Search Results (63)

Enhancing interlayer bond strength remains a critical challenge in the extrusion-based 3D printing of cementitious materials. This study investigates the optimisation of interlayer bond strength in extrusion-based 3D-printed cementitious materials through a combined application of Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs). Using a concise yet comprehensive dataset, RSM provided interpretable main effects, curvature, and interactions, while the ANN captured non-linearities beyond quadratic forms. Comparative analysis revealed that the RSM model achieved higher predictive accuracy ($R^2=0.95$) compared to the ANN model ($R^2=0.87$). Desirability-based optimisation confirmed the critical importance of minimising casting delays to mitigate interlayer weaknesses, with RSM suggesting a water-to-cement (W/C) ratio of approximately 0.45 and a minimal time gap of less than 5 min, while ANN predicted slightly lower optimal W/C values but with reduced reliability due to the limited dataset. Sensitivity analysis using partial dependence plots (PDPs) further highlighted that ordinary Portland cement (OPC) content and W/C ratio are the dominant factors, contributing approximately 2.0 and 1.8 MPa respectively to the variation in predicted bond strength, followed by superplasticiser dosage and silica content. Variables such as water content, viscosity-modifying agent, and time gap exhibited moderate influence, while sand and fibre content had marginal effects within the tested ranges. These results demonstrate that RSM provides robust predictive performance and interpretable optimisation guidance, while ANN offers flexible non-linear modelling but requires larger datasets to achieve stable generalisation. Integrating both methods offers a complementary pathway to advance mix design and process control strategies in 3D concrete printing. Full article

One-part geopolymers (OPG) offer a low-carbon alternative to Portland cement, yet mix design remains largely empirical. This study couples machine learning with SHAP (Shapley Additive Explanations) to quantify how mix and curing factors govern performance in Ca-containing OPG. We trained six regressors—Random Forest, ExtraTrees, SVR, Ridge, KNN, and XGBoost—on a compiled dataset and selected XGBoost as the primary model based on prediction accuracy. Models were built separately for four targets: compressive strength at 3, 7, 14, and 28 days. SHAP analysis reveals four dominant variables across targets—Slag, Na₂O, Ms, and the water-to-binder ratio (w/b)—while the sand-to-binder ratio (s/b), temperature, and humidity are secondary within the tested ranges. Strength evolution follows a reaction–densification logic: at 3 days, Slag dominates as Ca accelerates C–(N)–A–S–H formation; at 7–14 days, Na₂O leads as alkalinity/soluble silicate controls dissolution–gelation; by 28 days, Slag and Na₂O jointly set the strength ceiling, with w/b continuously regulating porosity. Interactions are strongest for Slag × Na₂O (Ca–alkalinity synergy). These results provide actionable guidance: prioritize Slag and Na₂O while controlling w/b for strength. The XGBoost+SHAP workflow offers transparent, data-driven decision support for OPG mix optimization and can be extended with broader datasets and formal validation to enhance generalization. Full article

In order to tackle the issues of poor mobility and unstable traction of electric vehicles on sandy landscapes, this research develops a high-accuracy numerical model for wheel–sand interaction relying on the Discrete Element Method (DEM). An innovative parameter calibration procedure is proposed herein, which optimizes the sand contact parameters. This reduces the error between the simulated and measured angles of repose to merely 1.2% and substantially improves the model’s reliability. The model was then used to systematically compare the performance of a 205/55 R16 slick tire with a treaded tire on sand. Simulations demonstrate that at a 30% slip ratio, the treaded tire exhibited significantly higher traction and greater sinkage than the slick tire. This indicates that tread patterns enhance traction mechanically by increasing the contact area and promoting shear deformation of the sand. The trends of traction with slip ratio and the corresponding sand flow patterns showed excellent agreement with experimental observations, which validated the simulation approach. This research provides an efficient and accurate tool for evaluating tire-sand interaction, providing critical support for the design and control of electric vehicles on complex terrains. Full article

In field exploration, disaster rescue, and complex terrain operations, the accuracy of ground type recognition directly affects the walking stability and task execution efficiency of legged robots. To address the problem of terrain recognition in complex ground environments, this paper proposes a high-precision classification method based on single-leg triaxial force signals. The method first employs a one-dimensional convolutional neural network (1D-CNN) module to extract local temporal features, then introduces a long short-term memory (LSTM) network to model long-term and short-term dependencies during ground contact, and incorporates a convolutional block attention module (CBAM) to adaptively enhance the feature responses of critical channels and time steps, thereby improving discriminative capability. In addition, an improved whale optimization algorithm (iBWOA) is adopted to automatically perform global search and optimization of key hyperparameters, including the number of convolution kernels, the number of LSTM units, and the dropout rate, to achieve the optimal training configuration. Experimental results demonstrate that the proposed method achieves excellent classification performance on five typical ground types—grass, cement, gravel, soil, and sand—under varying slope and force conditions, with an overall classification accuracy of 96.94%. Notably, it maintains high recognition accuracy even between ground types with similar contact mechanical properties, such as soil vs. grass and gravel vs. sand. This study provides a reliable perception foundation and technical support for terrain-adaptive control and motion strategy optimization of legged robots in real-world environments. Full article

In response to the technical challenges of traditional reservoir inversion techniques in determining inter-well wavelets and estimating geological statistical parameters, this study proposes an artificial intelligence high-resolution reservoir inversion technique based on seismic waveform constraints. This technology integrates multi-source heterogeneous data such as lithology characteristics, logging curves, and seismic waveforms through a deep learning neural network framework, and constructs an intelligent reservoir prediction model with geological and physical constraints. Results demonstrate that the proposed technique significantly enhances prediction accuracy for thin sand layers by effectively extracting high-frequency seismic information and establishing robust nonlinear mapping relationships. Inversion errors of reservoir parameters were reduced by more than 25%, while a vertical resolution of 0.5 m was achieved. Predictions agreed with actual drilling data with an accuracy of 86%, representing an 18% improvement over traditional methods. In practical applications, the technique successfully supported new well placement, contributing to a 22% increase in initial oil production in the pilot area. Furthermore, this study establishes a standardized technical procedure: “Time–Depth Modeling-Phase-Controlled Interpolation-Intelligent Inversion”. This workflow provides an innovative solution for high-precision reservoir characterization in regions with limited well control and complex terrestrial depositional systems, offering both theoretical significance and practical value for advancing reservoir prediction technology. Full article

This paper discusses the principal sources of uncertainty in the execution and interpretation of Bender Element (BE) tests conducted on reconstituted sand samples. Based on the experience accumulated by the Geotechnical Laboratory of the University of Coimbra, the study addresses three critical stages of the testing process: sample preparation, test execution, and result interpretation. For each stage, the key challenges are identified, and potential solutions are proposed. Particular emphasis is placed on the control of relative density and sample saturation during preparation, as well as on factors affecting signal quality and time lag of the system during test execution. The interpretation of the results is analyzed with respect to the limitations of currently employed methods. The overall reliability of the procedures employed throughout the testing process is also assessed, with the results providing guidance for improving the accuracy and consistency of BE test outcomes. Full article

Snowdrift, as a natural disaster, constantly compromises railway traffic by affecting how snow accumulates on the subgrade. This paper establishes a unified set of similarity criteria for wind tunnel testing, using viscous silica sand to simulate snow particles. By employing a geometric scale model (1:30) and similarity criteria (size, motion, dynamics, accumulation patterns, and time scales), it systematically investigates the evolution patterns of wind-induced snow accumulation on two types of roadbed structures: embankments and excavations. This study also evaluates the effectiveness of snow fences, proposing optimized placement distances and quantifying the effects of snow accumulation platform width. The results showed the following: (1) Snow on embankments has a “U”-shaped distribution, with the lowest wind speed (<0.5 m/s) and maximum accumulation at the leeward slope’s foot. In excavations, snow forms an “M”-shaped distribution, with significantly reduced wind speeds (<1 m/s) on the accumulation platform. (2) Snow fences effectively manage snow placement by lowering wind speed (below 1 m/s). A single-row snow fence with a porosity of 50% and a height of 3 m performs best when placed at seven times its height (7 H) from the slope’s toe. (3) A 5 m snow accumulation platform in excavations reduces surface snow accumulation (distribution coefficient drops to 1.6), outperforming scenarios without a platform (coefficient of 2.0). These findings contribute to the prevention and control of snowdrift disasters along railway lines in cold regions. They offer practical guidance for optimizing snow fence configurations, while also laying a foundation for future improvements in experimental accuracy through advanced techniques such as PIV and real-snow testing. Full article

The study block is located on the eastern edge of the Ordos Basin and is one of the typical medium coalbed methane blocks in China that have previously been subjected to exploration and development work. The rich CBM resource base and good exploration and development situation in this block mean there is an urgent need to accelerate development efforts, but compared with the current situation for tight sandstone gas where development is in full swing in the area, the production capacity construction of CBM wells in the area shows a phenomenon of lagging to a certain degree. In this study, taking the 4 + 5 coal seam of the YJP block in the Ordos Basin as the research object, we carried out technical research on an integrated program concerning CBM geology and engineering and put forward a comprehensive seismic geology analysis method for the prediction of the CBM content. The study quantitatively assessed the tectonic conditions, depositional environment, and coal seam thickness as potential controlling factors using gray relationship analysis, trend surface analysis, and seismic geological data integration. The results show that tectonic conditions, especially the burial depth, residual deformation, and fault development, are the main controlling factors affecting the coalbed methane content, showing a strong correlation (gray relational value greater than 0.75). The effects of the depositional environment (sand–shale ratio) and coal bed thickness were negligible. A weighted fusion model incorporating seismic attributes and geological parameters was developed to predict the gas content distribution, achieving relative prediction errors of below 15% in validation wells, significantly outperforming traditional interpolation methods. The integrated approach demonstrated enhanced spatial resolution and accuracy in delineating the lateral CBM distribution, particularly in structurally complex zones. However, limitations persist due to the seismic data resolution and logging data reliability. This method provides a robust framework for CBM exploration in heterogeneous coal reservoirs, emphasizing the critical role of tectonic characterization in gas content prediction. Full article

While the development of sustainable construction materials, such as green concrete made from glass waste or recycled concrete aggregate, has been extensively researched, much of the existing work has focused narrowly on these two components. This limited scope highlights the need for further investigation to comprehensively address their drawbacks and expand the available knowledge base. Moreover, the current study uniquely emphasizes the shear response of green concrete, a critical aspect that has not been previously explored. Push-off shear samples made of green concrete, a mixture of recycled concrete, and glass waste, were built and subjected to direct shear loading testing to investigate shear response. In different proportions (0, 10, 25, 50, and 100%), fine glass aggregate is used in place of river sand. At different ratios (0, 10, 20, and 40%), coarse glass aggregate was substituted for coarse natural aggregate to form four mixtures. Additionally, recycled concrete and coarse glass aggregates were utilized instead of coarse natural aggregates. In the last group, coarse natural aggregate was substituted with recycled concrete aggregates in different proportions (0, 16, 40, and 80%). Measurements were made of the applied shear force and the sliding of the shear transfer plane during the test. The tested mixtures’ failure, shear strength, shear slip, shear stiffness, and shear stress slip correlations were examined. According to the results, all of the samples failed in the shear transfer plane. The shear strength of mixes containing 10, 25, 50, and 100% fine glass was, respectively, 12.8%, 14.7%, 29.5%, and 39% lower than the control combination without fine glass. As the amount of recycled glass and concrete materials grew, so did the shear slip at the shear transfer plane. In recent years, numerous studies have proposed formulas to predict the push-off shear strength of plain concrete, primarily using compressive strength as the key parameter—often without accounting for the influence of infill materials. The present study introduces an improved predictive model that incorporates the contents of recycled concrete aggregate, coarse glass aggregate, or fine glass aggregate as correction factors to enhance accuracy. Full article

The surface settlement of railroad tunnels is dynamically updated as the construction progresses, exhibiting complex nonlinear characteristics. The accuracy of the on-site nonlinear regression fitting prediction method needs to be improved. To prevent surface settlement and surrounding rock collapse during railroad tunnel construction, while also ensuring the safety of the tunnel and existing structures, we propose a recursive prediction model for the long-term trend of surface settlement utilizing a singular spectrum analysis (SSA), improved sand cat swarm optimization (ISCSO), and a kernel extreme learning machine (KELM). First, SSA decomposition, known for its adaptive decomposition of one-dimensional nonlinear time series, reorganizes the early surface settlement data. The dynamic sliding window method is introduced to construct the prediction dataset, which is then trained using the KELM. ISCSO is used to optimize the key parameters of the KELM to obtain the long-term trend curves of surface settlement through recursive time series prediction. The superiority and effectiveness of ISCSO and the model are verified through numerical experiments and simulation experiments based on engineering cases, providing a reference for the early warning and control of surface settlement during the construction of similar tunnels. Full article

Measuring surface soil moisture is vital for understanding water availability, agricultural productivity, and climate change impacts, as well as for drought prediction and water resource management. However, obtaining accurate data is challenging due to the lack of reliable probes that work across diverse soil types and conditions. This study evaluated a prototype dielectric probe developed by Daiki Rika Kogyo Co., Ltd., Saitama, Japan, through controlled laboratory experiments. The probe measures the real and imaginary parts of dielectric permittivity over 10–150 MHz in a 5.6 cm diameter, with a 2 cm length volume, achieving a ±2% accuracy for the real part of oil–ethanol and ethanol–water mixtures (3.26–79). The imaginary part of the dielectric permittivity of aqueous solutions is convertible into electrical conductivity (EC) with reasonable accuracy. For variably saturated sand, the real part is convertible to a volumetric soil-moisture content (≥0.10 m³m⁻³) using a custom equation. The probe’s variable-frequency measurements reduce the limitations of fixed-frequency approaches, accounting for the EC, clay, porosity, and organic matter effects. With its VNA principle and simultaneous measurement of dielectric properties, it offers innovative capabilities for addressing water management, agriculture, and climate prediction challenges. Full article

This study explores the potential of repurposing historical aerial photographs to produce high-accuracy digital surface models (DSMs) at regional scales. A novel methodology is introduced, incorporating road points for quality control and refinement to enhance the precision and comparability of multitemporal DSMs. The method consists of two phases. The first is the photogrammetric phase, where DSMs are generated using photogrammetric and structure from motion (SfM) techniques. The second is the refinement phase, which uses a large number (millions) of points extracted from road centrelines to evaluate altimetric residuals—defined as the differences between photogrammetric DSMs and a reference DSM. These points are filtered to ensure that they represent stable positions. The analysis shows that the initial residuals exhibit geographical trends, rather than random behaviour, that are removed after the refinement. An application example covering the whole coast of the Valencian region (Eastern Spain, 518 km of coastline) shows the obtention of a series composed of six DSMs. The method achieves levels of accuracy (0.15–0.20 m) comparable to modern LiDAR techniques, offering a cost-effective alternative for three-dimensional characterisation. The application to the foredune and coastal environment demonstrated the method’s effectiveness in quantifying sand volumetric changes through comparison with a reference DSM. The achieved accuracy is crucial for establishing precise sedimentary balances, essential for coastal management. At the same time, this method shows significant potential for its application in other dynamic landscapes, as well as urban or agricultural monitoring. Full article

The weathered bedrock aquifer in the Jurassic coalfield of northern Shaanxi Province is a direct water-bearing aquifer, and accurately predicting its water-bearing properties is essential for preventing and controlling water hazards in mining operations. Traditional Bayes discriminant methods have limitations in predicting water-bearing properties, particularly because not all primary factors influencing water-bearing properties meet the criteria for multivariate normal distribution. In this paper, the southern flank of the Ningtiaota Minefield is taken as an example, with the weathered bedrock aquifer as the research object. Six main controlling factors are selected: weathered bedrock thickness, core recovery rate, degree of weathering, lithological combination, elevation of the weathered bedrock surface, and sand-to-base ratio. A kernel density estimator–Bayes (KDE–Bayes) discriminant method for predicting water-bearing properties is presented. The kernel density estimation was carried out on the three main controlling factors that do not conform to a normal distribution—weathered bedrock thickness, core recovery rate, and sand-to-base ratio—and, in conjunction with other primary factors, a KDE–Bayes model was constructed for predicting the water-bearing properties in the southern flank of the Ningtiaota Minefield, based on which a detailed prediction of the water-bearing properties of the south flank of the Ningtiaota Minefield was conducted. By analyzing the actual dewatering data from the S1231 working face and past water inrush (or outburst) incidents, the feasibility and accuracy of this prediction method are demonstrated, providing valuable insights for predicting the water-bearing properties of weathered bedrock aquifers in the Ningtiaota Coal Mine and similar mining conditions. Full article

Efficient and reliable corn (Zea mays L.) yield prediction is important for varietal selection by plant breeders and management decision-making by growers. Unlike prior studies that focus mainly on county-level or controlled laboratory-scale areas, this study targets a production-scale area, better representing real-world agricultural conditions and offering more practical relevance for farmers. Therefore, the objective of our study was to determine the best combination of vegetation indices and abiotic factors for predicting corn yield in a rain-fed, production-scale area, identify the most suitable corn growth stage for yield estimation using machine learning, and identify the most effective machine learning model for corn yield estimation. Our study used high-resolution (6 cm) aerial multispectral imagery. Sixty-two different predictors, including soil properties (sand, silt, and clay percentages), slope, spectral bands (red, green, blue, red-edge, NIR), vegetation indices (GNDRE, NDRE, TGI), color-space indices, and wavelengths were derived from the multispectral data collected at the seven (V4, V5, V6, V7, V9, V12, and V14/VT) growth stages of corn. Four regression and machine learning algorithms were evaluated for yield prediction: linear regression, random forest, extreme gradient boosting, and gradient boosting regressor. A total of 6865 yield values were used for model training and 1716 for validation. Results show that, using random forest method, the V14/VT stage had the best yield predictions (RMSE of 0.52 Mg/ha for a mean yield of 10.19 Mg/ha), and yield estimation at V6 stage was still feasible. We concluded that integrating abiotic factors, such as slope and soil properties, significantly improved model accuracy. Among vegetation indices, TGI, HUE, and GNDRE performed better. Results from this study can help farmers or crop consultants plan ahead for future logistics through enhanced early-season yield predictions and support farm profitability and sustainability. Full article

The composition of oil sands tailings is a complex mixture of water, fine clay, sand, silt, and residual bitumen that remains after the extraction of bitumen. Effective tailings disposal management requires an understanding of the mechanisms controlling water movement, surface settlement rates and extents (hydraulic conductivity and compressibility), and strength variation with depth. This investigation examines the self-weight consolidation behavior of oil sands tailings, typically assessed by utilizing large strain consolidation (LSC) methods such as the multi-step large strain consolidation (MLSC) test and seepage-induced consolidation test (SICT). These methods, however, are time consuming and often take weeks or years to complete. As an alternative, centrifuge testing, including both geotechnical beam type and benchtop devices, was utilized to evaluate the consolidation behaviors of three untreated high water content oil sands tailing slurries: two high-plasticity fluid fine tailing (FFT) samples and one low plasticity FFT. The centrifuge-derived compressibility data closely matched the LSC testing compressibility data within the centrifuge stress range. However, the hydraulic conductivity obtained from centrifuge testing was up to an order of magnitude higher than the LSC test results. Comparing centrifuge and large strain modeling results indicates that centrifuge test data demonstrate average void ratios 10–33% lower than those predicted by simulations using LSC parameters, highlighting a notable deviation. To examine the scale effect on result accuracy, validation tests indicated that the benchtop centrifuge (BTC) yielded comparable results to the geotechnical beam centrifuge (GBC) for the same prototype, saving time, resources, and sample volumes in the assessment of tailings consolidation behavior. These tests concluded that the small radius of the benchtop centrifuge had a minimal impact on the results. Full article