Search Results (1,713)

The pore pressure coefficient B, defined as the change in pore pressure per unit increment of confining pressure under undrained conditions, is a fundamental parameter in soil mechanics. It characterizes the coupling between soil skeleton deformation and pore water pressure and plays a critical role in establishing the effective stress framework for frozen soils. Existing studies mainly focus on unfrozen soils, while the temperature sensitivity and stress-path dependence of B in frozen soils undergoing phase transition remain insufficiently understood. To address this gap, this study conducts temperature-controlled triaxial tests and constant strain-rate loading tests to investigate the evolution of B in frozen sandy silt over a temperature range of −11 °C to −2 °C under different stress histories. The results show that: (1) post-loading B-values at −5 °C to −8 °C are significantly higher than those at −2 °C and −10 °C, by 6.5% and 8.2%, respectively; (2) within the framework of Gassmann’s equation, a theoretical model incorporating the soil freezing characteristic curve and the coupled effects of ice–water phase transition and soil skeleton deformation is developed to explain the temperature-dependent behavior of unfrozen water and B; and (3) a predictive model incorporating a temperature correction factor is proposed, which accurately captures the variation trend of B in frozen sandy silt. This study elucidates the evolution mechanism of the pore pressure coefficient under multi-field coupling conditions and provides a theoretical basis for frost heave assessment and constitutive modeling in cold-region engineering. Full article

Utility-scale photovoltaic (PV) plants rely on supervisory control and data acquisition (SCADA) streams for performance verification, yet high-frequency measurements are routinely affected by sensor dropouts, intermittency, and operating-state transitions that bias regression-based capacity estimates. This study evaluates a reproducible SCADA processing workflow for capacity-style reporting and a complementary soiling–clean temperature prediction model using data from a documented October 2022 test window (5 s SCADA aggregated to 1 min). The following three filtering approaches are compared: (i) naïve thresholds (Baseline A), (ii) deterministic stability screening using ramp-rate and rolling-variability constraints (Baseline B), and (iii) an optional residual-based outlier trimming step (Method C). Capacity is estimated via a multivariate regression evaluated on a fixed-size reporting-condition subset (RC197) with day-coverage constraints. All methods achieved high fit quality on RC197 ($R^2\approx 0.99$), with Baseline B improving error and uncertainty over Baseline A (RMSE 2.05 vs. 2.18 MW; $U_{95}$ 0.97% vs. 1.03%) while preserving day coverage; Method C yielded the lowest in-sample RMSE (1.89 MW) but reduced day coverage. For temperature prediction, a baseline-plus-residual learning formulation substantially improved leave-one-day-out performance, reducing MAE/RMSE from 2.99/3.76 °C to 1.43/1.80 °C and increasing $R^2$ from 0.60 to 0.91. The results highlight trade-offs between fit tightness and representativeness in capacity-style filtering and demonstrate residual learning is an effective approach for SCADA-based thermal characterization. Full article

In highway widening projects, the wet–dry cycling effect of weakly expansive soil fill under seasonal groundwater fluctuations exacerbates differential settlement. This study establishes a three-dimensional numerical model for a widened road with weakly expansive soil, based on a redeveloped numerical method and actual engineering projects. Through multi-scenario numerical simulations, the influence patterns and weighting factors of widening methods, road height, and water level on differential settlement were clarified. Three safety levels for differential settlement were defined using 6 cm and 12 cm as thresholds. A prediction model based on support vector machines was established to determine the combined threshold limits of key parameters under different differential settlement boundaries. The control effectiveness of sand replacement, water-blocking layers, and wicking geotextiles was comparatively evaluated: sand replacement reduces differential settlement by approximately 70% on average and is applicable to all scenarios; water-blocking layers reduce settlement by about 50% and are more suitable for bilateral widening or unilateral widening of low embankments; wicking geotextiles are unsuitable for controlling differential settlement in high-water-level areas. Selection principles for control methods under different conditions were proposed based on engineering requirements, and field tests validated the effectiveness of the proposed solutions. Full article

Leveraging fungal consortia to degrade heavy oil is an emerging strategy for mitigating/cleaning up environmental pollution. However, many consortia are predominantly evaluated by measuring the biodegradation efficiency of heavy oil, with insufficient attention paid to the mechanistic underpinnings and metabolic pathways. In this study, heavy oil-degrading fungal consortia were developed for potential application in soil bioremediation. Whole-genome sequencing was used to predict the metabolic pathways and interspecific interactions driving heavy oil biodegradation. Three heavy oil-degrading fungal strains, designated Aspergillus corrugatus FH2, Aspergillus terreus FL4, and Alternaria alstroemeriae FW1, were isolated from oil sludge in the Karamay Oilfield in Xinjiang, China. Four consortia were constructed through the combination of two or three strains. The consortium F13 (FH2 + FW1) achieved 72.0% removal of heavy oil in a simulated bioremediation test over 30 days, which was more efficient than other consortia and single strains (59.5–68.5%). Notably, the mean degradation rate of long-chain alkanes (C₂₄–C₂₈) by F13 reached 95.9%. After F13 treatment, the major fractions of heavy oil showed considerable degradation, 87.4% for saturates, 92.0% for aromatics, 69.5% for resins, and 27.3% for asphaltenes. Genome annotation of FH2, FL4, and FW1 revealed the presence of core genes for degradation of n-alkanes and aromatics, e.g., CYP505, frmA, fadB, hmgA, ALDH, and ACSL. These functional genes encoded cross-lineage enzymes, enabling synergistic catabolism of C₁₃–C₂₈ alkanes and aromatics. Our findings indicated that the fungal consortium of A. corrugatus FH2 and Al. alstroemeriae FW1 has remarkable bioremediation potential for heavy oil-contaminated sites. This study provides molecular evidence for the design of targeted interventions to improve soil remediation efficiency with fungal consortia. Full article

To address the limitations of traditional forest above-ground biomass (AGB) retrieval methods—namely, the restricted accuracy of physical models and the limited generalization ability of purely data-driven models—this study proposes a differentiable physical modeling (DPM) approach for forest AGB estimation. The method adopts the water cloud model (WCM) as a physics-based framework, grounded in radiative transfer theory, and integrates C-band synthetic aperture radar (SAR) data with multispectral imagery. Within the PyTorch tensor computation framework, automatic differentiation (AD) is employed to seamlessly couple the WCM with the deep fully connected neural network (DFCNN), enabling a differentiable implementation of the WCM. Using mean squared error (MSE) as the loss function, the neural network parameters are optimized through backpropagation and gradient descent, thereby constructing an end-to-end trainable DPM model that effectively retrieves forest AGB while preserving physical interpretability and generalization capability. To validate the proposed method, two representative test sites were selected: Simao in Pu’er, Yunnan Province, and Genhe in Inner Mongolia. GF-3 PolSAR and RADARSAT-2 data were used to extract backscattering coefficients and compute the radar vegetation index (RVI), while Landsat 8 OLI imagery was employed to calculate the normalized difference vegetation index (NDVI), difference vegetation index (DVI), and soil-adjusted vegetation index (SAVI). These datasets, together with ASTER GDEM, field-measured biomass, and other relevant datasets, were integrated to construct a multisource dataset combining remote sensing and ground observations. The performance of the DPM model was then compared with the traditional WCM and several data-driven models, including the fully connected neural network (FNN), generalized regression neural network (GRNN), RF, and Adaptive Boosting (AdaBoost). The results indicate that the DPM model achieved R² = 0.60, RMSE = 24.23 Mg/ha, Bias = 0.4 Mg/ha, and ubRMSE = 22.43 Mg/ha in Simao, and R² = 0.48, RMSE = 33.29 Mg/ha, Bias = 0.87 Mg/ha, and ubRMSE = 33.28 Mg/ha in Genhe, demonstrating consistently better performance than both the WCM and all tested data-driven models. The DPM model demonstrated consistent performance across ecologically contrasting forest regions. It alleviated the systematic overestimation bias of purely data-driven models and overcame the limitations in predictive accuracy resulting from the simplified structure of the WCM. The differentiability of the WCM enables the loss function errors to be backpropagated through the neural network, thereby allowing the optimization of the physical model parameters. Overall, the DPM framework integrates the advantages of both physical models and data-driven approaches, providing an estimation method with acceptable accuracy for forest AGB retrieval. It also offers theoretical and practical insights for the integration of deep learning and physical knowledge in other research fields. Full article

Predictive Digital Soil Mapping (PDSM) in Eastern Amazonia faces challenges due to its environmental complexity, difficult access, and scarce legacy data. While legacy soil maps contain valuable tacit knowledge, updating them requires methods that can handle uncertainty. This study evaluates the integration of old soil maps with machine learning to update soil information in Tracuateua, Pará, with a specific focus on the performance of ensemble learning and the explicit incorporation of uncertainty metrics in soil mapping units under hydromorphic influence, which, in addition to being difficult to access, are influenced by complex pedogenetic processes. We combined 270 sampling points, equivalent to the total pixels that captured the variability of soil mapping units, with environmental covariates and historical data. Several algorithms were tested, including an ensemble approach, to predict mapping units and quantify uncertainty through entropy and confusion indices. The ensemble model demonstrated improved stability and reduced classification uncertainty compared to single models, particularly in challenging hydromorphic environments. Although accuracy gains were modest, the models captured soil–environment relationships, with climate as: Annual Mean Temperature 22,000 years ago (Tmean_22k), relief: Channel Network Base Level (CNBL and altitude) and organism variables: Land Surface Temperature (LST) emerging as the main predictors. Spatialized uncertainty estimates, expressed through entropy and the confusion index, provide a practical decision-support tool for guiding field surveys and identifying areas of low mapping reliability. By explicitly transferring the pedologist’s mental model—encoded as tacit knowledge in legacy soil maps—into ensemble learning, this study presents a robust and transferable framework for updating soil maps in data-scarce tropical regions, balancing predictive performance, spatial consistency, and uncertainty-aware interpretation. Full article

Dredged Fill Soil, as a primary foundation material in reclamation projects, exhibits complex physical and mechanical properties, characterized by a high plasticity index, high water content, low density, high compressibility, large void ratio, and low bearing capacity. Its creep behavior is highly sensitive to temperature changes. This study systematically investigates the temperature-dependent creep behavior of reclaimed soil from Humen Port through laboratory experiments, theoretical modeling, and experimental validation. Triaxial creep tests conducted at different temperatures (5 °C, 15 °C, 25 °C, 35 °C) show that increasing temperature significantly exacerbates creep deformation: under undrained conditions, creep strain at 35 °C is nearly 300% higher than at 5 °C, while drainage reduces the strain by approximately 29.3%. Based on these results, a Burgers-type creep constitutive model considering temperature effects is developed, incorporating the impact of temperature on viscosity and elastic modulus. The model’s predictions show good agreement with the experimental results (15 °C: R² = 0.9788; 35 °C: R² = 0.9890), confirming the model’s validity. The research findings provide theoretical and practical references for the long-term stability evaluation and engineering design of reclaimed foundations in complex marine environments. Full article

Timely and accurate soil property information is critical for sustainable agriculture and precision nutrient management. Conventional laboratory methods are accurate but costly and labor-intensive, restricting their feasibility for high-density soil mapping. Low-cost, portable near-infrared (NIR) spectroscopy presents a promising alternative for rapid, on-site, and non-destructive soil analysis. This study aimed to evaluate the potential of a low-cost, portable NIR sensor (NeoSpectra) for the quantitative prediction of key soil properties in paddy fields from Southeastern China. The target properties were soil organic matter (SOM), total nitrogen (TN), pH, and particle size fractions (clay, silt, and sand). A total of 995 soil samples were collected from representative paddy fields in the region and spectra measurements were conducted in the laboratory on air-dried samples. We developed and compared the performance of multiple machine learning algorithms, including partial least squares regression (PLSR), Cubist, random forest (RF) and memory-based learning (MBL), to build robust calibration models. The predictive models showed substantial performance for SOM and TN, indicating high accuracy (R² > 0.75, LCCC > 0.85, RPD > 2) for quantitative prediction. Predictions for pH, silt, sand, and clay were less accurate (R² of 0.48–0.53, LCCC of 0.67–0.71, RPD of 1.39–1.49), suggesting the sensor’s utility is limited to indicating general trends for these properties. Among the tested algorithms, MBL consistently provided the most accurate and robust predictions across the majority of soil properties. Our findings demonstrate that the low-cost portable NIR sensor, when coupled with appropriate machine learning algorithms, is a powerful and viable tool for the rapid and reliable estimation of critical paddy soil fertility properties (SOM and TN). This technology has significant potential to support field-level soil health monitoring, precision fertilization strategies, and sustainable land management in the agricultural systems of Southeastern China. Full article

The load transfer method is important for the settlement prediction of axially loaded piles, but in multi-layered complex soils, it lacks analytical solutions. Traditional numerical methods such as the finite element method suffer from strong dependence on mesh generation, time-consuming iterative calculations, and high computational costs for back-analysis. This paper proposes a load transfer analysis model based on a Domain Decomposition Physics-Informed Neural Network. A multi-subnet parallel architecture is adopted to simulate multi-layered soils, solving the problem of inter-layer stress–strain discontinuity through interface coupling and gradient continuity constraints; a non-dimensionalization system and a hard constraint mechanism are introduced to enhance training efficiency and physical consistency; and a two-stage analysis framework comprising surrogate model forward analysis and field data inversion is established. Numerical experimental results indicate that the forward analysis of this model is in high agreement with FEM simulation results, and computational efficiency is improved by six orders of magnitude; based on a small amount of field static load test data, multi-layer soil parameters are accurately inverted, achieving more precise pile settlement prediction than FEM. Comparative analysis validates the effectiveness of the domain decomposition multi-subnet over a single network, demonstrating extensibility to hyperbolic and exponential multi-soil constitutive models. Full article

To optimize the structural parameters of a peanut digging shovel and enhance its operational performance, the forces exerted on the digging shovel were examined through a graphical mechanics approach. This analysis identified the primary structural and operational parameters of the shovel’s design. A numerical simulation model for the working resistance of the shovel was established adopting EDEM (2018) discrete element analysis software and subsequently validated through comparative analysis with field experiment results. Employing the Box–Behnken response surface method, quadratic regression models were constructed with digging resistance and soil non-breakage ratio as the response variables, while forward speed, soil entry angle, and blade tilt angle were taken as the influencing factors. Optimization analysis of these parameters was conducted. The optimization results indicate that with a forward speed of 0.8 m/s, a soil entry angle of 20°, and a blade tilt angle of 40°, the working resistance of the shovel is 1667 N, and the soil non-breakage ratio is 20.56%. The error between the field test results and the predictions from the optimized model was less than 2%, illustrating the feasibility of the model and the optimization outcomes. This study offers a technical reference for future simulation-based optimization of peanut digging shovels. Full article

The rapid development of the oil and gas industry has led to increasingly severe challenges for buried pipelines when crossing complex geological environments. Especially in fault zones induced by seismic action, the pipe–soil interaction mechanism and the rapid judgment of pipeline mechanical response urgently require in-depth research. This study conducted pipe–soil interaction tests on pipeline uplift under seismic-frequency loading, and for the first time, proposed a modified soil-spring method suitable for typical soft clay under seismic wave frequencies of 1–5 Hz. Through numerical simulation, the axial strain response of pipelines under normal fault movement was systematically analyzed. Considering comprehensively various variables such as fault dip angle, seismic wave frequency, internal pipeline pressure and wall thickness variation, this study extracted the maximum and minimum strain characteristics of the pipe top and pipe bottom, established a diversified intelligent prediction system for fault geological hazards, constructed the optimal machine learning model matching the type of normal fault geological hazards, and realized full-process intelligent modeling from model selection to parameter optimization. The research results can provide technical support for the seismic design and safety status prediction of pipelines under normal faulting conditions. Full article

Tillage–residue management is a controllable lever for improving maize yield and system resilience under climate variability. Here we propose a mixed-effects spatiotemporal learning framework (ME-LSTM) that integrates multi-source observations to enable robust yield prediction and management system evaluation across heterogeneous sites and years. First, we construct multi-year sliding-window inputs to represent legacy effects and cumulative influences of past management and environment. Second, a deep temporal encoder learns nonlinear dependencies from climate–soil–remote-sensing sequences to enhance interannual extrapolation. Third, a mixed-effects module explicitly separates management fixed effects from hierarchical random effects (e.g., source/study, site, year, and plot), absorbing source-specific biases and unobserved heterogeneity while improving interpretability. Finally, we parameterize management × climate/soil interactions to quantify system-specific sensitivities to environmental drivers and to support scenario-based comparison and recommendation of management options. Across multi-ecological maize datasets, ME-LSTM achieved an R² of 0.8989 with an RMSE of 309.83 kg ha⁻¹ on the test set. Ablation analyses show that removing remote-sensing features or ground-based temporal information substantially degrades performance, confirming the complementary value of multi-source fusion. Benchmarking against strong temporal baselines (LSTM, GRU, BiGRU, and Transformer) further demonstrates consistent accuracy gains of ME-LSTM, highlighting its suitability for small-sample, noisy, and hierarchically structured agricultural data. Overall, ME-LSTM provides an interpretable and scalable tool for climate-adaptive optimization of tillage–residue management and supports robust, actionable decision-making across diverse agro-ecological conditions. Full article

Machine learning (ML) models can complement traditional measurement-based approaches by supporting large-scale screening, spatial analysis, and prioritization of buildings for testing of indoor radon, a leading cause of lung cancer among non-smokers. Originating from uranium decay in soil and rock, radon enters homes via foundation cracks and accumulates indoors, influenced by building characteristics, ventilation, urbanization, and geogenic factors. As part of the Zagreb pilot within the “Evidence Driven Indoor Air Quality Improvement” (EDIAQI) project, this is the first ML application for indoor radon analysis in Croatia. This research evaluates residential indoor radon concentrations in Zagreb using ML applied to a dataset of 80 households. Several linear regression and tree-based ensemble methods were tested. The best-performing model (GBR) achieved an R² of 0.99 on the training set and 0.57 on the test set, with an RMSE of 33 Bq/m³ and MAE of 26 Bq/m³. Although predictive performance was moderate and generalization limited, key building characteristics such as construction year, dwelling type, occupancy details, and floor level were identified as relevant variables. The results suggest that machine learning may support radon risk prioritization in urban environments, but cannot replace direct measurements for regulatory purposes. Full article

Dynamic phase transition of natural gas hydrates confined within complex pore–throat structures is a key factor impacting the safe and efficient development of hydrate-bearing deposits. In this work, hydrate-bearing samples with varying saturation were first reconstructed with the proposed ice-seeding method using actual marine soil in hydrate-bearing sediments from the South China Sea. Dynamic evolution characteristics of hydrate formation in evolving porous media under different temperature and pressure conditions were analyzed in detail. Combined with high-resolution CT scanning, image processing, pore network extraction, and statistical analysis, the typical microscopic pore–throat structures of hydrate-bearing sediments were revealed, and the presence of nanopores was identified. Furthermore, highly controllable heterogeneous pore–throat structures were constructed for microfluidic chips by integrating stochastic modeling, equivalent modeling, and machine learning approaches. On this basis, a novel microfluidic testing method was developed for investigating the dynamic formation, dissociation, and phase transition characteristics of natural gas hydrates in complex pore structures by controlling the temperature. This study provides reliable data support and theoretical guidance for the productivity prediction of marine hydrate-bearing deposits. Full article

Earth-based materials are increasingly considered as low-carbon alternatives for sustainable building construction. However, the high variability of natural soils and the complex behaviour of their clay fraction remain major barriers to the standardisation of characterisation and formulation methods. This study proposes a methodological and experimental framework based on the fine fraction (<400 µm) of soils to predict the fresh-state and hardened-state performance of earthen construction materials. Two natural soils from southwestern France with contrasted mineralogical compositions were investigated using rheological studies, compaction, linear shrinkage, and unconfined compressive strength (UCS) tests. The results show that the fine fraction plays a dominant role in governing material behaviour: smectite-rich soils reach higher dry densities (up to ≈2.10 g·cm⁻³) and compressive strengths (up to ≈6 MPa) but exhibit greater shrinkage sensitivity, whereas kaolinite–illite-rich soils display reduced shrinkage and improved dimensional stability. By demonstrating the predictive capacity of fine-fraction-based indicators for mechanical performance and dimensional stability, this work contributes to the development of simplified, reproducible, and environmentally relevant methodologies for the design of low-carbon earthen building materials using locally sourced soils. Full article