Search Results (173)

Macro-scale modeling is a fundamental approach for assessing structural damage and occupant comfort in urban high-rises during earthquakes or typhoons. The key to its effectiveness is accurately reproducing dynamic responses and extracting modal characteristics. The critical issue is whether the macro-scale model can effectively capture Flexure-Shear Coupled (FSC) dynamic behavior. This paper proposes a macro-scale modeling method for high-rise structures with FSC dynamic behavior using deep learning (DL). FSC dynamic behavior is quantified by establishing Displacement Interaction Coefficients (DInC) under each mode shape. To account for the flexural resistance of horizontal members and the anti-overturning contribution of vertical members in high-rise structures, equivalent stiffness parameters representing horizontal and vertical members are introduced into the Lumped Parameter Model (LPM), enhancing the flexibility of the macro-scale model in expressing FSC dynamic behavior. The DInCs are used as input features to identify the LPM’s stiffness parameters, enabling efficient macro-scale modeling. The method was validated on a frame and a frame-core tube structure by comparing dynamic characteristics with their detailed finite element models. This method holds engineering application potential in areas requiring highly accurate and rapid structural characteristic or response calculations, such as seismic response analysis and design optimization of high-rise structures. Full article

Electrified roads represent an emerging transportation solution in the context of global energy transition. These systems enable vehicles equipped with roof-mounted pantographs to draw power from overhead contact lines while in motion, allowing continuous energy replenishment. The effectiveness of this energy transfer—namely, the quality of pantograph–catenary interaction—is significantly influenced by the pantograph’s equivalent mechanical parameters. This study develops a three-dimensional overhead catenary model and a five-mass pantograph model tailored to electrified roads. Under conditions of road surface irregularities, it investigates how variations in equivalent pantograph parameters affect key contact performance indicators. Simulation results are used to identify a new set of equivalent pantograph parameters that significantly improve the overall quality of pantograph–catenary interaction compared to the baseline configuration. Sensitivity analysis further reveals that, under road-induced excitation, pan-head stiffness is the most critical factor affecting contact performance, while pan-head damping, upper frame stiffness, and upper frame damping show minimal influence. By constructing a coupled dynamic model and conducting parameter optimization, this study elucidates the role of key pantograph parameters for electrified roads in determining contact performance. The findings provide a theoretical foundation for future equipment development and technological advancement. Full article

Special-shaped concrete-filled steel tube (CFST) frames can be embedded in partition walls to improve space utilization, but their frame-level seismic behavior across joint types remains under-documented. This study examines six two-story, single-bay frames with cruciform, T-, and L-shaped CFST columns and three joint configurations: external hoops with vertical ribs, fully bolted joints, and fully bolted joints with replaceable flange plates. Low-cycle reversed loading tests were combined with validated ABAQUS and OpenSees models to interpret mechanisms and conduct parametric analyses. All frames exhibited stable spindle-shaped hysteresis with minor pinching; equivalent viscous damping reached 0.13–0.25, ductility coefficients 3.03–3.69, and drift angles 0.088–0.126 rad. Hooped-and-ribbed joints showed the highest capacity and energy dissipation, while replaceable joints localized damage for rapid repair. Parametric results revealed that increasing the steel grade and steel ratio (≈5–20%) improved seismic indices more effectively than raising the concrete strength. Recommended design windows include axial load ratio < 0.4–0.5, slenderness ≤ 30, stiffness ratio ≈ 0.36, and flexural-capacity ratio ≈ 1.0. These findings provide symmetry-based, repair-oriented guidance for transportation buildings requiring rapid post-earthquake recovery. Full article

This paper analyzes the seismic performance and damage characteristics of high-rise frame-core-tube structures on soft soil, explicitly incorporating dynamic soil–pile–structure interaction (SSI). A refined 3D finite element model of a 52-storey soil–pile–structure system was developed in ABAQUS, utilizing viscous-spring boundaries and the equivalent nodal force method for seismic input. Nonlinear analyses under six seismic waves were compared to a fixed-base model neglecting SSI. Key findings demonstrate that SSI significantly alters structural response; it amplifies lateral displacements and inter-storey drift ratios throughout the structure, particularly at the top level. While total base shear decreased, frame column base shear forces substantially increased. SSI also reduced peak top-storey accelerations, diminished short-period spectral components, and prolonged the predominant period of response spectra. Analysis of member damage revealed SSI generally reduced compressive and tensile damage in core walls, floor slabs, and frame beams. Principal compressive stresses at the base of frame columns increased under SSI. These results highlight the necessity of including dynamic SSI in seismic analysis for high-rises on soft soil, specifically due to its detrimental amplification of forces in frame columns. Full article

In modern shock absorber development, the fatigue durability of shim-based clamped valve systems remains a critical factor influencing both performance and operational safety. In this study, the authors extend their previous research achievements by developing a fatigue life prediction methodology that integrates an established finite element framework with a strength-based fatigue model incorporating experimentally derived and validated Wöhler characteristics of the metal alloy used in the valve shims. The focus of this work is the validation of the proposed methodology for hydraulic shock absorbers equipped with shim stack valve systems, supporting the virtual pre-selection of valve configurations during the OEM design process. This approach enables substantial reductions in experimental testing and facilitates cost-effective development under realistic operating conditions. To address random-amplitude loading scenarios, the rainflow-counting algorithm was employed to convert complex load histories into equivalent constant-amplitude cycles, thereby accurately capturing material memory effects associated with stress–strain hysteresis. Experimental validation was conducted using a high-performance servo-hydraulic load frame tester. The validated model demonstrated a prediction uncertainty of 46% for random-amplitude lifetime estimation. Full article

Compared to traditional connection joints, hybrid connection joints are more suitable for large-span frames, especially for prefabricated buildings. This study aims to investigate the seismic performance of novel hybrid connection joints using the proposed innovative finite element modeling method based on the cohesion zone model (referred to as the CZM method). The crack development mechanism of the beam–column interface and the bond–slip mechanism of mild steel were investigated in this work; the performances of self-centering and energy dissipation were also studied using the CZM method. It is demonstrated that the CZM method can be used to accurately and efficiently estimate the performance of hybrid connection joints. This study also shows that the damage of mild steel, post-tensioned steel (referred to as PT steel), and concrete of the innovative hybrid connection joint is slight, the residual deformation of the joint is small, and the equivalent viscous damping coefficient ${\xi }_{\mathrm{e}\mathrm{q}}$ is between 7.8% and 14.85%, which shows good self-resetting and energy dissipation performance. Full article

Breathing phase detection is fundamental for various clinical and digital health applications, yet standard Mel Frequency Cepstral Coefficients (MFCCs) settings often limit classification performance. This study systematically optimized MFCC parameters, specifically the number of coefficients, frame length, and hop length, using a proprietary dataset of respiratory sounds (n = 1500 segments). Classification performance was evaluated using Support Vector Machines (SVMs) and benchmarked against deep learning models (VGGish, YAMNet, MobileNetV2). Optimal parameters (30 MFCC coefficients, 800 ms frame length, 10 ms hop length) substantially enhanced accuracy (87.16%) compared to default settings (80.96%) and performed equivalently or better than deep learning methods. A trade-off analysis indicated that a clinically practical frame length of 200–300 ms balanced accuracy (85.08%) and latency effectively. The study concludes that optimized MFCC parameters significantly improve respiratory phase classification, providing efficient and interpretable solutions suitable for real-time clinical monitoring. Future research should focus on validating these parameters in broader clinical contexts and exploring multimodal and federated learning strategies. Full article

Fiber-reinforced resin matrix composites are generally composed of fibers and matrix with significantly different properties, which are non-uniform and anisotropic in nature. Macro-failure criteria generally view composite plies as a uniform whole and do not accurately reflect fiber- and matrix-scale failures. In this study, the interface phase effect between fiber and matrix has been introduced into the frame of trans-scale analysis to better model the failure process, and the equivalent mechanical property characterization model of the interface phase has also been established. Combined with the macro–micro-strain transfer method, the trans-scale correlation of the mechanical response of the composite laminates between the macro scale and the fiber, matrix and interface micro scale has been achieved. Based on the micro-scale failure criterion and the stiffness reduction strategy, the trans-scale failure analysis method of composite materials incorporating the interface phase effect has been developed, which can simultaneously predict the failure modes of the matrix, fiber and interface phase. A numerical implementation of the developed trans-scale failure analysis method considering interface phase was carried out using the Python and Abaqus 2020 joint simulation technique. Case studies were carried out for three material systems, and the prediction data of the developed trans-scale failure analysis methodology incorporating interface phase effects for composite materials, the prediction data of the Linde failure criterion and the experimental data were compared. The comparison with experimental data confirms that this method has good prediction accuracy, and compared with the Linde and Hashin failure methods, only it can predict the failure mode of the fiber–matrix interface. The case analysis shows that its prediction accuracy has been improved by about 2–3%. Full article

This paper proposes a novel suspended seismic structure system called Base-suspended Pendulum Isolation (BSPI) structure. The BSPI structure can isolate seismic action and reduce structural seismic response by hanging the structure with hanger rods set at the base. The viscous dampers are installed in the isolation layer to dissipate earthquake energy and control the displacement. Firstly, the configuration of suspension isolation layer and mechanical model of the BSPI structure are described. Then, an equivalent scaled BSPI structure physical model was tested on the shaking table. The test results demonstrate that the BSPI structure has a good isolation effect under earthquakes, and the viscous dampers had an obvious control effect on the structure’s displacement and acceleration response. Finally, numerical simulation of the tests was carried out. The accuracy of the numerical models are confirmed by the good agreement between the simulation and test results. The numerical models for the BSPI structure and conventional reinforced concrete (RC) frame structure are built and analyzed using the commercial software ABAQUS. Research results indicate that the lateral stiffness of the BSPI structure is reduced greatly by installing the suspension layer, and the acceleration response of BSPI structure is significantly reduced under rare earthquakes, which is only 1/2 of that of the RC frame. The inter-story displacement of the BSPI structure is less than 1/100, which meets the seismic fortification goal and is reduced to 50% of that of the BSPI structure without damper under rare earthquakes. Full article

In this paper, we presents a case study involving the implementation experience and a methodological framework through a comprehensive comparative analysis of the YOLOX and YOLOv12 object detection models for agricultural automation systems deployed in the Jetson AGX Orin edge computing platform. We examined the architectural differences between the models and their impact on detection capabilities in data-imbalanced potato-harvesting environments. Both models were trained on identical datasets with images capturing potatoes, soil clods, and stones, and their performances were evaluated through 30 independent trials under controlled conditions. Statistical analysis confirmed that YOLOX achieved a significantly higher throughput (107 vs. 45 FPS, p < 0.01) and superior energy efficiency (0.58 vs. 0.75 J/frame) than YOLOv12, meeting real-time processing requirements for agricultural automation. Although both models achieved an equivalent overall detection accuracy (F1-score, 0.97), YOLOv12 demonstrated specialized capabilities for challenging classes, achieving 42% higher recall for underrepresented soil clod objects (0.725 vs. 0.512, p < 0.01) and superior precision for small objects (0–3000 pixels). Architectural analysis identified a YOLOv12 residual efficient layer aggregation network backbone and area attention mechanism as key enablers of balanced precision–recall characteristics, which were particularly valuable for addressing agricultural data imbalance. However, NVIDIA Nsight profiling revealed implementation inefficiencies in the YOLOv12 multiprocess architecture, which prevented the theoretical advantages from being fully realized in edge computing environments. These findings provide empirically grounded guidelines for model selection in agricultural automation systems, highlighting the critical interplay between architectural design, implementation efficiency, and application-specific requirements. Full article

Based on the need to enhance the seismic performance of point-supported steel frame precast cladding panel systems, this study proposes a novel friction-energy-dissipating connection joint. Through establishing refined finite element models, low-cycle reversed loading analyses and elastoplastic time-history analyses were conducted on three frame systems. These included a benchmark bare frame and two cladding-panel-equipped frame structures configured with energy-dissipating joints using different specifications of high-strength bolts (M14 and M20, respectively). The low-cycle reversed loading results demonstrate that the friction energy dissipation of the novel joints significantly improved the seismic performance of the frame structures. Compared to the bare frame, the frames equipped with cladding panels using M14 bolts demonstrated 10.9% higher peak lateral load capacity, 17.6% greater lateral stiffness, and 45.6% increased cumulative energy dissipation, while those with M20 bolts showed more substantial improvements of 22.8% in peak load capacity, 32.0% in lateral stiffness, and 64.2% in cumulative energy dissipation. The elastoplastic time-history analysis results indicate that under seismic excitation, the maximum inter-story drift ratios of the panel-equipped frames with M14 and M20 bolts were reduced by 42.7% and 53%, respectively, compared to the bare frame. Simultaneously, the equivalent plastic strain in the primary structural members significantly decreased. Finally, based on the mechanical equilibrium conditions, a calculation formula was derived to quantify the contribution of joint friction to the horizontal load-carrying capacity of the frame. Full article

Digital twin technology is emerging as a core technology that models physical objects or systems in a digital space and links real-time data to accurately reflect the state and behavior of the real world. For the effective operation of such digital twins, high-performance visualization methods that support an intuitive understanding of the vast amounts of data collected from sensors and enable rapid decision-making are essential. The proposed system is designed as a balanced 3D monitoring solution that prioritizes intuitive, real-time state observation. Conventional 3D-simulation-based systems, while offering high physical fidelity, are often unsuitable for real-time monitoring due to their significant computational cost. Conversely, 2D-based systems are useful for detailed analysis but struggle to provide an intuitive, holistic understanding of multiple assets within a spatial context. This study introduces a visualization approach that bridges this gap. By leveraging sensor data, our method generates a physically plausible representation 3D CAD models, enabling at-a-glance comprehension in a visual format reminiscent of simulation analysis, without claiming equivalent physical accuracy. The proposed method includes GPU-accelerated interpolation, the user-selectable application of geodesic and Euclidean distance calculations, the automatic resolution of CAD model connectivity issues, the integration of Physically Based Rendering (PBR), and enhanced data interpretability through ramp shading. The proposed system was implemented in the Unity3D environment. Through various experiments, it was confirmed that the system maintained high real-time performance, achieving tens to hundreds of Frames Per Second (FPS), even with complex 3D models and numerous sensor data. Moreover, the application of geodesic distance yielded a more intuitive representation of surface-based phenomena, while PBR integration significantly enhanced visual realism, thereby enabling the more effective analysis and utilization of sensor data in digital twin environments. Full article

A wave of energy efficiency-focused activity has spread across Europe in recent years, with ambitious goals for improving the energy performance of existing buildings through various directives. Among these existing buildings, there are older structures with heritage-protected facades. Some of the protected facades consist of timber plank frame walls, which were common in Norway in the 19th and early 20th centuries. Internal insulation is an option for increasing the energy efficiency of such walls while preserving their protected facades. However, this approach alters the moisture performance of the wall and introduces a potential risk for mould growth, which must be assessed. To better understand the performance of these walls, the $s_d$ values of traditional types of building paper have been tested, as timber plank frame walls comprise vertical planks covered in building paper. In addition, the risk of mould growth in timber plank frame walls has been evaluated using the one-dimensional simulation tool WUFI^® Pro by modelling the wall with internal retrofitting and varying input parameters. The types of building paper used have a wide range of vapour resistance values (diffusion-equivalent air layer thicknesses, $s_d$ values), which range from 0.008 m to 5.293 m. Adding 50 mm of interior insulation generally resulted in a low risk of mould growth, except in cases involving the use of a moisture-adaptive vapour barrier (MAVB). The MAVB did not result in an acceptable mould growth risk in any of the tested scenarios. Full article

This paper presents an advanced seismic performance evaluation of reinforced concrete (RC) seismically isolated frame structures under the conditions of rare earthquakes. By employing an elastic–plastic analysis in conjunction with a nonlinear multi-degree-of-freedom model, this study innovatively assesses the incremental dynamic vulnerability of isolated structures. A novel equivalent linearization method is introduced for both single- and two-degree-of-freedom isolation structures, providing a simplified yet accurate means of predicting seismic responses. The reliability of the modified Takeda hysteretic model is verified through comparative analysis with experimental data, providing a solid foundation for the research. Furthermore, a multi-degree-of-freedom shear model is employed for rapid elastic–plastic analysis, validated against finite element software, resulting in an impressive 85% reduction in computation time while maintaining high accuracy. The fragility analysis reveals the staggered upward trend in the vulnerability of the upper structure and isolation layer, highlighting the importance of comprehensive damage control to enhance overall seismic performance. Full article

Hand hygiene is paramount for public health, especially in critical sectors like healthcare and the food industry. Ensuring compliance with recommended hand washing gestures is vital, necessitating autonomous evaluation systems leveraging machine learning techniques. However, the scarcity of comprehensive datasets poses a significant challenge. This study addresses this issue by presenting an open synthetic hand washing dataset, created using 3D computer-generated imagery, comprising 96,000 frames (equivalent to 64 min of footage), encompassing eight gestures performed by four characters in four diverse environments. This synthetic dataset includes RGB images, depth/isolated depth images and hand mask images. Using this dataset, four neural network models, Inception-V3, Yolo-8n, Yolo-8n segmentation and PointNet, were trained for gesture classification. The models were subsequently evaluated on a large real-world hand washing dataset, demonstrating successful classification accuracies of 56.9% for Inception-V3, 76.3% for Yolo-8n and 79.3% for Yolo-8n segmentation. These findings underscore the effectiveness of synthetic data in training machine learning models for hand washing gesture recognition. Full article