Current Issue : July-September Volume : 2025 Issue Number : 3 Articles : 5 Articles
Due to their construction efficiency, prefabricated concrete pavements are becoming a good choice for airport construction or refreshing. However, as a new type of pavement structure, their structural analysis theory and actual structural performance have not been determined. Therefore, a new method based on a neural network is applied to implement a long-term structural assessment, with the input being monitored strain data; it is named the jellyfish search algorithm-optimized BP neural network (JS-BP) model. Considering the structural characteristics, three key parameters are selected as the key parameters to implement the assessment, namely, the bending and tensile modulus, reaction modulus at top of the subgrade, and seam equivalent modulus. To implement the method, the databases are established first with the simulation results from some finite element models of prefabricated concrete pavement. Then, the proposed JS-BP neural network model is trained and checked with the established database. The simulation results verify an excellent accuracy of the proposed method as the difference between the predicted value and the true value is smaller than 1%. Moreover, the aircraft loads show some influence on the prediction results, in which the prediction error is about 5% for most cases, while it is up to 15% for assessing the top surface reaction modulus of the subgrade. Compared with the proposed JS-BP model, the accuracy of the traditional BP model is not so high, as the largest error can be up to 25%. Lastly, the proposed method is verified with some experiments using laboratory models. From the test results it is indicated that the prediction accuracy of the proposed method for the three parameters is still good enough, as the prediction error is within 5%....
The prompt and accurate detection of tunnel lining cracks is essential for maintaining the safety and reliability of tunnels. Deep learning-based approaches have significantly advanced automated crack detection, delivering improved efficiency and precision in tunnel inspection. Nevertheless, the intricate characteristics of cracks, manifesting as fine, elongated, and irregular structures, pose substantial challenges for deep learning-based semantic segmentation networks, hindering their ability to achieve comprehensive and accurate identification. Aiming to tackle these challenges, this paper proposes a novel dual-view snake Unet (DSUnet) model, which integrates a hybrid snake cascading (HSC) module and a Haar wavelet downsampling (HWD) operation. The HSC module enhances the network’s capability of extracting tunnel lining cracks by synergistically combining features derived from standard convolutions and bidirectional dynamic snake convolutions, thereby capturing intricate geometric and contextual information. Meanwhile, the HWD operation facilitates the preservation of critical spatial information by performing multi-scale feature refinement, which effectively reduces segmentation uncertainty. Experimental results demonstrate the proposed DSUnet achieves a mean Dice coefficient (MDice) of 71.8% and a mean intersection over union (MIoU) of 77.4%. Compared to the baseline Unet model, DSUnet delivers improvements of 1.3% in MDice and 0.6% in MIoU, respectively. Additionally, the proposed model consistently outperforms several state-of-the-art semantic segmentation networks, highlighting its robustness and accuracy in detecting tunnel lining cracks. These findings position DSUnet as a promising tool for automated tunnel inspection, contributing to improved safety and operational reliability....
Ultra-high-performance fiber-reinforced concrete (UHPFRC) has the characteristics of high strength, toughness, and excellent crack resistance. In order to fully utilize the high-strength properties of UHPFRC and reduce the structural weight and construction cost, solid slabs can be fabricated into hollow-core slabs or composite sandwich slabs. In order to further analyze the mechanical properties and mechanism of action of UHPFRC hollow-core slabs, one solid slab and two hollow-core slabs with the same geometric dimensions, reinforcement, and steel fiber volume content are designed in this paper, and their stress performance under a static load was investigated using a four-point bending test. The research results show that the UHPFRC hollow-core slab is anisotropic, and the bending stiffness of the section with parallel, distributed tubes is slightly smaller than that of the solid slab. The addition of steel fibers can greatly limit the development of cracks on a slab surface, so the elastic limit of a UHPFRC hollow slab is higher than that of a conventional concrete hollow slab. The whole bending process is roughly divided into the elastic stage, the elastic–plastic stage, and the plastic stage; the crack development process on the bottom of the slab can be classified into the cracking stage, the stable crack development stage, and the rapid propagation stage. In the elastic stage, the cross-sectional deformation of the UHPFRC hollow-core slab in the bending process still satisfies the assumption of a flat section. A row of parallel, round tubes on the neutral axis has a little effect on the cracking load, bearing capacity, and deformation capacity of the UHPFRC slab. By conducting the comparative analysis of the hollow rate and bearing capacity, when the hollow rate reaches 13.57%, the comprehensive weight of the solid slab is reduced by 13.16%, the cracking moment is slightly reduced, and the ultimate load is only reduced by 8.78%. Under the premise of meeting the bearing capacity, the hollow rate of the UHPFRC hollow-core slab can be appropriately increased to save money and energy....
In this study, we analyzed novel internally reinforced hollow-box beams to evaluate their strength using the finite element method (FEM) in ANSYS Mechanical APDL 18.1. Twelve different FEM models were subjected to static bending loads, and their performance was assessed based on Huber–Mises equivalent strength values. The results show that most optimized models exhibited improved strength compared to their initial versions, with some configurations achieving up to a 470% increase. These findings highlight the effectiveness of structural optimization in enhancing the strength behavior of hollow-box beams, providing valuable insights for engineering applications....
Monitoring the behavior of structures over time is a defining activity for any type of construction that provides data necessary to assess whether these structures meet requirements for stability and durability. In today’s rapidly urbanizing world, it is essential to monitor construction projects. Whether monitoring the impact on buildings surrounded by new constructions, underground infrastructure, or high-rise structure projects, the solutions and results provided by the construction monitoring process, carried out during the execution phase and/or operational stage, enable communities to progress and thrive without jeopardizing people and assets. This study aimed to highlight the level of interest in structural monitoring activities. A bibliometric analysis based on scientific articles published in the most popular databases brings to the forefront correlations and links between various fields of activity and the domain of construction monitoring through the application of various technologies. These published studies and the centralization of the number of searches for specialized terms related to structural health monitoring activities present a combination of classical theories with modern technologies that have evolved rapidly due to the continuous development of civil and geodetic engineering technology, as well as the introduction of artificial intelligence in interpreting recorded observations. This research results show that this topic is relevant and increasingly studied; for example, the number of scientific articles published on this subject doubled in the last three years compared to previous years. According to the literature, research trends are focused on new technologies, including the application of various sensor types, UAV technology, and LiDAR. The number of publications showed an increased interest in the study, monitoring, and evaluation of bridges, followed by research on civil constructions. Among civil constructions, aging or special buildings were most frequently encountered, while new structures accounted for a smaller percentage according to scientific articles published in the specialized literature....
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