Current Issue : July-September Volume : 2024 Issue Number : 3 Articles : 5 Articles
In this study, 18 short-column lightweight ceramsite concrete samples were prepared in rectangular stainless steel pipes, which were used for axial pressure performance tests that took the cross-sectional length–width ratio of the rectangular stainless steel pipe (1.0, 1.5 and 2.0), the wall thickness of the steel pipe (3 mm, 4 mm and 5 mm), and the strength grade of the filled concrete (C20 and C30) as the main parameters. Then, the failure patterns, axial load–displacement curve, axial load–strain curve, ultimate bearing capacity and the interaction between the steel pipe and concrete in the specimens were measured. The test results revealed that the short-column concrete specimens in the steel pipes exhibited typical shear failure and “waist-bulging” failure under axial compressive loads. In the elastic stage, the bearing capacity of the specimens was able to reach 65–85% of the ultimate bearing capacity, with the residual bearing capacity essentially reaching 70% of the ultimate bearing capacity. Furthermore, the ultimate bearing capacity of the specimens demonstrated an increase with the rise in the strength grade of the filled concrete, with the thickness of the stainless steel pipe and with the decrease in the length–width ratio of the steel pipe crosssection. The specimens exhibited a distinct hoop effect. As the length–width ratio decreased and the hoop coefficient increased, the ductility coefficient and the strength enhancement coefficient basically displayed an increasing tendency, while the concrete contribution ratio exhibited a decreasing trend....
The identification of the development of structural defects is an important part of bridge structure damage diagnosis, and cracks are considered the most typical and highly dangerous structural disease. However, existing deep learning-based methods are mostly aimed at the scene of concrete cracks, while they rarely focus on designing network architectures to improve the visionbased model performance from the perspective of unmanned aircraft system (UAS) inspection, which leads to a lack of specificity. Because of this, this study proposes a novel lightweight deep convolutional neural network-based crack pixel-level segmentation network for UAS-based inspection scenes. Firstly, the classical encoder-decoder architecture UNET is utilized as the base model for bridge structural crack identification, and the hourglass-shaped depthwise separable convolution is introduced to replace the traditional convolutional operation in the UNET model to reduce model parameters. Then, a kind of lightweight and efficient channel attention module is used to improve model feature fuzzy ability and segmentation accuracy. We conducted a series of experiments on bridge structural crack detection tasks by utilizing a long-span bridge as the research item. The experimental results show that the constructed method achieves an effective balance between reasoning accuracy and efficiency with the value of 97.62% precision, 97.23% recall, 97.42% accuracy, and 93.25% IOU on the bridge concrete crack datasets, which are significantly higher than those of other state-of-the-art baseline methods. It can be inferred that the application of hourglass-shaped depthseparable volumes can actively reduce basic model parameters. Moreover, the lightweight and efficient attention modules can achieve local cross-channel interaction without dimensionality reduction and improve the network segmentation performance....
Cables are important components of long-span bridge structures, whose operation is significantly affected by cable force changes. Nowadays, cable force testing is performed by physical methods; that is, sensors are installed on the cable structure to monitor its force changes. Obviously, this strategy requires an extensive amount of time to achieve cable force calculation, which makes it impossible to monitor the force of the cable structure in real time. Meanwhile, smartphones have attracted extensive attention in the field of structural health monitoring (SHM) because of their higher cost-effectiveness than accelerometers, which include price and lifespan. Besides, many people own a smartphone, which leads to the possibility of a wider range of applications. Therefore, this paper presents a framework for the rapid estimation of the cable force of long-span bridges based on smartphones-captured video and a template matching algorithm. First, the empirical mode decomposition (EMD) method with wavelet decomposition (WD) method, that is, the EMDWD model, is constructed to extract the vibration signal of the bridge cable by eliminating the effects of smartphone vibration and environmental noise on the measured dynamic displacement, thus effectively improving the accuracy of data processing. In addition, the vibration identification model of bridge cable based on a template matching algorithm is established, and the deformation curve of cable is obtained. Finally, the frequency of bridge suspender is calculated by the Fourier transform method (FFT), and the cable force is estimated based on the smartphone-captured video....
Receiving inspection plays a crucial role in ensuring construction quality after the completion of engineering projects. Traditional inspection measurement methods, such as manual observation means and optical equipment measurement methods, have limitations in terms of the number of measurement sites and the range of measurements. These traditional methods fail to provide accurate curve parameters and continuous spatial morphology information for large-span curved bridge structures. This paper proposes a reverse model measurement method to address this issue. The reverse model is built based on point cloud data acquired by 3D laser scanning technology. Finally, take the Taizicheng No. 1 Bridge as an example, the validity of the proposed method is verified....
The construction industry has been trying to enhance the level of digitalization and autonomy by adopting various communication and information technologies (ICT), e.g., augmented reality (AR), virtual reality (VR), robotics, drones, or building information modeling (BIM). However, improvement of the safety and productivity in their domains is still a struggle. One of the main reasons for failing to accelerate their digital transformation is ignoring the deep understanding of the concept of digital twin, its usage, and the potential benefits of digital twins in the construction industry. Therefore, this paper investigated the impacts and potentials of digital twins on the construction industry through a quantitative systematic review assisted by the text mining method. The study presented the potential usability of digital twins, leading and core technologies, and applications, revealing their benefits and potential for optimizing project planning, execution, and management process. Through this comprehensive literature review, this study elucidated the distinctive features, advantages, and immense potential that digital twins bring to the construction field. The findings highlight the transformative impact of digital twins, providing critical insights for their broader adoption and groundbreaking applications in the industry. By addressing the challenges of adopting this technology, the article provided valuable insights for advancing research and the broad implementation of digital twins in the sector....
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