Search Results (3,204)

Optimizing cable forces in cable-stayed bridges is challenging due to structural nonlinearity and the limitations of traditional methods, which often focus on isolated performance indicators. This study proposes an integrated framework combining Gaussian process regression (GPR) with an enhanced whale optimization algorithm improved by the Salp Swarm Algorithm (EWOSSA). GPR is first used to model the nonlinear relationship between cable forces and structural responses. The EWOSSA then efficiently optimizes the GPR-based model to identify optimal cable forces. A case study on a cable-stayed bridge with a 2 × 145 m main spans demonstrates the effectiveness of the proposed approach. Compared with conventional methods such as the internal-force equilibrium and zero-displacement methods, the EWOSSA-GPR framework achieves superior performance across multiple structural metrics. It ensures a more uniform cable force distribution, reduces girder displacements, and improves bending moment profiles, offering a comprehensive solution for optimal structural performance in cable-stayed bridges. Full article

Growing concern over anthropogenic underwater noise, highlighted by initiatives like the Marine Strategy Framework Directive (MSFD) and its Technical Group on Underwater Noise (TG Noise), emphasizes regions like the Western Black Sea, where increasing activities threaten marine habitats. This region is experiencing rapid growth in maritime traffic and resource exploitation, which is intensifying concerns over the noise impacts on its unique marine habitats. While machine learning offers promising solutions, a research gap persists in comprehensively evaluating diverse ML models within an integrated framework for complex underwater acoustic data, particularly concerning real-world data limitations like class imbalance. This paper addresses this by presenting a multi-faceted framework using passive acoustic monitoring (PAM) data from fixed locations (50–100 m depth). Acoustic data are processed using advanced signal processing (broadband Sound Pressure Level (SPL), Power Spectral Density (PSD)) for feature extraction (Mel-spectrograms for deep learning; PSD statistical moments for classical/unsupervised ML). The framework evaluates Convolutional Neural Networks (CNNs), Random Forest, and Support Vector Machines (SVMs) for noise event classification, alongside Gaussian Mixture Models (GMMs) for anomaly detection. Our results demonstrate that the CNN achieved the highest classification accuracy of 0.9359, significantly outperforming Random Forest (0.8494) and SVM (0.8397) on the test dataset. These findings emphasize the capability of deep learning in automatically extracting discriminative features, highlighting its potential for enhanced automated underwater acoustic monitoring. Full article

The fixed-time trajectory tracking control problem of the uncertain nonlinear Euler–Lagrange system is studied. To ensure the fast, high-precision trajectory tracking performance of this system, a non-singular terminal sliding-mode controller based on Gaussian process regression is proposed. The control algorithm proposed in this paper is applicable to periodic motion scenarios, such as spacecraft autonomous orbital rendezvous and repetitive motions of robotic manipulators. Gaussian process regression is employed to establish an offline data-driven model, which is utilized for compensating parametric uncertainties and external disturbances. The non-singular terminal sliding-mode control strategy is used to avoid singularity and ensure fast convergence of tracking errors. In addition, under the Lyapunov framework, the fixed-time convergence stability of the closed-loop system is rigorously demonstrated. The effectiveness of the proposed control scheme is verified through simulations on a spacecraft rendezvous mission and periodic joint trajectory tracking for a robotic manipulator. Full article

Wind farm performance monitoring has traditionally relied on deterministic models, such as power curves or machine learning approaches, which often fail to account for farm-wide behavior and the uncertainty quantification necessary for the reliable detection of underperformance. To overcome these limitations, we propose a probabilistic methodology for turbine-level active power prediction and uncertainty estimation using high-frequency SCADA data and farm-wide autoregressive information. The method leverages a Stochastic Variational Gaussian Process with a Linear Model of Coregionalization, incorporating physical models like manufacturer power curves as mean functions and enabling flexible modeling of active power and its associated variance. The approach was validated on a wind farm in the Belgian North Sea comprising over 40 turbines, using only 15 days of data for training. The results demonstrate that the proposed method improves predictive accuracy over the manufacturer’s power curve, achieving a reduction in error measurements of around 1%. Improvements of around 5% were seen in dominant wind directions (200°–300°) using 2 and 3 Latent GPs, with similar improvements observed on the test set. The model also successfully reconstructs wake effects, with Energy Ratio estimates closely matching SCADA-derived values, and provides meaningful uncertainty estimates and posterior turbine correlations. These results demonstrate that the methodology enables interpretable, data-efficient, and uncertainty-aware turbine-level power predictions, suitable for advanced wind farm monitoring and control applications, enabling a more sensitive underperformance detection. Full article

Slope stability analysis is conventionally performed using the strength reduction method with the proportional reduction in shear strength parameters. However, during actual slope failure processes, the attenuation characteristics of rock mass cohesion ($c$) and internal friction angle ($\phi$) are often inconsistent, and their reduction paths exhibit clear nonlinearity. Relying solely on proportional reduction paths to calculate safety factors may therefore lack scientific rigor and fail to reflect true slope behavior. To address this limitation, this study proposes a novel approach that considers the non-proportional reduction of $c$ and $\phi$, without dependence on predefined reduction paths. The method begins with an analysis of slope stability states based on energy dissipation theory. A Bayesian Gaussian Mixture Model (BGMM) is employed for intelligent interpretation of the dissipated energy data, and, combined with energy mutation theory, is used to identify instability states under various reduction parameter combinations. To compute the safety factor, the concept of a “reference slope” is introduced. This reference slope represents the state at which the slope reaches limit equilibrium under strength reduction. The safety factor is then defined as the ratio of the shear strength of the target analyzed slope to that of the reference slope, providing a physically meaningful and interpretable safety index. Compared with traditional proportional reduction methods, the proposed approach offers more accurate estimation of safety factors, demonstrates superior sensitivity in identifying critical slopes, and significantly improves the reliability and precision of slope stability assessments. These advantages contribute to enhanced safety management and risk control in slope engineering practice. Full article

This study presents a comprehensive methodology for the detection and characterization of fractures in geological samples using X-ray computed tomography (CT). By combining convolution-based image processing techniques with advanced neural network-based segmentation, the proposed approach achieves high precision in identifying complex fracture networks. The method was applied to a marly limestone sample from the Maiolica Formation, part of the Umbria–Marche stratigraphic succession (Northern Apennines, Italy), a geological context where fractures often vary in size and contrast and are frequently filled with minerals such as calcite or clays, making their detection challenging. A critical part of the work involved addressing multiple sources of uncertainty that can impact fracture identification and measurement. These included the inherent spatial resolution limit of the CT system (voxel size of 70.69 μm), low contrast between fractures and the surrounding matrix, artifacts introduced by the tomographic reconstruction process (specifically the Radon transform), and noise from both the imaging system and environmental factors. To mitigate these challenges, we employed a series of preprocessing steps such as Gaussian and median filtering to enhance image quality and reduce noise, scanning from multiple angles to improve data redundancy, and intensity normalization to compensate for shading artifacts. The neural network segmentation demonstrated superior capability in distinguishing fractures filled with various materials from the host rock, overcoming the limitations observed in traditional convolution-based methods. Overall, this integrated workflow significantly improves the reliability and accuracy of fracture quantification in CT data, providing a robust and reproducible framework for the analysis of discontinuities in heterogeneous and complex geological materials. Full article

Concrete resistivity is a critical parameter for assessing durability and monitoring the structural health of reinforced concrete. This study systematically evaluates the effects of the water-to-cement (w/c) ratio, saturation ratio (SR), and temperature on concrete resistivity using three different predictive models: linear regression, cubic Support Vector Machine (SVM), and Gaussian Process Regression (GPR). Each model was independently trained and tested to assess its ability to capture the nonlinear relationships between these key parameters. Experimental results show that resistivity decreases significantly under increasing load due to geometrical effects. For a w/c ratio of 0.4, resistivity decreases by −12.48% at 100% SR and by −6.68% at 60% SR under 20% loading. Higher w/c ratios (0.5 and 0.6) exhibit more pronounced resistivity reductions due to increased porosity and ion mobility, with a maximum decrease of −13.68% for w/c = 0.6. Among the developed predictive models, the Matern 5/2 Gaussian process regression (GPR) model demonstrated the highest accuracy, achieving an RMSE of 5.21, R² of 0.99, MSE of 27.19, and MAE of 3.40, significantly outperforming the other approaches. Additionally, a permutation importance analysis revealed that the saturation ratio (SR) is the most critical variable influencing resistivity, followed by the water–cement ratio, while temperature has the least impact. These findings provide valuable insights into the durability assessment and corrosion prevention of reinforced concrete, offering practical implications for the optimization of material design and structural health monitoring in civil engineering. Full article

Validating the potential of multi-frequency synthetic aperture radar (SAR) data for multi-layer and profile soil moisture (SM) estimation modeling, we conducted an airborne multi-frequency SAR joint observation experiment (AMFSEX) over the Liao River Basin in China. The experiment simultaneously acquired airborne high spatial resolution quad-polarization (quad-pol) SAR data at five frequencies, including the Ka-, X-, C-, S-, and L-band. A preliminary “vegetation–soil” parameter estimation model based on the multi-frequency SAR data was established. Theoretical penetration depths of the multi-frequency SAR data were analyzed using the Dobson empirical model and the Hallikainen modified model. On this basis, a water cloud model (WCM) constrained by multi-polarization weighted and penetration depth weighted parameters was used to analyze the estimation accuracy of the multi-layer and profile SM (0–50 cm depth) under different vegetation types (grassland, farmland, and woodland). Overall, the estimation error (root mean square error, RMSE) of the surface SM (0–5 cm depth) ranged from 0.058 cm³/cm³ to 0.079 cm³/cm³, and increased with radar frequency. For multi-layer and profile SM (3 cm, 5 cm, 10 cm, 20 cm, 30 cm, 40 cm, 50 cm depth), the RMSE ranged from 0.040 cm³/cm³ to 0.069 cm³/cm³. Finally, a multi-input multi-output regression model (Gaussian process regression) was used to simultaneously estimate the multi-layer and profile SM. For surface SM, the overall RMSE was approximately 0.040 cm³/cm³. For multi-layer and profile SM, the overall RMSE ranged from 0.031 cm³/cm³ to 0.064 cm³/cm³. The estimation accuracy achieved by coupling the multi-source data (multi-frequency SAR data, multispectral data, and soil parameters) was superior to that obtained using the SAR data alone. The optimal SM penetration depth varied across different vegetation cover types, generally falling within the range of 10–30 cm, which holds true for both the scattering model and the regression model. This study provides methodological guidance for the development of multi-layer and profile SM estimation models based on the multi-frequency SAR data. Full article

This study presents a robust machine learning framework based on Gaussian process regression (GPR) to predict the tensile strength of polymer nanocomposites reinforced with various nanofillers and processed under diverse techniques. A comprehensive dataset comprising 25 polymer matrices, 22 surface functionalization methods, and 24 processing routes was constructed from the literature. GPR, coupled with Monte Carlo sampling across 2000 randomized iterations, was employed to capture nonlinear dependencies and uncertainty propagation within the dataset. The model achieved a mean coefficient of determination (R²) of 0.96, RMSE of 12.14 MPa, MAE of 7.56 MPa, and MAPE of 31.73% over 2000 Monte Carlo iterations, outperforming conventional models such as support vector machine (SVM), regression tree (RT), and artificial neural network (ANN). Sensitivity analysis revealed the dominant influence of Carbon Nanotubes (CNT) weight fraction, matrix tensile strength, and surface modification methods on predictive accuracy. The findings demonstrate the efficacy of the proposed GPR framework for accurate, reliable prediction of composite mechanical properties under data-scarce conditions, supporting informed material design and optimization. Full article

Steel corrosion prediction in concrete infrastructure remains a critical challenge for durability assessment and maintenance planning. This study presents a comprehensive framework integrating domain expertise with advanced machine learning for carbonation-induced corrosion prediction. Four Gaussian Process Regression (GPR) variants were systematically developed: Baseline GPR with manual optimization, Expert Knowledge GPR employing domain-driven dual-kernel architecture, GPR with Automatic Relevance Determination (GPR-ARD) for feature selection, and GPR-OptCorrosion featuring specialized multi-component composite kernels. The models were trained and validated using 180 carbonated mortar specimens with 15 systematically categorized variables spanning mixture, material, environmental, and electrochemical parameters. GPR-OptCorrosion achieved superior performance (R² = 0.9820, RMSE = 1.3311 μA/cm²), representing 44.7% relative improvement in explained variance over baseline methods, while Expert Knowledge GPR and GPR-ARD demonstrated comparable performance (R² = 0.9636 and 0.9810, respectively). Contrary to conventional approaches emphasizing electrochemical indicators, automatic relevance determination revealed supplementary cementitious materials (silica fume and fly ash) as dominant predictive factors. All advanced models exhibited excellent generalization (gaps < 0.02) and real-time efficiency (<0.006 s), with probabilistic uncertainty quantification enabling risk-informed infrastructure management. This research contributes to advancing machine learning applications in corrosion engineering and provides a foundation for predictive maintenance strategies in concrete infrastructure. Full article

This paper investigates the nonlinear bending analysis of nano-beams using the physics-informed neural network (PINN) method. The nonlinear governing equations for the bending of size-dependent nano-beams are derived from Hamilton’s principle, incorporating nonlocal strain gradient theory, and based on Euler–Bernoulli beam theory. In the PINN method, the solution is approximated by a deep neural network, with network parameters determined by minimizing a loss function that consists of the governing equation and boundary conditions. Despite numerous reports demonstrating the applicability of the PINN method for solving various engineering problems, tuning the network hyperparameters remains challenging. In this study, a systematic approach is employed to fine-tune the hyperparameters using hyperparameter optimization (HPO) via Gaussian process-based Bayesian optimization. Comparison of the PINN results with available reference solutions shows that the PINN, with the optimized parameters, produces results with high accuracy. Finally, the impacts of boundary conditions, different loads, and the influence of nonlocal strain gradient parameters on the bending behavior of nano-beams are investigated. Full article

High-resolution ISAR (Inverse Synthetic Aperture Radar) imaging plays a crucial role in dynamic target monitoring for aerospace, maritime, and ground surveillance. Among various remote sensing techniques, ISAR is distinguished by its ability to produce high-resolution images of non-cooperative maneuvering targets. To meet the increasing demands for resolution and robustness, modern ISAR systems are evolving toward wideband and multi-channel architectures. In particular, multi-channel configurations based on large-scale receiving arrays have gained significant attention. In such systems, each receiving element functions as an independent spatial channel, acquiring observations from distinct perspectives. These multi-angle measurements enrich the available echo information and enhance the robustness of target imaging. However, this setup also brings significant challenges, including inter-channel coupling, high-dimensional joint signal modeling, and non-Gaussian, mixed-mode interference, which often degrade image quality and hinder reconstruction performance. To address these issues, this paper proposes a Hybrid Variational Bayesian Multi-Interference (HVB-MI) imaging algorithm based on a hierarchical Bayesian framework. The method jointly models temporal correlations and inter-channel structure, introducing a coupled processing strategy to reduce dimensionality and computational complexity. To handle complex noise environments, a Gaussian mixture model (GMM) is used to represent nonstationary mixed noise. A variational Bayesian inference (VBI) approach is developed for efficient parameter estimation and robust image recovery. Experimental results on both simulated and real-measured data demonstrate that the proposed method achieves significantly improved image resolution and noise robustness compared with existing approaches, particularly under conditions of sparse sampling or strong interference. Quantitative evaluation further shows that under the continuous sparse mode with a 75% sampling rate, the proposed method achieves a significantly higher Laplacian Variance (LV), outperforming PCSBL and CPESBL by 61.7% and 28.9%, respectively and thereby demonstrating its superior ability to preserve fine image details. Full article

Urban mobility resilience plays a central role in sustainable urban planning discussions, especially considering the challenges of extreme events, climate change, and the increasing scarcity of fossil fuels. This study evaluates urban mobility resilience in Petrópolis (RJ), incorporating socio-spatial heterogeneity and energy vulnerability. This research fills methodological gaps in the literature by proposing a composite resilience index that integrates technical, socioeconomic, and fossil fuel dependency variables within a robust multicriteria framework. We selected eleven variables relevant to urban mobility and organized them into inference blocks. We normalized the variables using Gaussian functions, respecting their maximization or minimization characteristics. We applied the Analytic Hierarchy Process (AHP) to assign weights to the criteria and then aggregated and ranked the results using multicriteria analysis. The final index represents the adaptive capacity of urban territories facing the energy crisis, and we applied it spatially to the neighborhoods of Petrópolis. The analysis identified a significant concentration of neighborhoods with low resilience, particularly in quadrants, combining deficiencies in public transportation, high dependence on fossil fuels, and socioeconomic constraints. Factors such as limited pedestrian access, insufficient motorized public transport coverage, and a high proportion of elderly residents emerged as significant constraints on urban resilience. Intervention strategies that promote active mobility, improve accessibility, and diversify transportation modes proved essential for strengthening local resilience. The results emphasize the urgent need for public policies to reduce energy vulnerability, foster active mobility, and promote equity in access to transportation infrastructure. Full article

Time-frequency analysis (TFA) technology is an important tool for analyzing non-Gaussian mechanical fault vibration signals. In the complex background of infinite variance process noise and Gaussian colored noise, it is difficult for traditional methods to obtain the highly concentrated time-frequency representation (TFR) of fault vibration signals. Based on the insensitive property of fractional low-order statistics for infinite variance and Gaussian processes, robust fractional lower order adaptive linear chirplet transform (FLOACT) and fractional lower order adaptive scaling chirplet transform (FLOASCT) methods are proposed to suppress the mixed complex noise in this paper. The calculation steps and processes of the algorithms are summarized and deduced in detail. The experimental simulation results show that the improved FLOACT and FLOASCT methods have good effects on multi-component signals with short frequency intervals in the time-frequency domain and even cross-frequency trajectories in the strong impulse background noise environment. Finally, the proposed methods are applied to the feature analysis and extraction of the mechanical outer race fault vibration signals in complex background environments, and the results show that they have good estimation accuracy and effectiveness in lower MSNR, which indicate their robustness and adaptability. Full article

Space-based radar systems offer significant advantages for air surveillance, including wide-area coverage and extended early-warning capabilities. The integrated design of detection and imaging in space-based wideband radar further enhances its accuracy. However, in the wideband tracking mode, large aircraft targets exhibit extended characteristics. Measurements from the same target cross multiple range resolution cells. Additionally, the nonlinear observation model and uncertain measurement noise characteristics under space-based long-distance observation substantially increase the tracking complexity. To address these challenges, we propose a robust aerial target tracking method for space-based wideband radar applications. First, we extend the observation model of the gamma Gaussian inverse Wishart probability hypothesis density filter to three-dimensional space by incorporating a spherical–radial cubature rule for improved nonlinear filtering. Second, variational Bayesian processing is integrated to enable the joint estimation of the target state and measurement noise parameters, and a recursive process is derived for both Gaussian and Student’s t-distributed measurement noise, enhancing the method’s robustness against noise uncertainty. Comprehensive simulations evaluating varying target extension parameters and noise conditions demonstrate that the proposed method achieves superior tracking accuracy and robustness. Full article