Search Results (13,914)

The crosslinked binders formed by using glycidyl azide polymer (GAP) as the binder matrix and bis-propargyl succinate (BPS) as the curing agent have good application prospects in the field of solid propellants. Aiming at the shortcomings of traditional experimental research, such as high cost, and molecular dynamics (MD) simulation, which are time-consuming for complex combination problems, this study will realize accurate prediction of the mechanical properties of binders through machine learning (ML) based on the molecular simulation dataset. Firstly, 273 sets of GAP-BPS binder models under different conditions were formed based on 21 crosslinking degrees and 13 temperatures, and MD simulation and mechanical property simulation were carried out. Then, the initial conditions of molecular simulation (crosslinking degree, temperature) and structural parameters (free volume) were taken as features, and the bulk modulus and shear modulus were taken as labels to form the dataset. Three machine learning models were trained and evaluated based on this dataset to test their prediction performance. Based on the cross-validation results, the Tabular Prior Data Fitting Network (TabPFN) exhibits the highest average prediction values (the average R² for bulk modulus and shear modulus were 0.9684 and 0.8827, respectively). But the significance analysis reveals that TabPFN significantly outperforms the RF model only in predicting bulk modulus. In subsequent prediction tasks with smaller datasets, TabPFN achieves superior average prediction values compared with RF and XGBoost. Full article

With the increasing application of the Vertical Shaft Machine (VSM) method in ultra-deep shafts, accurate prediction of construction-induced structural stresses is vital for engineering safety. Currently, VSM is predominantly used in soft soils, where structural response analysis still relies on finite element (FE) simulations that are computationally intensive and complex to model. To improve analysis efficiency and understand the structural behavior of VSM shafts in granite composite strata, this study takes the first VSM shaft project in South China—the Guangzhou–Huadu Intercity Railway Shield Shaft—as a case study. A “monitoring-driven, large-sample data, machine learning substitution” framework is proposed for predicting structural stresses during construction. The framework calibrates an FE model using monitoring data. Through full factorial design, key design parameters—including main reinforcement diameter, stirrup diameter, concrete strength grade, and steel plate thickness—are systematically varied. Parametric FE simulations are then conducted to construct large-sample response databases (540 sets for ring 0 and 864 sets for the cutting edge ring). Genetic algorithm is introduced to optimize the hyperparameters of Random Forest, XGBoost, and Neural Network models, and their predictive performances are systematically compared. Results show that the proposed framework effectively substitutes traditional FE analysis and enables rapid multi-parameter comparison. Among the models, GA-XGBoost achieves the highest prediction accuracy across all stress indicators (R² > 0.999, where R² is the coefficient of determination, with values closer to 1 indicating better predictive performance), demonstrating the superiority of its gradient boosting and regularization mechanisms in handling tabular data with strong physical correlations. Moreover, the method exhibits good extensibility to other engineering response predictions beyond construction stresses. Full article

Carbonate mound–shoal complexes, despite their complex pore structures and pronounced heterogeneity, represent one of the most productive reservoir units within carbonate formations. Accurately predicting key physical properties—such as porosity, permeability, and flow zone index—from well log data remains a significant challenge for conventional empirical methods. This study investigates the application of machine learning algorithms for optimizing the prediction of reservoir properties in hill-and-plain carbonate bodies. Six machine learning approaches—Support Vector Machines (SVM), Backpropagation Neural Networks (BPNN), Long Short-Term Memory Networks (LSTM), K-Nearest Neighbors (KNN), Random Forests (RF), and Gaussian Process Regression (GPR)—are systematically evaluated and compared. The analysis employed flow zone indices, geological data, and well log curves to classify porosity–permeability types. Seven logging parameters were used as input features: spectral gamma ray (SGR), uranium-free gamma ray (CGR), photoelectric absorption cross-section index (PE), bulk density (RHOB), acoustic travel time (DT), neutron porosity (NPHI), and true resistivity (RT). These features were paired with measured physical property values to train and validate the predictive models. Results demonstrate distinct algorithmic advantages for specific properties. The RF model achieved superior performance in permeability prediction, yielding an R² of 0.6824, whereas the GPR model provided the highest accuracy for porosity estimation, with an R² of 0.7342 and an Accuracy Index (ACI) of 0.9699. Despite these improvements, machine learning models still face limitations in accurately characterizing low-permeability zones within highly heterogeneous hill–terrace reservoirs. To address this challenge, the study integrates geological prior knowledge into the machine learning framework and applies cross-validation techniques to optimize model parameters, thereby providing a practical and robust approach for detailed assessment of mound–hoal carbonate reservoirs. Full article

With the growing demand for hybrid and electric vehicles, the accurate prediction of NVH (Noise, Vibration, and Harshness) behavior in Permanent Magnet Synchronous Machines (PMSMs) has become a critical aspect of electric motor design. This paper presents a detailed modeling approach for electromagnetic-induced noise and vibrations in PMSMs, integrating both analytical and numerical methods. The model focuses on quantifying the contributions of radial and tangential electromagnetic forces, which are key drivers of vibro-acoustic responses. The analytical part employs curved beam theory and a simplified acoustic model, offering rapid insights during early design stages. In parallel, a detailed numerical model based on finite element analysis is developed using a physics-based approach that accounts for the actual geometry and material properties of the PMSM prototype. This allows for enhanced accuracy without relying on experimental material parameter identification. Moreover, the detailed model includes the fluid–structure interaction introduced by the channels of the cooling fluid of the electric machine, which, although poorly addressed by the existing literature, was found to play a key role in driving the vibrational behaviour of the structure. By combining analytical speed with numerical precision, the proposed approach enables consistent and physically-based NVH predictions across various design phases, ultimately supporting improved electric machine performance and reducing development time and costs. Validation against experimental data confirms the ability of the model to accurately predict both sound pressure levels and housing surface vibrations. The novelty of this work lies in its integration of fluid–structure interaction and material modeling without the need for empirical parameter tuning, offering a robust tool for NVH design in electric vehicle applications. Full article

Air pollution caused by fine particulate matter (PM_2.5) poses a serious public health threat in many South Asian megacities where monitoring networks remain limited. Lahore, Pakistan—frequently ranked among the world’s most polluted cities—still lacks reliable short-term PM_2.5 forecasting systems. This study develops a performance-weighted ensemble machine learning framework that integrates satellite observations, meteorological reanalysis data, and ground monitoring measurements to improve daily PM_2.5 prediction. Eleven predictor variables were processed using a unified Google Earth Engine pipeline, including MODIS aerosol optical depth, Sentinel-5P trace gases (CO, NO₂, SO₂), and ERA5 meteorological parameters. Four tree-based machine learning algorithms—Random Forest, XGBoost, LightGBM, and CatBoost—were trained using daily observations from 2019 to 2023. Model evaluation using an independent 2024 dataset showed strong predictive capability, with Random Forest achieving R² = 0.77 (RMSE = 24.75 µg m⁻³), XGBoost R² = 0.76 (RMSE = 26.32 µg m⁻³), CatBoost R² = 0.73 (RMSE = 30.39 µg m⁻³), and LightGBM R² = 0.70 (RMSE = 32.75 µg m⁻³). To further enhance performance, the best models were combined into a weighted ensemble (RF 0.5, XGBoost 0.3, and CatBoost 0.2), which produced the highest validation accuracy (R² = 0.77; RMSE = 23.37 µg m⁻³). Statistical testing using paired t-tests and Diebold–Mariano tests confirmed that the ensemble significantly reduced forecast errors compared with individual models. Feature importance analysis revealed that surface pressure, temperature, CO, and NO₂ were the most influential predictors of PM_2.5 variability. The proposed framework demonstrates that combining satellite data, reanalysis meteorology, and ground observations through ensemble learning can provide accurate and scalable air quality forecasting for data-limited urban environments. Full article

Carbon fiber reinforced polymer (CFRP) composites are widely used in many engineering applications such as aerospace, automotive, and defense industries due to their superior properties such as high specific strength, stiffness, and corrosion resistance. However, these materials require drilling, especially during assembly processes. Damage mechanisms arising during this process, such as delamination, high thrust force, and torque, negatively affect structural integrity and production quality. This study proposes a data-driven, multi-objective optimization approach to solve problems encountered during drilling in multi-walled carbon nanotube (MWCNT)-reinforced CFRP nanocomposites. The study considers the MWCNT reinforcement ratio, cutting speed, and feed rate as process parameters and examines their effects on thrust force, torque, and delamination factor. Second-degree polynomial regression-based prediction models were created using the experimental data obtained, and these models were included in the multi-objective optimization process. During the optimization phase, thrust force and torque values were simultaneously minimized, while the delamination factor was kept below the statistically determined constraint of F_d ≤ 1.054. Pareto-optimal solution sets were obtained using NSGA-II and MOPSO meta-heuristic algorithms in the solution process. The results indicate that suitable combinations of drilling parameters can be identified through Pareto-based optimization, allowing significant reductions in thrust force and torque while maintaining the delamination factor below the specified limit. The study presents a reliable optimization approach for the more efficient machining of CFRP nanocomposites. Full article

Diameter at Breast Height (DBH) is a key parameter in forest measurement. However, existing research has mostly focused on improving the accuracy of individual technologies, lacking a systematic synthesis of the evolutionary logic of measurement techniques and a standardized selection framework for forestry applications. To this end, this paper constructs a multi-level classification framework based on measurement platforms and technical principles, establishes for the first time a five-dimensional comprehensive evaluation system (covering accuracy, efficiency, cost, environmental adaptability, and automation) along with a hierarchical technology decision tree, and systematically analyzes the application logic of multi-source fusion technologies across three levels: ground-based, near-ground mobile, and aerial. The review indicates that traditional contact-based measurement has limited efficiency; modern remote sensing technologies (photogrammetry and LiDAR) offer significant advantages in automation and accuracy, but still face challenges such as high equipment costs, complex data processing, and poor environmental adaptability. Multi-source fusion and machine learning are key methods to overcome the limitations of single sensors and improve the robustness of DBH estimation. Finally, it is anticipated that with decreasing sensor costs and the advancement of intelligent algorithms, DBH measurement will continue to evolve toward automation, intelligence, and engineering practicality, providing technical support for large-scale, long-term, and repeatable forest monitoring. Full article

Characterizing multiscale fracture networks in shale gas reservoirs remains challenging, while the limited applicability of conventional continuum-based models and insufficient multi-objective coordination often lead to low efficiency in development optimization. To address these issues, this study proposes a production history matching and multi-objective collaborative optimization framework for shale gas horizontal wells based on an equivalent fractal fracture (EFF) model. By integrating fractal theory with intelligent optimization techniques, a multiscale equivalent fractal permeability tensor is constructed, forming a hybrid machine-learning framework that combines physics-based fractal constraints with data-driven learning for efficient representation of complex fracture networks. Microseismic event clouds were converted into continuous fracture-density and fractal-geometry descriptors through denoising, temporal alignment, and spatial interpolation, and these descriptors were mapped to the equivalent fractal fracture model to dynamically update key flow parameters for history matching and parameter inversion. On this basis, a multi-objective collaborative optimization strategy is developed to achieve simultaneous time-varying fracture characterization and dynamic regulation of development parameters. Comparative results indicate that the EFF-based approach yields a production prediction error of 6.8%, slightly higher than the 4.2% obtained using discrete fracture network (DFN) models, while requiring only one-eighteenth of the computational time. Using the net present value (NPV) as the unified objective function, constraints are imposed on bottom-hole flowing pressure, flowback rate and system switching time for optimization. With the optimized pressure drop being more uniform and the gas saturation distribution being more balanced, it is verified that “EFF + NPV” can achieve the coordinated optimization of “production capacity—decline—cost” and enhance the development efficiency. Full article

Knee joint moment is an important biomechanical parameter for sports assessment, rehabilitation monitoring, and human–machine interaction. However, direct measurement is often restricted to laboratory-based settings. Surface electromyography (sEMG) offers a non-invasive alternative for indirect joint moment estimation, but many existing deep learning models remain too computationally demanding for potential wearable edge deployment. To address this gap, this study proposes Topo2DCNN-LSTM, a lightweight two-dimensional (2D) convolutional neural network model, designed for sagittal-plane knee flexion moment estimation. The model used a feature-based sequential representation, transforming raw sEMG signals into compact Root Mean Square (RMS) feature frames. The input was processed by a lightweight 2D convolutional neural network (CNN) encoder and paired with long short-term memory (LSTM) units. The model was trained on a public walking dataset of healthy subjects with synchronized sEMG and joint kinetics at two treadmill speeds. When compared with selected deep learning baselines, the quantized model achieved a mean RMS Error of 0.088 ± 0.020 Nm/kg at 1.2 m/s and 0.114 ± 0.034 Nm/kg at 1.8 m/s. On a SparkFun Thing Plus–SAMD51, it achieved an average inference latency of 28 ms using 71,316 bytes of random-access memory (RAM) and 257,172 bytes of flash. These results support its use as a proof of concept for personalized unilateral knee moment estimation with isolated on-device inference feasibility under resource-constrained and limited walking conditions. Full article

Accurate prediction of corrosion-fatigue crack growth rate in aluminum alloys is critical for the safety assessment of aerospace structures. Conventional empirical fracture-mechanic models often struggle to capture multiphysics coupling effects, whereas purely data-driven machine-learning models may lack physical interpretability and generalize poorly beyond the training distribution. To address this challenge, this study proposes a physics-guided knowledge-based XGBoost (KBXGB) model. Based on a comprehensive dataset comprising 2786 experimental records, Permutation Feature Importance was utilized to identify 11 key features, including the stress intensity factor range, stress ratio, frequency, and environmental parameters. The KBXGB framework learns the residual between physics-based empirical models (e.g., the Paris and Walker laws) and measured experimental data, recasting the complex nonlinear mapping into a correction of the systematic deviations of the physical models, thereby achieving deep integration of domain knowledge and data-driven learning. Test results demonstrate that the KBXGB model achieves a coefficient of determination (R²) of 0.9545 and a reduced Mean Relative Error (MRE) of 1.61% on the test set, outperforming standard XGBoost and traditional regression models. Crucially, in independent extrapolation validation, the standard XGBoost model failed (R² = 0.2858) with non-physical staircase artifacts, whereas the KBXGB model maintained high predictive fidelity (R² = 0.8646) and successfully reproduced physical crack growth trends. The proposed approach effectively mitigates the “black-box” limitations of machine learning in sparse data regions, offering a high-precision and physically robust tool for corrosion fatigue-life prediction under complex service conditions. Full article

In semi-arid regions, sustainable groundwater management for irrigation is critical for agricultural productivity and food security. This study presents an integrated methodological framework combining hydrochemical characterization, machine learning (ML) modeling, and explainable artificial intelligence (XAI) to predict the Irrigation Water Quality Index (IWQI) in the Ain Oussera plain, Djelfa Province, Algeria. A total of 191 groundwater samples were collected from November 2023 to September 2024 and analyzed for major ions and physicochemical parameters. Multiple irrigation suitability indices were calculated, including Sodium Adsorption Ratio (SAR), Sodium Percentage (Na%), Magnesium Hazard (MH), Permeability Index (PI), Residual Sodium Carbonate (RSC), Soluble Sodium Percentage (SSP), and Kelly’s Ratio (KR). Five ML models were developed and evaluated for IWQI prediction: Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors, and Support Vector Regression. Results showed that 55% of groundwater samples exhibited low to no restrictions for irrigation use, while 19% required high to severe restrictions. The XGBoost model demonstrated superior performance, with the highest R² (0.95) and the lowest RMSE (3.22) among all tested algorithms. SHAP (SHapley Additive exPlanations) analysis provided a transparent interpretation of model predictions, identifying electrical conductivity and Sodium Adsorption Ratio as the most influential parameters affecting IWQI, while chloride, sodium, total hardness, and magnesium had minimal impact. Spatial mapping using Inverse Distance Weighting (IDW) interpolation in ArcGIS 10.8 revealed considerable spatial variability in water quality throughout s the plain. This research addresses a critical gap in North African groundwater management by integrating ML predictive capabilities with XAI transparency, providing water resource managers and agricultural stakeholders with interpretable, data-driven tools for sustainable irrigation planning in water-stressed semi-arid environments. Full article

Background: Surface roughness in turning operations is still verified predominantly after machining, which limits the possibility of timely corrective intervention. Methods: This study examined whether normal-direction peak-to-peak vibration displacement can serve as a practical low-frequency indicator of surface roughness during finish turning of EN AW-2011 aluminum alloy. The analysis was based on 190 synchronized displacement-roughness observation pairs obtained in one controlled experimental campaign on a CQ6230 conventional precision lathe, using a VB-8206SD displacement logger mounted radially on the tool holder and contact profilometry measurements reported as Ra and Rz. The analytical workflow included explicit quality-control safeguards for malformed rows, missing values, and obvious artefacts; in the present dataset, these checks did not indicate a failure state that would invalidate the main calculations. The workflow combined descriptive statistics, moving-average trend inspection, low-frequency FFT and STFT descriptors, Pearson correlation analysis, and ordinary least squares regression. Results: The displacement signal exhibited a mean value of 0.0446 mm with a standard deviation of 0.0256 mm and showed strong within-dataset linear relations with roughness parameters: Ra = 14.204 + 24.191 V (R² = 0.9929, RMSE = 0.052 µm) and Rz = 63.207 + 105.253 V (R² = 0.9905, RMSE = 0.264 µm). Conclusions: The results support setup-specific roughness-related process-state assessment using low-rate normal-direction displacement measurements. However, because the 190 records represent a time-ordered synchronized sequence rather than 190 independent cutting trials, and because no separate validation set was available, the fitted equations should be interpreted as descriptive within-setup calibration rather than as universally validated predictive models. Full article

Early-age shrinkage is a critical factor governing the dimensional stability and cracking susceptibility of ultra-high-performance concrete (UHPC). However, accurate prediction of UHPC shrinkage remains challenging due to the strong nonlinear interactions among mixture parameters, curing conditions, and hydration-induced internal moisture evolution, particularly when only limited experimental data are available. In this study, a systematic experimental program was conducted to investigate the influence of the binder-to-sand ratio, water-to-binder ratio, polypropylene fiber dosage, and curing environment on both early drying shrinkage and autogenous shrinkage of UHPC. Based on the experimental results, a structured dataset covering all shrinkage test data was constructed to support data-driven modeling. To improve prediction reliability under small-sample conditions, a Bayesian-optimized Extreme Gradient Boosting (BO-XGBoost) framework was developed and benchmarked against several conventional machine learning models, including Backpropagation Neural Networks (BPNNs), Random Forest (RF), and Support Vector Machines (SVMs). Shrinkage test data from other literature validated the prediction accuracy of this model, demonstrating its rationality and practicality. In addition, the Shapley Additive Explanations (SHAP) method was employed to quantitatively interpret the contribution and interaction mechanisms of key variables affecting shrinkage behavior. The results show that the BO-XGBoost model achieves the highest prediction accuracy and stability among the evaluated algorithms. SHAP analysis further reveals that curing age and curing environment dominate drying shrinkage, whereas autogenous shrinkage is primarily governed by the curing age and water-to-binder ratio. The interaction analysis also identifies the coupled effects between low water-to-binder ratio and extended curing age. The proposed framework not only improves prediction robustness for UHPC shrinkage under limited data conditions but also provides interpretable insights into the mechanisms governing early-age deformation. These findings offer a data-driven basis for optimizing UHPC mixture design and mitigating early-age cracking risks in engineering applications. Full article

Entropic soft-min operators are widely used to obtain smooth approximations of minimum and argmin mechanisms in optimization, machine learning, and reinforcement learning. The quality of this approximation is controlled by an inverse temperature parameter that governs the trade-off between smoothness and fidelity, yet its selection is usually based on global heuristics or worst-case bounds that do not account for the geometry of the candidate cost vector. This study investigates the calibration of the inverse temperature parameter from a geometry-aware perspective, with explicit guarantees on the approximation error between the entropic soft-min and the exact minimum value. After establishing the structural properties of the relaxation error, including monotonicity with respect to the inverse temperature and its dependence on the geometry of the near-optimal set, we introduce a conformal calibration rule that selects the smallest inverse temperature, ensuring that a prescribed upper quantile of the approximation error remains below a target tolerance with distribution-free finite-sample validity. The resulting selector adapts to the geometry distribution represented in the calibration population and provides a principled alternative to mean-based and worst-case tuning rules. Numerical experiments, including geometry-controlled benchmarks and a contextual bandit setting illustrating the impact of geometry-aware calibration on decision-making under estimated action values, show that the proposed method accurately tracks oracle calibration temperatures, preserves the desired operator-level coverage, and makes explicit how geometric heterogeneity governs the effective sharpness required by the soft-min approximation. Additional shifted evaluations illustrate the role of exchangeability in the validity guarantee and the consequences of transferring temperatures across populations with different near-optimal geometries. Full article

With the development of scientific machine learning (SciML), the proposal of physics-informed neural networks (PINNs) has provided a powerful paradigm for solving partial differential equations (PDEs). While PINNs perform well in solving high-dimensional PDEs, they perform worse than traditional numerical methods for low-dimensional problems. This discrepancy arose from potential convergence conflicts induced by distinct physical magnitude of loss terms. To decouple the convergence conflicts, we propose a partial derivative guided multi-branch physics-informed neural network (PDGM-PINN). Inspired by SciML, we treat both the solution and partial derivatives as dependent variables to be predicted. The partial derivatives are directly predicted by sub-branches, while the main branch approximates the PDE solution, and all branches share error backpropagation information. Furthermore, we redesign the loss function. The loss of the governing equation is computed with the solution and partial derivatives predicted by the main and sub-branches. Schwarz’s theorem and Kullback–Leibler divergence are incorporated into the loss terms as soft constraints of partial derivatives continuity and residual distributions consistency for the governing equations. We conducted comprehensive experimental evaluations on seven PDEs, and ablation experiments, sensitivity analyses, and complexity analyses were carried out to investigate the rationality of PDGM-PINN. The results demonstrate that PDGM-PINN achieves the best performance among PINN variants with the fewest trainable parameters, effectively avoiding architectural redundancy. Full article