Search Results (16,442)

Glass Fiber-Reinforced Polymer (GFRP) bars are becoming increasingly common in structural engineering applications due to their superior material properties, mainly their resistance to corrosion due to their metallic nature in comparison to steel reinforcement and their improved durability in alkaline environments compared to CFRP and BFRP reinforcement. However, GFRP bars also suffer from a few limitations. One of the main issues that affects the performance of GFRP reinforcing bars is their bond with concrete, which may differ from the bond between traditional steel bars and concrete. However, despite the wide attention of researchers, there has not been a critical review of the recent research progress on bond behavior between GFRP bars and concrete. The objective of this paper is to provide an overview of the current state of research on bond in GFRP-reinforced concrete in an attempt to systematize the existing scientific knowledge. The study summarizes experimental investigations that directly measure bond strength and investigates the different factors that influence it. Additionally, an overview of the analytical and empirical models used to simulate bond behavior is then presented. The findings indicate the dependence of the bond on several factors that include bar diameter, bar surface, concrete strength, and embedment length. Additionally, it was concluded that both traditional and more recent bond models do not explicitly account for the effect of different factors, which highlights the need for improved bond models that do not require calibration with experimental tests. Full article

This research presents a novel method for detecting building roof types using deep learning models based on height profiles from high-resolution digital surface models. While deep learning has proven effective in digit, handwritten, and time series classification, this study focuses on the emerging and crucial area of height profile detection for building roof type classification. We propose an innovative approach to automatically generate, classify, and detect building roof types using height profiles derived from normalized digital surface models. We present three distinct methods to detect seven roof types from two height profiles of the building cross-section. The first two methods detect the building roof type from two-dimensional (2D) height profiles: two binary images and a two-band spectral image. The third method, vector-based, detects the building roof type from two one-dimensional (1D) height profiles represented as two 1D vectors. We trained various one- and two-dimensional convolutional neural networks on these 1D and 2D height profiles. The DenseNet201 network could directly detect the roof type of a building from two height profiles stored as a two-band spectral image with an average accuracy of 97%, even in the presence of consecutive chimneys, dormers, and noise. The strengths of this approach include the generation of a large, detailed, and storage-efficient labeled height profile dataset, the development of a robust classification method using both 1D and 2D height profiles, and an automated workflow that enhances building roof type detection. Full article

Halifax Harbour, a major seaport in Nova Scotia that is approximately 100 km southeast of the Bay of Fundy, comprises a deep inner region called Bedford Basin, connected to the adjacent ocean by a shallow channel called The Narrows. A study of sea level and currents reveals the presence of episodic oscillations in The Narrows, with a period of approximately 2 h. The oscillation strength varies from day to day and, to some extent, through the seasons. The median amplitude of the associated sea level variation is $18\%$ that of the de-tided signal, rising to $32\%$ at the 95-th percentile. Values this large may be of concern for the transit of deep-draft vessels through shallow parts of the harbour and for the clearance of tall vessels under the two bridges that span The Narrows. Another concerning issue is the matter of oscillations being superimposed on storm surges. In addition to such direct effects of sea level variation, shear associated with the oscillations may increase the turbulent mixing in the region, affecting the overall state of this estuarine system. We explore the nature of the oscillations as a first step towards the improvement of prediction schemes for sea level and currents in the region. This involves an analysis of the oscillations in the context of seiche and Helmholtz resonance theories and the use of a 2D numerical model to handle realistic bathymetric conditions and other complications that the simpler theories cannot address. We conclude that the predictions of Helmholtz resonance theory are in reasonable agreement with both the observations and the predictions of the numerical model. Full article

This paper presents an advanced time-series forecasting methodology that integrates multiple machine learning models to improve data prediction in enhanced long-range navigation (eLoran) systems. The analysis evaluates five forecasting approaches: multivariate linear regression, long short-term memory (LSTM) networks, random forest (RF), a fusion model combining LSTM and RF, and a dynamic weighting (DW) model. The results demonstrate that the DW model achieves the highest prediction accuracy while maintaining strong computational efficiency, making it particularly suitable for real-time applications with stringent performance requirements. Although the LSTM model effectively captures temporal dependencies, it demands considerable computational resources. The hybrid model utilises the strengths of LSTM and RF to enhance the accuracy but involves extended training times. By contrast, the DW model dynamically adjusts the relative contributions of LSTM and RF on the basis of the data characteristics, thereby enhancing the accuracy while significantly reducing the computational demands. Demonstrating exceptional performance on the ASF2 dataset, the DW model provides a well-balanced solution that combines precision with operational efficiency. This research offers valuable insights into optimising additional secondary phase factor (ASF) prediction in eLoran systems and highlights the broader applicability of real-time forecasting models. Full article

With the increasing environmental pollution and resource consumption caused by automobiles, a lightweight design of automobiles is the best solution at present. In this paper, the load-bearing frame of pure electric SUVs is taken as the research object. The finite element analysis method is used to analyze the strength, stiffness and modal performance of the load-bearing frame, and the material selection of the frame is optimized according to the analysis results to achieve a lightweight design. First, a three-dimensional model of the pure electric SUV frame is established using SolidWorks software 2019 and then imported into ANSYS 2024 R1 Workbench for meshing and material property definition. Then, through finite element static analysis, the various force conditions of the frame under three typical working conditions of full-load bending, full-load braking and full-load turning are simulated; the stress distribution and deformation of the frame under different working conditions are confirmed; and the strength and stiffness performance of the frame are evaluated. After the above analysis, a modal analysis of the frame is carried out, and the natural frequency and vibration mode of the frame are finally obtained. According to the analysis results, the material replacement method is selected to optimize the lightweight design of the frame. The results show that the weight of the frame is significantly reduced after material optimization, while still meeting the strength, stiffness and modal performance requirements. This article provides a certain reference value for the lightweight design of pure electric SUV frames in the future. Full article

The mechanical characteristics of soil–rock mixtures (S-RMs) are essential for ensuring geotechnical engineering stability and are significantly influenced by the microstructure’s contact network configuration. Due to the irregularity of particle shapes and the variability in particle grading with S-RMs, their macro-mechanical characteristics and mesoscopic contact skeleton distribution exhibit increased complexity. To further elucidate the macro-mesoscopic mechanical behavior of S-RMs, this study employed the DEM to develop a model incorporating irregular specimens representing various states, based on CT scan outlines, and applied flexible boundary conditions. A main skeleton system of contact force chains is an effective methodology for characterizing the dominant structural features that govern the mechanical behavior of soil–rock mixture specimens. The results demonstrate that the strength of S-RMs was significantly influenced by gravel content and consolidation state; however, the relationship is not merely linear but rather intricately associated with the strength and distinctiveness of the contact force chain skeleton. In the critical state, the mechanical behavior of S-RMs was predominantly governed by the characteristics of the principal contact force skeleton: the contact force skeleton formed by gravel–gravel, despite having fewer contact forces, exhibits strong contact characteristics and an exceptionally high-density distribution of weak contacts, conferring the highest shear strength to the specimens. Conversely, the principal skeleton formed through gravel–sand exhibits contact characteristics that are less distinct compared to those associated with strong contacts. Simultaneously, the probability density distribution of weak contacts diminishes, resulting in reduced shear strength. The contact skeleton dominated by sand–sand contact forces displays similar micro-mechanical characteristics yet possesses the weakest macroscopic behavior strength. Consequently, the concept of the main skeleton of contact force chains utilized in this study presents a novel research approach for elucidating the macro- and micro-mechanical characteristics of multiphase media. Full article

Microbially-induced calcite precipitation (MICP) has emerged as a promising bio-geotechnical technique for sustainable soil improvement, yet accurate prediction of treatment effectiveness remains challenging due to complex multi-factor interactions. This study develops an ensemble learning framework (LARO-EnML) for predicting the unconfined compressive strength (UCS) of MICP-treated sand. A comprehensive database containing 402 experimental datasets was utilised in the study, consisting of unconfined compression test results from bio-cemented sands with eight key input parameters considered. The performance evaluation demonstrates that LARO-EnML achieves superior predictive accuracy, with RMSE of 0.5449, MAE of 0.2853, R² of 0.9570, and OI of 0.9597 on the test data, significantly outperforming other models. Model interpretability analysis reveals that calcite content serves as the most influential factor, with a strong positive correlation to strength enhancement, while urease activity exhibits complex, staged influence characteristics. This research contributes to advancing the practical implementation of MICP technology in geotechnical engineering by offering both accurate predictive capability and enhanced process understanding through interpretable ML approaches. Full article

The implantable capsule grouting pile is a novel pile foundation technology in which a capsule is affixed to the side of the implanted pile to facilitate grouting and achieve extrusion-based reinforcement. This technique is designed to improve the bearing capacity of implanted piles in coastal areas with deep, soft soil. This study conducted model tests involving multiple grouting positions across different foundation types to refine the construction process and validate the enhancement of bearing capacity. Systematic measurements and quantitative analyses were performed to evaluate the earth pressure distribution around the pile, the resistance characteristics of the pile end, the evolution of side friction resistance, and the overall bearing performance. Special attention was given to variations in the lateral friction resistance adjustment coefficient under different working conditions. Furthermore, an actual case analysis was conducted based on typical soft soil geological conditions. The results indicated that the post-grouting process formed a dense soil ring through the expansion and extrusion of the capsule, resulting in increased soil strength around the pile due to increased lateral earth pressure. Compared to conventional piles, the grouted piles exhibited a synergistic improvement characterized by reduced pile end resistance, enhanced side friction resistance, and improved overall bearing capacity. The ultimate bearing capacity of model piles at different grouting depths across different foundation types increased by 6.8–22.3% compared with that of ordinary piles. In silty clay and clayey silt foundations, the adjustment coefficient η_s of lateral friction resistance of post-grouting piles ranged from 1.097 to 1.318 and increased with grouting depth. The findings contribute to the development of green pile foundation technology in coastal areas. Full article

Driver models, which provide mathematical or computational representations of human driving behavior, are crucial for intelligent driving systems by enabling stable and repeatable operations. However, existing models typically employ fixed weighting parameters to simulate preview delay, failing to reflect individual driver differences and real-time dynamic behaviors. This paper proposes a Brain-Memory Driver Model (BMDM) that emulates human brain memory mechanisms to dynamically adjust preview weights by integrating global path curvature, real-time vehicle speed, and steering torque. This emulation involves a three-stage process: capturing data in an Instantaneous Memory (IM) region, filtering data via a forgetting mechanism in a Short-Time Memory (STM) region to reduce scale, and retaining data based on correlation strength in a Long-Time Memory (LTM) region for persistent mining. By deploying a trained behavioral memory database, the model dynamically calibrates preview weights based on the driver’s state and real-time curvature variations under different road conditions. This enables the model to more accurately simulate authentic preview characteristics and improves its adaptability. Simulation results from an automated steering case study demonstrate that the improved model exhibits control performance closer to the real driving process, reproducing authentic steering behavior within the human–vehicle–road closed-loop system from an intelligent biomimetic perspective. Full article

In this study, a kind of sustainable ultra-high-performance concrete (UHPC) was designed by using coal gangue ceramsite (CGC) and a modified Andreasen–Andersen model. However, when CGC lightweight aggregate with high water absorption is used in UHPC with a low water–cement ratio, CGC has an adverse effect on the working performance of UHPC and may lead to the decrease of mechanical properties. This study found that a 5% silane coupling agent KH560 can make CGC hydrophobic, and cause its contact angle to increase from 0° to 111.32°. Adding 100% hydrophobic modified CGC into UHPC will significantly improve its working performance, with the highest increase of 38.51%. At the same time, the addition of 20% modified CGC can further improve the compressive strength of UHPC (28 days reached 150.1 MPa), reduce the internal porosity by 21.4%, and make the interface bond more compact. In addition, the hydration degree of UHPC has also been improved, a result caused by the cement obtaining more free water for a more complete hydration reaction. This study can provide a new scheme for solving the problem of the solid waste of coal gangue. Full article

In response to the high-performance requirements of concrete materials under complex triaxial stress states and water-containing environments in marine engineering, this study focuses on water-containing basalt fiber-reinforced concrete (BFRC). Uniaxial compression and splitting tensile tests were conducted on specimens with different fiber contents (0.0%, 0.05%, 0.10%, 0.15%, and 0.20%) to determine the optimal fiber content of 0.1%. The compressive strength of the concrete with this fiber content increased by 13.5% compared to the control group without fiber, reaching 36.90 MPa, while the tensile strength increased by 15.9%, reaching 2.33 MPa. Subsequently, NMR and SEM techniques were employed to analyze the internal pore structure and micro-morphology of BFRC. It was found that an appropriate amount of basalt fiber (content of 0.1%) can optimize the pore structure and form a reticular three-dimensional structure. The pore grading was also improved, with the total porosity decreasing from 7.48% to 7.43%, the proportion of harmless pores increasing from 4.03% to 4.87%, and the proportion of harmful pores decreasing from 1.67% to 1.42%, thereby significantly enhancing the strength of the concrete. Further triaxial compression tests were conducted to investigate the mechanical properties of BFRC under different confining pressures (0, 3, and 6 MPa) and water contents (0%, 1%, 2%, and 4.16%). The results showed that the stress–strain curves primarily underwent four stages: initial crack compaction, elastic deformation, yielding, and failure. In terms of mechanical properties, when the confining pressure increased from 0 MPa to 6 MPa, taking dry sandstone as an example, the peak stress increased by 54.0%, the elastic modulus increased by 15.7%, the peak strain increased by 37.0%, and the peak volumetric strain increased by 80.0%. In contrast, when the water content increased from 0% to 4.16%, taking a confining pressure of 0 MPa as an example, the peak stress decreased by 27.4%, the elastic modulus decreased by 43.2%, the peak strain decreased by 59.3%, and the peak volumetric strain decreased by 106.7%. Regarding failure characteristics, the failure mode shifted from longitudinal splitting under no confining pressure to diagonal shear under confining pressure. Moreover, as the confining pressure increased, the degree of failure became more severe, with more extensive cracks. However, when the water content increased, the failure degree was relatively mild, but it gradually worsened with further increases in water content. Based on the CDP model, a numerical model for simulating the triaxial compression behavior of BFRC was developed. The simulation results exhibited strong consistency with the experimental data, thereby validating the accuracy and applicability of the model. Full article

Background: Sarcopenia significantly affects the health and quality of life in older adults. Exercise combined with nutritional interventions is widely recognized as an effective strategy for improving sarcopenia outcomes. However, current studies rarely focus on differential effects across subpopulations with distinct demographic and health characteristics. This study aimed to explore the effects of combined exercise and nutrition interventions on sarcopenia-related outcomes, considering the variations in population characteristics. Methods: A systematic search was conducted across PubMed, Embase, the Web of Science, and Cochrane Library, covering the literature published between January 2010 and March 2025. Only randomized controlled trials (RCTs) evaluating combined exercise and nutritional interventions for sarcopenia were included. The primary outcomes were handgrip strength (HS), the skeletal muscle mass index (SMI), gait speed (GS), and the five-times sit-to-stand test (5STS). The mean differences (MD) with 95% confidence intervals (CIs) were calculated. Random-effects models were used for the meta-analysis and subgroup comparisons. Results: Fifteen RCTs involving 1258 participants in the intervention group and 1233 in the control group were included. Exercise combined with nutritional interventions significantly improved sarcopenia-related outcomes. HS improved with a pooled MD of 1.77 kg (95% CI: 0.51 to 3.03, p = 0.006); SMI increased by 0.22 kg/m² (95% CI: 0.09 to 0.35, p = 0.0007); GS improved by 0.09 m/s (95% CI: 0.04 to 0.14, p = 0.0002); and 5STS performance improved with a time reduction of −1.38 s (95% CI: −2.47 to −0.28, p = 0.01). Subgroup analyses indicated that the intervention effects varied according to age, BMI, and living environment. Conclusions: Exercise combined with nutrition is effective in improving key outcomes associated with sarcopenia in older adults. The magnitude of these effects differed across population subgroups, underscoring the importance of tailoring interventions to specific demographic and health profiles. Full article

The Anderson localization of phonons in disordered superlattices has been proposed as a route to suppress thermal conductivity beyond the limits imposed by conventional scattering mechanisms. A commonly used signature of phonon localization is the emergence of the nonmonotonic dependence of thermal conductivity $\kappa$ on system length L, i.e., a $\kappa$-L maximum. However, such behavior has rarely been observed. In this work, we conduct extensive non-equilibrium molecular dynamics (NEMD) simulations, using the LAMMPS package, on both periodic superlattices (SLs) and aperiodic random multilayers (RMLs) constructed from Si/Ge and Lennard-Jones materials. By systematically varying acoustic contrast, interatomic bond strength, and average layer thickness, we examine the interplay between coherent and incoherent phonon transport in these systems. Our two-phonon model decomposition reveals that coherent phonons alone consistently exhibit a strong nonmonotonic $\kappa$-L. This localization signature is often masked by the diffusive, monotonically increasing contribution from incoherent phonons. We further extract the ballistic-limit mean free paths for both phonon types, and demonstrate that incoherent transport often dominates, thereby concealing localization effects. Our findings highlight the importance of decoupling coherent and incoherent phonon contributions in both simulations and experiments. This work provides new insights and design principles for achieving phonon Anderson localization in superlattice structures. Full article

Remote sensing has emerged as a promising technology for large-scale detection and updating of global wind turbine databases. High-resolution imagery (e.g., Google Earth) facilitates the identification of offshore wind turbines (OWTs) but offers limited offshore coverage due to the high cost of capturing vast ocean areas. In contrast, medium-resolution imagery, such as 10-m Sentinel-2, provides broad ocean coverage but depicts turbines only as small bright spots and shadows, making accurate detection challenging. To address these limitations, We propose a novel deep learning approach to capture the variability in OWT appearance and shadows caused by changes in solar illumination and satellite viewing geometry. Our method learns intrinsic, imaging geometry-invariant features of OWTs, enabling robust detection across multi-seasonal Sentinel-2 imagery. This approach is implemented using Faster R-CNN as the baseline, with three enhanced extensions: (1) direct integration of imaging parameters, where Geowise-Net incorporates solar and view angular information of satellite metadata to improve geometric awareness; (2) implicit geometry learning, where Contrast-Net employs contrastive learning on seasonal image pairs to capture variability in turbine appearance and shadows caused by changes in solar and viewing geometry; and (3) a Composite model that integrates the above two geometry-aware models to utilize their complementary strengths. All four models were evaluated using Sentinel-2 imagery from offshore regions in China. The ablation experiments showed a progressive improvement in detection performance in the following order: Faster R-CNN < Geowise-Net < Contrast-Net < Composite. Seasonal tests demonstrated that the proposed models maintained high performance on summer images against the baseline, where turbine shadows are significantly shorter than in winter scenes. The Composite model, in particular, showed only a 0.8% difference in the F1 score between the two seasons, compared to up to 3.7% for the baseline, indicating strong robustness to seasonal variation. By applying our approach to 887 Sentinel-2 scenes from China’s offshore regions (2023.1–2025.3), we built the China OWT Dataset, mapping 7369 turbines as of March 2025. Full article

The efficient depolymerization of lignin has become a key challenge in the preparation of high-value-added chemicals. Graphitic carbon nitride (g-C₃N₄)-based photocatalytic system shows potential due to its mild and green characteristics over other depolymerization methods. However, its inherent defects, such as a wide band gap and rapid carrier recombination, severely limit its catalytic performance. In this paper, a g-C₃N₄ modification strategy of K⁺ doping and surface alkalinization is proposed, which is firstly applied to the photocatalytic depolymerization of the lignin β-O-4 model compound (2-phenoxy-1-phenylethanol). K⁺ doping is achieved by introducing KCl in the precursor thermal polymerization stage to weaken the edge structure strength of g-C₃N₄, and post-treatment with KOH solution is combined to optimize the surface basic groups. The structural/compositional evolution of the materials was analyzed by XRD, FTIR, and XPS. The morphology/element distribution was visualized by SEM-EDS, and the optoelectronic properties were evaluated by UV–vis DRS, PL, EIS, and transient photocurrent (TPC). K⁺ doping and surface alkalinization synergistically regulate the layered structure of the material, significantly increase the specific surface area, introduce nitrogen vacancies and hydroxyl functional groups, effectively narrow the band gap (optimized to 2.35 eV), and inhibit the recombination of photogenerated carriers by forming electron capture centers. Photocatalytic experiments show that the alkalinized g-C₃N₄ can completely depolymerize 2-phenoxy-1-phenylethanol with tunable product selectivity. By adjusting reaction time and catalyst dosage, the dominant product can be shifted from benzaldehyde (up to 77.28% selectivity) to benzoic acid, demonstrating precise control over oxidation degree. Mechanistic analysis shows that the surface alkaline sites synergistically optimize the C_β-O bond breakage path by enhancing substrate adsorption and promoting the generation of active oxygen species (·OH, ·O₂⁻). This study provides a new idea for the efficient photocatalytic depolymerization of lignin and lays an experimental foundation for the interface engineering and band regulation strategies of g-C₃N₄-based catalysts. Full article