Search Results (204)

This study investigates the traction response and operational risk of a compact ball-frame and tensioned-steel-cable drag-reduction system for submarine cable pulling inside HDD steel casings, based on local full-scale experiments. Thirteen test cases were designed by considering pipe curvature, device spacing, terminal reaction-force loading mode, and dry or sand–slurry in-casing conditions. In addition to the equivalent friction coefficient, three response descriptors, namely, the average traction force, peak coefficient, and fluctuation coefficient, were introduced to evaluate mean resistance, peak amplification, and process stability. The results show that pipe curvature significantly amplifies both traction peaks and response fluctuations, and should therefore be regarded as a key factor governing operational risk. The effect of device spacing is environment-dependent: under dry conditions, a moderate reduction in spacing improves rolling continuity, whereas under sand–slurry conditions, excessively dense deployment may aggravate local obstruction and response fluctuation. Stronger terminal reaction-force loading also increases peak amplification and instability. Based on these findings, a case-specific and experiment-oriented framework for operational-risk classification is proposed. The present results are intended to support traction-response characterization, device arrangement, and construction control under representative local conditions, rather than to replace full-scale field validation. Full article

The target price for naval equipment orders is driven by the coupling of multidimensional technical and economic factors, exhibiting typical characteristics such as high dimensionality, strong nonlinearity, multicollinearity, and small-sample fluctuations. Traditional cost estimation methods struggle to achieve high-precision fitting and interpretable decision support. To address these issues, this paper constructs an integrated prediction model that combines Boruta–Lasso two-stage feature selection, grid search-optimized CatBoost, and SHAP interpretability analysis. First, the Boruta algorithm is used for rough screening of feature significance, then Lasso regression is applied for sparse fine screening, effectively eliminating redundant features and significantly mitigating multicollinearity; grid search and five-fold repeated cross-validation are employed to optimize CatBoost hyperparameters, while 10 repeated experiments with random seeds are conducted to verify model generalization robustness. SHAP is used to quantify the marginal contribution of features, revealing nonlinear associations and statistical response transition points between core features and price. This study is based on 33 publicly available real data from main combat vessels, from which 198 modeling samples were generated through interpolation-based small-sample data augmentation. The interpolated samples were only used for data augmentation and were not considered independent empirical samples. All core conclusions were validated on the 33 original real samples, and there are no missing values in the dataset. Experimental results show that the proposed model achieved the best individual results on the test set, with a coefficient of determination of R² = 0.8949, root mean square error RMSE = 0.0554, and mean absolute error MAE = 0.0476. Across 10 repeated robustness experiments, the average results were R² = 0.8828, RMSE = 0.0586, and MAE = 0.0529, with overall performance better than comparison models such as XGBoost, random forest, and standard CatBoost. Ablation experiments validated the effectiveness of the two-stage Boruta–Lasso selection strategy in improving model accuracy and stability. SHAP attribution analysis shows that full-load displacement, number of vertical missile launch cells, number of phased array radars, and combat capability are core features highly correlated with price, all showing significant nonlinear positive correlations and clear statistical response transition points. The dataset in this study has no missing values, is entirely constructed based on publicly traceable data, and does not include confidential information such as internal shipyard costs. The findings reflect statistical associations rather than causal effects. However, the sample size and ship-type coverage are limited, so the model’s applicability is somewhat constrained, and its generalization ability needs to be further verified on larger-scale, multi-ship-type independent datasets. This model combines high prediction accuracy, strong robustness, and good interpretability, providing reliable technical support for ship equipment procurement pricing demonstration, full lifecycle cost management, and scientific procurement decision-making. Full article

The evolution from the Internet of Things (IoT) to the Internet of Vehicles (IoV) has expanded intelligent connectivity across embedded systems while increasing cybersecurity risks arising from large scale data exchange and device heterogeneity. As IoV environments become more dynamic and safety critical, centralized Intrusion Detection Systems (IDSs) face constraints related to latency, privacy exposure, and bandwidth overhead. These limitations motivate a transition to edge-enabled IoV architectures, where localized vehicular and anchor nodes supported by edge servers enable decentralized processing, enhanced privacy, and reduced communication load. To address these operational challenges, this paper proposes FedX (Federated Explainable Ensemble Intrusion Detection System), a privacy-preserving and explainable federated ensemble IDS that integrates XGBoost and LightGBM models across resource-constrained edge vehicles and roadside units (RSUs) to enable collaborative, low-latency anomaly detection without sharing raw data. By applying adaptive weighting based on model confidence and resource availability, FedX enhances robustness and efficiency while enabling explainable decisions via SHAP and LIME analysis, which highlights reliance on key features (flow duration, speed, RPM) for high-confidence (>97%) intrusion alerts grounded in domain-specific behavior. Privacy is further enforced through Gaussian differential privacy and secure aggregation to mitigate inference and inversion attacks. Experiments on the CICIoV2024 dataset show that FedX achieves 99.1% accuracy, outperforming existing federated ensemble IDS models by up to 2.1%. The system reduces communication overhead by 17% relative to full synchronization through adaptive weighted transmission and secure aggregation. It maintains negligible accuracy loss (<1.5%) under a strong privacy budget ($ϵ$ = 1.1). The deployment of proposed IDS on Raspberry Pi 4 underscores its efficacy for edge computing. Experimental results indicate that adaptive weighting yields a 1.8% performance increase, while resource profiling shows 45% lower CPU utilization and over 50% lower power consumption compared with centralized baselines. The findings demonstrate that FedX, combined with explainable AI enables trustworthy, interpretable, and energy-efficient intrusion detection for secure next-generation Edge-enabled IoV networks. Full article

This study validates Danielson Framework for Teaching (DFfT) instruments’ structure, dependability, and contextual appropriateness within the multicultural, standards-driven education system of the United Arab Emirates (UAE) in accordance with Vision 2021 and national teacher competency frameworks. Quantitative data were collected from 629 UAE schoolteachers through administering a questionnaire-based survey. Principal Component Analysis and Confirmatory Factor Analysis yielded discriminant, convergent, and construct validity in addition to internal consistency using the Composite Reliability Index and Average Variance Extracted for all scales. Four DFfT domains were shown to have a stable structure based on Principal Component Analysis results: planning and preparation (six factors, α = 0.92–0.99), learning environment (five factors, α = 0.98–0.99), learning experiences (five factors, α = 0.96–0.99), and principled teaching (six factors, α = 0.69–0.99). Notably, all constructs had excellent model fit with substantial factor loadings and inter-item as confirmed by the results of the Confirmatory Factor Analysis. With the exception of one minor subscale (α = 0.69), all dependability coefficients exceeded recommended benchmarks. The first-order full DFfT structural model of the four main domains validation demonstrated a reliable framework (CFI = 0.917, TLI = 0.902, IFI = 0.919, χ²/df = 1.635, and RMSEA = 0.078) for professional development, instructional improvement, and policy alignment with potential relevance beyond the UAE context, as well as psychometric soundness and contextual adaptability for teachers’ professional growth and evaluation in UAE schools. The study’s findings are significant, as they are the first to empirically validate the psychometric properties of the Danielson framework of teaching instruments in the UAE. Full article

Wind turbine blades are subjected to complex environmental conditions during long-term operation, which may lead to structural degradation and performance loss. To ensure structural integrity, fatigue testing prior to deployment is essential. This paper proposes a vision-based method for measuring the full-cycle breathing deformation of wind turbine blades during fatigue testing. The method captures dynamic image sequences of the blade’s hotspot cross-section using industrial cameras and employs a feature-based template matching approach to reconstruct the three-dimensional coordinates of target points. Through coordinate transformation, the deformation trajectories are obtained, enabling quantitative analysis of the blade’s dynamic responses in both flapwise and edgewise directions. A dedicated hardware–software system was developed and validated through full-scale fatigue experiments. Quantitative comparison with strain gage measurements shows that the proposed method achieves mean absolute deviations of 0.84 mm and 0.93 mm in two independent experiments, respectively, with closely matched deformation trends under typical loading conditions. These results demonstrate that the proposed method can reliably capture the global deformation behavior of the blade with millimeter-level accuracy, while significantly reducing instrumentation complexity compared to conventional contact-based approaches. The proposed method provides an effective and practical solution for full-field dynamic deformation measurement in blade fatigue testing, offering strong potential for structural health monitoring and early damage detection in wind turbine systems. Full article

With the increasing functional and geometric complexity of modern steel buildings, irregular beam-to-column joints are becoming increasingly common in engineering practice, while their seismic performance and force transfer mechanisms remain insufficiently understood. Based on previous full-scale cyclic loading tests on unequal-depth steel beam (UDSB) and staggered steel beam (SSB) joints incorporating inclined internal diaphragms, this study presents numerical simulations and parametric analyses of irregular steel beam to concrete-filled steel tube (CFST) column joints. Three-dimensional nonlinear finite element models were developed using ABAQUS and validated against experimental results. The strengthening effects of internal diaphragms and concrete infill were then comparatively investigated. The results indicate that internal diaphragms increase the initial stiffness and load-carrying capacity of the joints to approximately 2.0–2.3 times and 1.16–1.8 times, respectively, compared with joints without diaphragms, whereas concrete infill provides smaller enhancements of about 1.3 times in stiffness and 1.2–1.3 times in strength. In addition, the hysteretic response of joints without diaphragms shows good agreement with the post-fracture behavior observed in the experiments, validating the diaphragm fracture mechanism. A parametric study further demonstrates that, under cyclic loading, the beam depth ratio, staggered floor ratio, column wall thickness, column width, diaphragm thickness, and diaphragm opening diameter have significant influences on joint strength and stress distribution, while the effect of axial load ratio is relatively minor. Finally, a strength prediction method applicable to inclined-diaphragm UDSB and SSB joints is proposed, and corresponding fitted expressions are derived based on the parametric results. The findings provide useful guidance for the seismic design of irregular steel beam–CFST column joints incorporating internal diaphragms. Full article

This work presents an evaluation of the effectiveness of active ventilation methods compared to passive ventilation methods in a typical B + GF + 9 building, focusing on the impact of burner height location on smoke control performance. The numerical model was validated using a full-scale room fire experiment involving a 4350 kJ/s wood crib load, where the HRR was calibrated via the mass loss method, achieving an RMSE of 210 kW and MRE of 5.04%. FDS simulations were conducted across six scenarios involving burners on the ground, fifth, and ninth floors. The findings demonstrate that, while natural ventilation allows the stairwell to reach lethal conditions with temperatures exceeding 180 °C and CO concentrations above 0.24%, the implementation of top-level mechanical pressurization maintains temperatures below the 60 °C tenability threshold. The mechanical ventilation system extended the Available Safe Egress Time (ASET) by 75% to 110%, with effectiveness increasing as the burner elevation approached the fan location. Overall, the study provides a validated approach for transforming stairwells into protected refuge zones in existing mid-rise buildings. Overall, merging empirical with computational methods is a proven basis for simulating scaled-up, complicated layouts. This guarantees accurate initial conditions when analyzing urban fire emergencies. Full article

Accurate characterization of pavement responses under real traffic loading is essential for improving pavement design reliability. This study presents SmartPave, a full-scale embedded monitoring system for measuring multilayer pavement responses under heavy vehicle loading. The system integrates embedded multi-sensors to capture stress, strain, temperature, and moisture within pavement layers. Field experiments were conducted under static and moving loading conditions. The results show that peak vertical stresses in the granular base were approximately 1.7–2.0 times higher than those at the subgrade, indicating stress attenuation with depth, while tensile strains at the bottom of the asphalt layer ranged between 200 and 350 µε. Lower vehicle speeds increased load duration and amplified viscoelastic strain responses. These findings demonstrate the capability of the system to provide reliable field data for mechanistic analysis and model calibration. Full article

Pavement rutting is caused by grooves formed by vehicle traffic, affecting driving comfort, safety, and service life. Rutting detection methods have evolved from manual and automated approaches to intelligent detection for smart cities and maintenance. However, lightweight intelligent detection still faces challenges such as insufficient accuracy and technical complexity, and a mature system has yet to be established. This study aims to develop a portable intelligent terminal for pavement rut detection, which can address the challenges associated with traditional pavement rut detection while providing accuracy and reliability. In this study, rutting detection experiments were performed on a full-scale accelerated loading track to collect data on vibration acceleration, angular velocity, and attitude angles. Comparative experiments were carried out between traditional and lightweight detection methods. Subsequently, GRU-CNN, LSTM–Transformer, GRU, and LSTM models were developed to analyze and compare their performance in predicting rutting depth. The results show that the terminal operates stably, offering convenient usability and reliable data acquisition. Furthermore, vehicle angular velocity and roll angle emerge as critical indicators reflecting rutting impacts on driving states and prove suitable for pavement rut depth detection. The proposed GRU-CNN model achieves superior accuracy and overall performance relative to widely used models. Under synchronous detection conditions, the lightweight method yields a mean absolute error (MAE) of 1.22 mm, achieving performance improvements of 17.32%, 8.74%, and 10.08% over the LSTM–Transformer, GRU, and LSTM models, respectively. Additionally, the method yields a mean absolute percentage error of approximately 10.6%, representing error reductions of 15.87%, 19.08%, and 23.74% compared to the aforementioned baseline models, which meets application requirements. Innovation lies in the development of a lightweight intelligent terminal and GRU-CNN hybrid model that integrates vehicle dynamic parameters for large-scale pavement rutting detection. This study presents a lightweight, real-time pavement rutting detection method based on vehicle operation data for the construction and maintenance of smart cities and intelligent transportation infrastructure, combining the features of high cost effectiveness, high accuracy, and ease of large-scale application. Full article

We propose a Cognitive Load Theory (CLT)-informed attention mechanism for transformer-based text classification. The proposed attention mechanism computes a per-token cognitive-load signal—derived from attention entropy, margin-based classification uncertainty, and optional inverse document frequency—and maps this signal to a learnable attention “budget” that scales outgoing attention mass during decoding. Unlike architectural efficiency techniques such as Multi-Query or Grouped-Query Attention, the CLT mechanism requires no structural modifications and introduces only modest per-step computational overhead while preserving full compatibility with standard transformer architectures. Experiments across four datasets (IMDB, AG News, SST-2, and DBpedia) show that CLT-informed attention achieves accuracy comparable to or exceeding a fixed-budget baseline while delivering consistently lower test loss, faster convergence to the best validation checkpoint, reduced attention entropy, and strong alignment between cognitive load and attention mass. Among all variants, an entropy-only load signal yields the most stable and consistent performance across datasets. These results demonstrate that lightweight, cognitively motivated constraints can structure transformer attention while maintaining or improving downstream classification performance. Full article

A methodology was developed to evaluate the behavior of reinforced concrete slabs used in temporary traffic bridge systems installed over uncured cement concrete pavement sections using highly scaled-down experimental reinforced concrete slabs. A full-scale reinforced concrete slab was first designed and its behavior, such as strain and deflection, was numerically analyzed. A small-scale reinforced concrete slab was then designed considering a dimensional reduction ratio of 1/6. When using this reduction ratio, there is no actual reduced size steel bar, so the smallest size steel bar available must be used for placement. Therefore, numerical analyses were performed to design the steel bar arrangement of the small-scale slab so that the same behavior as that of the full-scale slab occurred. To conduct experiments, small-scale experimental slabs were fabricated according to the design. Since the size of coarse aggregates must be reduced in concrete used for small-scale slabs, specimens using the concrete mix design for full-scale slabs were also produced and the compressive strengths were compared to confirm that the strengths were the same. Next, a study was conducted on the selection of strain gauges that can be used in small-scale slab experiments, and a method for installing displacement gauges to accurately measure slab deflection was also designed. Based on this series of basic studies, load tests were performed to measure the strains and deflections of small-scale slabs. Comparing the measured behavior of the small-scale slab with the numerical analysis results, it was confirmed that the same behavior was observed. Therefore, the experimental results and numerical analysis results of the small-scale slab were consistent, and the numerical analysis results of the small-scale slab and the full-scale slab were identical, proving that the experimental results of the full-scale slab can be inferred through experiments using the small-scale slab. This study confirmed that if small-scale slabs are designed and manufactured to appropriately reflect the characteristics of full-scale slabs, even though the process is challenging, the behavior of full-scale slabs can be approximately determined through experiments using small-scale slabs. Full article

In the modular construction of liquefied natural gas (LNG) plants and receiving terminals, transport beams are critical components that enable modular mobility. However, these beams are susceptible to large deformations due to complex loads during land and sea transportation. Traditional monitoring methods (i.e., strain gauge and deflection meters) often suffer from low efficiency and poor accuracy and may disrupt operational continuity in real-time monitoring systems. This paper presents a non-contact, real-time deformation detection system for LNG modular transport beams based on digital image technology, which integrates a high-resolution camera with a real-time software framework to remotely monitor structural integrity. An experiment was conducted on a full-scale support column-transport beam frame with specialized connection joints designed for rapid assembly. Five digital image correlation (DIC) detection regions (5 cm × 5 cm) were established on box-shaped beam sleeves, column sleeves, and the end plates of the beam–column joints. In addition, displacement gauges were installed at the same DIC locations. The experimental results demonstrate that the DIC measurements show good agreement with traditional measurement methods, verifying the applicability of the proposed system for large-scale LNG engineering structures. Full article

Hydrogen-fueled gas turbines face challenges related to flashback risk, nitrogen oxide (NO_x) emissions, and operational flexibility. In this study, a Center-Graded Spiral Micromixing (CGSM) combustor was designed and experimentally investigated to enhance the robustness of fuel–air mixing under hydrogen-rich conditions. The proposed CGSM concept employs spiral microtubes to induce curvature-driven secondary flows, promoting mixing through airflow-controlled mechanisms rather than relying solely on fuel jet momentum. Numerical simulations were conducted to qualitatively analyze the internal flow and mixing characteristics of the spiral microtubes, followed by pressurized combustor experiments at an inlet pressure of 0.3 MPa and elevated air temperatures. The experimental results demonstrate stable combustion of pure hydrogen under lean conditions, with NO_x emissions being maintained below 25 ppm, corrected to 15% O₂, without observable flashback or combustion oscillations within the designated operating range (from ignition to full load). The combustor further exhibits stable operation with blended hydrogen–methane and hydrogen–ammonia fuels, enabling online fuel switching without hardware modification. Application tests on an 80 kW micro-gas turbine indicate that the CGSM combustor can support stable operation across the full range of load conditions, from ignition to full-load operation, under both simple- and reheat-cycle modes, with performance characteristics that are consistent with established operational standards for micro-gas turbines. These results suggest that the CGSM concept provides a feasible micromixing strategy for hydrogen and hydrogen-rich fuels at a moderate pressure and micro-gas turbine scale. Full article

With the rapid advancement in information technology, the digital twin and smart assembly process simulation have become an integral part of the design and manufacturing of high-precision products. However, conventional Tunnel Boring Machine (TBM) cutterhead design and on-site assembly planning remain largely experience-driven and fragmented, with limited interoperability between geological characterization, structural verification, and constructability validation. This study proposes a digital twin-driven framework for TBM cutterhead design optimization and assembly process simulation that integrates geology-aware design inputs, BIM-based information modelling, FEM-based structural assessment, and immersive virtual environments within a unified virtual–physical workflow. To ensure consistent data exchange across platforms, an IFC4.3-compliant ontology is established using a non-intrusive property-set (Pset) extension strategy to represent cutterhead components, geological parameters, FEM load cases/results, and assembly tasks. Tunnel-scale stress analysis and cutter–rock interaction modelling are used to define project-representative cutter loading envelopes, which are mapped to a high-fidelity cutterhead FEM model for iterative structural refinement. The optimized configuration is then transferred to a game-engine/VR environment to support full-scale design inspection and assembly rehearsal, followed by manufacturing and field deployment with bidirectional feedback. To validate the proposed framework, an implementation case study of a deep hard-rock tunnelling project is presented where five design iterations were tracked across BIM–FEM–VR and nine constructability issues detected and resolved prior to assembly. The results indicate that the proposed digital twin approach strengthens traceability from geology to loading to structural response, reduces localized stress concentration at critical interfaces, and improves assembly readiness for complex tunnelling projects. Full article

Ballast void formation is a known issue in railway turnouts, yet the underlying mechanisms remain insufficiently understood. This study investigates the mechanical response of a long turnout sleeper lying on a ballast bed under loading using both full-scale laboratory experiments and Discrete Element Method (DEM) simulations to study the correlation between applied load, sleeper deformation, sleeper-ballast interface pressure and residual settlement. The DEM simulations employed a deformable sleeper model using the PFacet approach in the Yade framework and an elasto-plastic contact law accounting for edge breakage (Conical Damage Model) to reproduce ballast-ballast and sleeper-ballast contact behaviour. Results show that the DEM model can replicate key experimental trends, including asymmetric sleeper bending, uplift, and the evolution of ballast pressure distribution in the short term. Under extended cyclic loading, the simulation reproduces the progressive formation of stable bedding conditions and the emergence of ballast voids, aligning with experimental observations. A simplified approach to represent USPs via reduced contact stiffness yielded realistic deformation and pressure behaviour, although residual settlement differed. The results demonstrate that DEM can reproduce and explain sleeper-ballast interaction mechanisms, providing mechanistic insight into how uneven pressure distribution and ballast rearrangement contribute to void formation in turnouts. Full article