Lichen Wang

The new crossed cable-truss spoke structure (CCTSS) significantly improves the lateral stiffness and integral stability of the ordinary spoke cable-truss structure, but it still has the shortcomings of general tensile structures, like low redundancy and weak collapse resistance. Its collapse resistance is still unclear. In the paper, the structural characteristics of CCTSS are introduced. Secondly, the influence of initial prestresses on the collapse performance of CCTSS is studied. Then the collapse response features and collapse mechanism of the members and joints of CCTSS are revealed under the actions of no loads, full-span loads and half-span loads. Finally, a calculation method of the dynamic force amplification coefficient is proposed based on the collapse results of CCTSS, and a calculation method of the importance of members and joints is further proposed based on the dynamic internal force amplification coefficient, which indirectly evaluates structural collapse resistance. The results show that CCTSS has good local collapse resistance, but the failure of ring cables and joints at the ring cables will cause the structure to lose its integral bearing capacity. Meanwhile, the proposed calculation method of the importance of components and joints has a simple calculation process and is convenient to utilize, which has good engineering application value. The research content provides a theoretical basis and analysis method for structural safety design.

With the development of artificial intelligence, intelligent assessment methods have been applied in post-earthquake emergency rescue. These methods enable rapid and accurate identification and localization of earthquake-induced damage to ceilings in large public buildings, which often serve as emergency shelters. However, in practical applications, challenges remain: damage recognition accuracy is low when using wide-field distant shots, while close-up local shots are unsuitable for identifying panoramic regional damage. As a result, high-precision intelligent safety assessment of the entire ceiling area cannot be achieved. Therefore, this study proposes a panoramic image stitching method based on SIFT feature point detection and registration, optimized by the RANSAC algorithm, to generate high-resolution, wide-angle panoramic images of ceilings in large public buildings. The BRISQUE values of the stitched images range between 20 and 30, indicating good stitching quality. Subsequently, by integrating damage recognition and image stitching techniques, a safety assessment test was conducted on 227 stitched images of earthquake-induced ceiling damage captured in real scenes, using evaluation indicators such as damage type and severity quantification. The safety assessment achieved an overall accuracy of 98.7%, demonstrating the effectiveness of ceiling damage detection technology based on image stitching. This technology enables intelligent post-earthquake safety assessment of ceilings in large public buildings across the entire area.

Carbon-fiber-reinforced polymer (CFRP) tendons have become a viable alternative to steel cables in cable roof structures owing to their high tensile strength, low weight, and resistance to corrosion. However, the effective anchoring of CFRP tendons is a challenge because of their poor transverse mechanical properties. Therefore, the mechanical properties of CFRP tendons and a tendon–wedge assembly under transverse compression were investigated by simulating the force environment of the CFRP tendon inside an integrated-wedge anchorage. The deformation of and local damage to CFRP tendons under transverse compression were explored using load–strain curves and full-field strain measured using digital image correlation. The experimental and numerical results show that large-diameter CFRP tendons with a length in the range of 90–110 mm had better cross-sectional deformation resistance and more stable transverse mechanical properties. Longer CFRP tendons with larger diameters have lower contact compressive stress and local maximal shear stress under the same transverse compressive load. Based on the analysis of the experimental and numerical results, we propose design suggestions for tendon size selection and integrated-wedge design details, such as the manufacturing materials of the wedge, the radius through the gap of the wedge, and the radial difference of the groove, to improve the anchoring properties and efficiency of the integrated-wedge anchorage.

To alleviate the workload in prevailing expert-based onsite inspection, a vision-based method using state-of-the-art deep learning architectures is proposed to automatically detect ceiling damage in large-span structures. The dataset consists of 914 images collected by the Kawaguchi Lab since 1995 with over 7000 learnable damages in the ceilings and is categorized into four typical damage forms (peelings, cracks, distortions, and fall-offs). Twelve detection models are established, trained, and compared by variable hyperparameter analysis. The best performing model reaches a mean average precision (mAP) of 75.28%, which is considerably high for object detection. A comparative study indicates that the model is generally robust to the challenges in ceiling damage detection, including partial occlusion by visual obstructions, the extremely varied aspect ratios, small object detection, and multi-object detection. Another comparative study in the F1 score performance, which combines the precision and recall in to one single metric, shows that the model outperforms the CNN (convolutional neural networks) model using the Saliency-MAP method in our previous research to a remarkable extent. In the case of a large-area ratio with a non-ceiling region, the F1 score of these two models are 0.83 and 0.28, respectively. The findings of this study push automatic ceiling damage detection in large-span structures one step further.