessing Safety Hardware (MASH) strong soil, a well-graded gravelly soil commonly used in crash testing of soil-embedded barriers and safety features. This soil exhibits complex mechanical responses governed by inter-particle friction, dilatancy, confining pressure, and moisture content. Nonetheless, the predictive fidelity of these simulations is governed by the selection and implementation of soil constitutive models, which must capture the nonlinear, dilatant, and pressure-sensitive behavior of granular materials under high strain rate loading. This review critically examines the theoretical foundations and practical applications of a range of soil constitutive models embedded in the LS-DYNA hydrocode, including elastic, elastoplastic, elasto-viscoplastic, and multi-yield surface formulations. Emphasis is placed on the unique behaviors of MASH strong soil, such as confining-pressure dependence, limited elastic range, and strong dilatancy, which must be accurately represented to model the soil’s transition between solid-like and fluid-like states during impact loading. This paper addresses existing gaps in the literature by offering a structured basis for selecting and evaluating constitutive models in simulations of high-energy vehicular impact events involving soil–structure systems. This framework supports researchers working to improve the numerical analysis of impact-induced responses in soil-embedded structural systems. Full article

Quicksand initiation in saturated sandy soils represents a critical geohazard with significant implications for geotechnical infrastructure stability. Despite its importance, the granular-scale mechanisms driving the physical state transitions during quicksand remain insufficiently understood. This study employs a custom-designed hydrodynamic seepage testing system to investigate these mechanisms, enabling precise regulation of hydrodynamic velocity and real-time monitoring of pressure variations. Through experiments on quartz sand specimens with varying particle gradations, we demonstrate that particle gradation primarily governs quicksand susceptibility, while hydrodynamic velocity controls its initiation timing and exhibits a linear correlation with seepage discharge. The quicksand process evolves through three distinct stages: self-consolidation, reorganization, and quicksand initiation, with the reorganization stage identified as the pivotal phase where particle rearrangement dictates system stability. These findings elucidate the intrinsic physical mechanisms of quicksand as a hydraulic failure phenomenon, offering valuable insights for predictive modeling and geohazard mitigation in granular media. Full article