Search Results (290)

The accurate identification of wood patterns is critical for optimizing the use of sustainable wood building materials, promoting resource efficiency, and reducing waste in construction. This study presents a deep learning-based approach for enhanced wood material recognition, combining EfficientNet architecture with advanced data augmentation techniques to achieve robust classification. The augmentation strategy incorporates geometric transformations (flips, shifts, and rotations) and photometric adjustments (brightness and contrast) to improve dataset diversity while preserving discriminative wood grain features. Validation was performed using a controlled augmentation pipeline to ensure realistic performance assessment. Experimental results demonstrate the model’s effectiveness, achieving 88.9% accuracy (eight out of nine correct predictions), with further improvements from targeted image preprocessing. The approach provides valuable support for preliminary sustainable building material classification, and can be deployed through user-friendly interfaces without requiring specialized AI expertise. The system retains critical wood pattern characteristics while enhancing adaptability to real-world variability, supporting reliable material classification in sustainable construction. This study highlights the potential of integrating optimized neural networks with tailored preprocessing to advance AI-driven sustainability in building material recognition, contributing to circular economy practices and resource-efficient construction. Full article

The service years of the milled pavement are varied in numerous SBS modified asphalt pavement milling assignments. To investigate the milling characteristics of SBS (styrene–butadiene–styrene) modified asphalt pavements with different service years, the values of the bonding parameters were calibrated and verified and then used to build three simulation models for the milling of old asphalt pavements with service years of 2~3 years, 7~8 years, and 11~12 years, respectively. The milling characteristics of SBS modified asphalt pavements with different service years were investigated using the moving speed $v$ and rotating speed $\omega$ of the milling rotor as test factors, and the particle bonding ratio ($R_b$) and rotor average force ($F_a$) as test indexes. The results demonstrate that the following: The regularity of the effects of milling rotor moving speed and rotating speed on the particle bonding ratio and milling rotor average forces remained consistent overall as the pavement age increased. For the same milling parameters, the particle bonding ratio and the rotor average force are reduced. From 2~3 years old pavements to 7~8 years old pavements, the overall reduction in the particle bonding ratio indicator is about 12%, and the average force on the milling rotor is about 24%. From 7~8 years old pavements to 11~12 years old pavements, the overall reduction in the particle bonding ratio indicator is about 3%, and the average force on the milling rotor is about 15%. Full article

Preserving heritage sites is a complex challenge that requires multidisciplinary approaches, combining scientific accuracy with cultural and historical sensitivity. In alignment with UNESCO’s conservation guidelines, this study investigated the airflow dynamics and wind-induced structural effects within ancient architecture using advanced computational fluid dynamics (CFD). The study site was the Na Phra Meru Historical Temple in Ayutthaya, Thailand, where the shear stress transport $k-\omega$ turbulence model was applied to analyze distinctive airflow patterns. A high-precision 3D computational domain was developed using Faro focus laser scanning technology, with the CFD results being validated based on onsite experimental data. The findings provided critical insights into the temple’s ventilation behavior, revealing strong correlations between turbulence characteristics, wind speed, temperature, and relative humidity. Notably, the small slit windows generated complex flow mixing, producing a large internal recirculation zone spanning approximately 70% of the central interior space. In addition to airflow distribution, the study evaluated the aerodynamic forces and rotational moments acting on the structure based on five prevailing wind directions. Based on these results, winds from the east and northeast generated the highest aerodynamic loads and rotational stresses, particularly in the lateral and vertical directions. Overall, the findings highlighted the critical role of airflow and wind-induced forces in the deterioration and long-term stability of heritage buildings. The study demonstrated the value of integrating CFD, environmental data, and structural analysis to bridge the gap between conservation science and engineering practice. Future work will explore further the interactions between wall moisture and the multi-layered pigments in mural paintings to inform preservation practices. Full article

This study proposes a practical method for the early detection of failure signs in a rotating biological contactor (RBC) system that has been in long-term operation at a municipal solid waste landfill. Seventeen years of inspection logs, recorded between 2006 and 2023, were digitized and analyzed with a focus on abnormal noise, electric current values, operational status, and failure history. The analysis revealed that frequent occurrences of abnormal noise and sudden fluctuations in current tend to precede equipment failures. Based on these findings, we developed a scoring model for the predictive maintenance of RBCs. Traditionally, determining the score required professional knowledge such as performing a sensitivity analysis. However, by utilizing AI (ChatGPT o4), we were able to obtain recommended values for these parameters. This means that operators can now build and adjust a scoring model for predictive maintenance of RBCs according to their specific on-site conditions. On the other hand, sudden increases in current and abnormal noises were previously considered strong indicators of failure prediction. These parameters will depend on factors such as the sensitivity of electrical current meters and surrounding noise. Therefore, depending on the specific environmental conditions at the site, the scoring model developed in this study may have limited predictive accuracy. Full article

The curing reaction of hydroxyl-terminated polybutadiene (HTPB) solid propellant slurry alters its internal molecular structure, leading to variations in rheological properties. This study investigates the evolution of the rheological behaviour of HTPB propellant slurry during the curing process. Rheological parameters of the slurry at different curing stages were measured using a rotational rheometer, and its time-dependent rheological characteristics were systematically analysed. Building upon the Herschel–Bulkley–Papanastasiou (HBP) viscosity model, a temporal variable was innovatively incorporated to extend the model into the time domain, resulting in the development of the Herschel–Bulkley–Papanastasiou–Wang (HBPW) constitutive viscosity model. Model parameters were determined through experimental data, and the accuracy of the HBPW model was rigorously validated by comparing numerical simulations with experimental results. The findings demonstrate that the HBPW model effectively captures the viscosity variation patterns of HTPB propellant slurry with respect to both shear rate and curing time, exhibiting a minimal discrepancy of 1.7525% between simulations and experimental data. This work establishes a novel theoretical framework for analysing the rheological properties of HTPB propellant slurry, providing a scientific foundation for optimised propellant formulation design and processing techniques. Full article

Arc scanning synthetic aperture radar (ArcSAR) can achieve high-resolution panoramic imaging and retrieve submillimeter-level deformation information. To monitor buildings in a city scenario, ArcSAR must be lightweight; have a high resolution, a mid-range (around a hundred meters), and low power consumption; and be cost-effective. In this study, a novel high-resolution wide-beam single-chip millimeter-wave (mmwave) ArcSAR system, together with an imaging algorithm, is presented. First, to handle the non-uniform azimuth sampling caused by motor motion, a high-accuracy angular coder is used in the system design. The coder can send the radar a hardware trigger signal when rotated to a specific angle so that uniform angular sampling can be achieved under the unstable rotation of the motor. Second, the ArcSAR’s maximum azimuth sampling angle that can avoid aliasing is deducted based on the Nyquist theorem. The mathematical relation supports the proposed ArcSAR system in acquiring data by setting the sampling angle interval. Third, the range cell migration (RCM) phenomenon is severe because mmwave radar has a wide azimuth beamwidth and a high frequency, and ArcSAR has a curved synthetic aperture. Therefore, the fourth-order RCM model based on the range-Doppler (RD) algorithm is interpreted with a uniform azimuth angle to suit the system and implemented. The proposed system uses the TI 6843 module as the radar sensor, and its azimuth beamwidth is 64°. The performance of the system and the corresponding imaging algorithm are thoroughly analyzed and validated via simulations and real data experiments. The output image covers a 360° and 180 m area at an azimuth resolution of 0.2°. The results show that the proposed system has good application prospects, and the design principles can support the improvement of current ArcSARs. Full article

Medical image segmentation, especially in chest X-ray (CXR) analysis, encounters substantial problems such as class imbalance, annotation inconsistencies, and the necessity for accurate pathological region identification. This research aims to improve the precision and clinical reliability of pulmonary abnormality segmentation by developing NCT-CXR, a framework that combines anatomically constrained data augmentation with expert-guided annotation refinement. NCT-CXR applies carefully calibrated discrete-angle rotations (±5°, ±10°) and intensity-based augmentations to enrich training data while preserving spatial and anatomical integrity. To address label noise in the NIH Chest X-ray dataset, we further introduce a clinically validated annotation refinement pipeline using the OncoDocAI platform, resulting in multi-label pixel-level segmentation masks for nine thoracic conditions. YOLOv8 was selected as the segmentation backbone due to its architectural efficiency, speed, and high spatial accuracy. Experimental results show that NCT-CXR significantly improves segmentation precision, especially for pneumothorax (0.829 and 0.804 for ±5° and ±10°, respectively). Non-parametric statistical testing (Kruskal–Wallis, H = 14.874, p = 0.0019) and post hoc Nemenyi analysis (p = 0.0138 and p = 0.0056) confirm the superiority of discrete-angle augmentation over mixed strategies. These findings underscore the importance of clinically constrained augmentation and high-quality annotation in building robust segmentation models. NCT-CXR offers a practical, high-performance solution for integrating deep learning into radiological workflows. Full article

Background: The segmentation of individual teeth in three-dimensional (3D) dental models is a key step in orthodontic computer-aided design systems. Traditional methods lack robustness when handling challenging cases such as missing or misaligned teeth. Objectives: to semantically segment maxillary teeth and palatal rugae in 3D textured scans using Convolutional Neural Networks (CNNs) and assess tooth movement after orthodontic treatment using stable rugae references. Methods: Building on the robustness of two-dimensional image semantic segmentation, we developed a method to convert 3D textured palate scans into two-dimensional images for segmentation, then back projected them onto the original 3D meshes. A dataset of 100 textured scans from 100 patients seeking orthodontic treatment was manually segmented by orthodontic experts. The proposed 3D segmentation method was applied to these scans. Finally, each pair of segmented 3D scans from the same patient, before and after treatment, was aligned by superimposing them on the stable rugae region. Results: The 3D segmentation method achieved an accuracy of 98.69% and an average Intersection over Union (IoU) of 84.5%. The common stable coordinate frame for both scans using the rugae area as a stable reference enabled the computation of the 3D translational and rotational motions of each maxillary tooth. Neither pre- nor post-processing of the data was required to enhance segmentation. Conclusions: The proposed method enabled successful motion measurement of teeth using the rugal area as a stable reference and providing rotation and translational measurements of the maxillary teeth. Full article

Concrete-like composites are widely used in the building of civil engineering applications such as houses, dams, and roads. Mesoscale modeling is a powerful tool for the physical and mechanical analysis of concrete-like composites. A novel discrete element method (DEM)-enhanced external force-free method for multi-phase concrete-like composite modeling with an interface transition zone (ITZ) is presented in this paper. Firstly, randomly distributed particles with arbitrary shapes are generated based on a grading curve. Then, a Minkowski sum operation for particles is implemented to control the minimum gap between adjacent particles. Secondly, a transition from particles to clumps is realized using the overlapping discrete element cluster (ODEC) method and is randomly placed into a specific space. Thirdly, the DEM simulation with a simple linear contact model is employed to separate the overlapped clumps. Meanwhile, the initial position, displacement, and rotation of clumps are recorded. Finally, the mesoscale model is reconstructed based on the displacement and rotation information. The results show that this method can efficiently generate multi-phase composites with arbitrary particle shapes, high volume fractions, an overlapped ITZ, and a periodic structure. This study proposes a novel, efficient tool for analyzing and designing composite materials in resilient civil infrastructure. Full article

Gears play an important role in modern machinery and are indispensable transmission components, particularly at high speeds, where lubrication is essential for the reliability and efficiency of the gear unit. In order to study the oil coverage law and heat dissipation mechanism of high-speed rotating meshing gears by injection angle, this paper adopts the moving particle semi-implicit method to establish a high-speed rotating gear lubrication model, study the intrinsic effect of different jet angles on gear lubrication, and build a gear lubrication bench for experimental verification. Numerical simulation found that with an increase in spray angle, the gear surface coverage and heat transfer coefficient of the high-speed rotating transmission gears initially increase and then decrease. They reflect the same lubrication law characteristics. When the injection angle was 90°, the surface coverage and heat transfer coefficient values were at their greatest, resulting in the best spray lubricating effect. According to the experimental results, under the conditions of 0.5 MPa injection pressure and high-speed rotation of the transmission gear with vertical injection, the lubricant covers the largest surface area of the gear and the least power loss. Simultaneously, in our previous study, we experimentally obtained the optimal parameter conditions on the basis of which we derived. The effect of nozzle diameter on jet lubrication was investigated in a previous study, and in this article, the effect of nozzle angle and distance on gear lubrication is investigated; the optimal conditions for high-speed lubrication of gears are the incident distance of 3.5 cm, incident angle of 90°, incident diameter of 1.5 mm, and gear speed of 2000 r/min, and the lubrication effect reaches the best ideal state; reduction in oil loss due to oil injection lubrication and power loss due to different parameters of the lubrication system. Lubrication design provides a theoretical foundation for the transmission system. Full article

The construction industry is transforming significantly, with emerging technologies reshaping project management by enhancing efficiency, sustainability, and safety. This study examines the integration of these innovations into Chad’s construction sector, drawing on insights from 79 industry participants. Given Chad’s unique economic and infrastructural landscape, understanding the practical implementation of these technologies is crucial. This research demonstrated strong reliability and validity through exploratory factor analysis, with a KMO value above 0.75, statistical significance at p < 0.001, and a Cronbach’s Alpha exceeding 0.8. Using Promax rotation, this study identified 15 key factors, providing valuable insights into how technologies such as Building Information Modeling (BIM), Artificial Intelligence (AI), the Internet of Things (IoT), and Digital Twin technology are transforming construction processes. These tools enhance design accuracy, facilitate real-time decision-making, and minimize material waste while supporting global sustainability goals, including the United Nations’ Sustainable Development Goals (SDGs). Examining the adoption of these technologies within Chad is particularly important, as the country faces unique challenges that demand tailored solutions. While digital transformation in the construction industry has been widely studied worldwide and in Africa, Chad’s industry remains relatively unexplored in this regard. This research bridges this gap by identifying both the opportunities and the barriers to technological integration in the sector. Embracing these innovations could help modernize Chad’s construction industry, addressing persistent inefficiencies and promoting environmental sustainability. However, widespread adoption is hindered by significant challenges, including high implementation costs, limited access to advanced tools, and a shortage of skilled professionals. Overcoming these obstacles will require strategic investments in education, infrastructure, and supportive policies. By fully leveraging technological advancements, Chad has the potential to build a more competitive, resilient, and sustainable construction industry, driving national development while aligning with global sustainability initiatives. Full article

Office buildings often consume a large amount of energy during their operational phase, primarily due to insufficient consideration of the coordination among energy consumption, thermal comfort, and visual comfort in the design process. This study employs a multi-objective genetic algorithm to optimize the overall performance of office buildings by parameterizing seven key design variables: floor plan aspect ratio, building orientation angle, window-to-wall ratios (WWRs) in all directions, shading strategy, shading device orientation, shading device length, and shading device spacing. A building performance simulation model was established to conduct a global optimization search, with simultaneous analysis across the east, south, west, and north façades to obtain a set of Pareto-optimal solutions that satisfy multiple performance objectives. The results indicate that optimal comprehensive performance across energy use, thermal comfort, and visual comfort can be achieved under the following conditions: a floor plan aspect ratio of 0.67–1, building rotation of 0–20° clockwise, an east-facing WWR of 0.4, south- and west-facing WWRs of 0.2–0.4, and a north-facing WWR of 0.4–0.6. For shading, horizontal devices with a length of 0.8–1.0 m, downward tilt angle of 10–30°, and spacing of 0.6–1.2 m are recommended. These findings provide scientific parameter references and optimization pathways for the design of high-performance office buildings in various climate conditions. Full article

Current-carrying coils and rotating permanent magnets can be used to create time-varying excitation magnetic fields for electrodynamic wireless power transmission (EWPT). Both types of transmitters produce low-frequency, time-varying fields at the locations of the receiver, but with fundamental differences. A coil transmitter produces a uniaxial magnetic field, where the direction of the field is along a single axis, but the amplitude varies in a bipolar fashion. In contrast, a rotating magnet transmitter produces a rotating magnetic field, with the amplitude varying in two orthogonal directions. Building on prior work for coil transmitters, this manuscript presents the modeling and a simulation framework for rotating magnet transmitters. The performance of an EWPT system is then studied both theoretically and experimentally for both transmitter types. For the same B-field amplitude (501 µT) and a fixed transmitter-receiver distance of 12 cm, a receiver driven by a coil transmitter produces 38 mW, whereas the same receiver driven by a rotating magnet transmitter produces 149 mW, nearly four times higher. This power increase is a result of 50% higher receiver rotation speeds using the rotating magnet transmitter. The power transfer efficiency is also six times higher for the rotating magnet transmitter. Full article

Remote sensing technology plays a crucial role across various sectors, such as meteorological monitoring, city planning, and natural resource exploration. A critical aspect of remote sensing image analysis is land target detection, which involves identifying and classifying land-based objects within satellite or aerial imagery. However, despite advancements in both traditional detection methods and deep-learning-based approaches, detecting land targets remains challenging, especially when dealing with small and rotated objects that are difficult to distinguish. To address these challenges, this study introduces an enhanced model, YOLOv5s-CACSD, which builds upon the YOLOv5s framework. Our model integrates the channel attention (CA) mechanism, CARAFE, and Shape-IoU to improve detection accuracy while employing depthwise separable convolution to reduce model complexity. The proposed architecture was evaluated systematically on the DOTAv1.0 dataset, and our results show that YOLOv5s-CACSD achieved a 91.0% mAP@0.5, marking a 2% improvement over the original YOLOv5s. Additionally, it reduced model parameters and computational complexity by 0.9 M and 2.9 GFLOPs, respectively. These results demonstrate the enhanced detection performance and efficiency of the YOLOv5s-CACSD model, making it suitable for practical applications in land target detection for remote sensing imagery. Full article

This paper extends the multipoint flux approximation (MPFA-O) method to model coupled pressure and saturation dynamics in subsurface reservoirs with heterogeneous anisotropic permeability and non-K-orthogonal grids. The MPFA method is widely used for reservoir simulation to address the limitations of the two-point flux approximation (TPFA), particularly in scenarios involving full-tensor permeability and strong anisotropy. However, the MPFA-O method is known to suffer from spurious oscillations and numerical instability, especially in high-anisotropy scenarios. Existing stability-enhancing techniques, such as optimal quadrature schemes and flux-splitting methods, mitigate these issues but are computationally expensive and do not always ensure monotonicity or oscillation-free solutions. Building upon prior advancements in the MPFA-O method for pressure equations, this work incorporates the saturation equation to enable the simulation of a coupled multiphase flow in porous media. A unified framework is developed to address stability challenges associated with the tight coupling of pressure and saturation fields while ensuring local conservation and accuracy in the presence of full-tensor permeability. The proposed method introduces stability-enhancing modifications, including a local rotation transformation, to mitigate spurious oscillations and preserve physical principles such as monotonicity and the maximum principle. Numerical experiments on heterogeneous, anisotropic domains with non-K-orthogonal grids validate the robustness and accuracy of the extended MPFA-O method. The results demonstrate improved stability and performance in capturing the complex interactions between pressure and saturation fields, offering a significant advancement in subsurface reservoir modeling. This work provides a reliable and efficient tool for simulating coupled flow and transport processes, with applications in CO₂ storage, hydrogen storage, geothermal energy, and hydrocarbon recovery. Full article