Tao Wang

Sky corridors are widely adopted in modern high‐rise connected buildings to facilitate pedestrian circulation and enable collaborative resistance to horizontal loads. However, rigidly connected sky corridors exhibit poor seismic resilience and are prone to severe damage under strong earthquakes, as evidenced by the failure of several high‐rise structures in Bangkok during the 2025 Myanmar earthquake. To address this issue, this paper proposes a two‐stage friction‐based energy‐dissipating resilient joint for steel truss connections, which satisfies bearing capacity requirements while suppressing irreversible damage such as member buckling and weld fracture under excessive loading. Three connection configurations, namely the conventional connection (CC), a connection with a diagonal friction damper (FC), and the proposed two‐stage friction connection (TFC), were tested under cyclic loading. Experimental results show that both CC and FC suffered significant diagonal web and chord buckling, weld fracture, and web tearing, whereas TFC exhibited stable ductile behavior, excellent energy dissipation capacity, and no visible structural damage. Stress‐based analysis reveals an effective load transfer path through the chords to the main steel frame. Finally, a simplified theoretical model for predicting the connection resistance is developed and validated, with errors less than 11%.

This paper proposes a convolutional neural network (CNN)-based image recognition method for crack detection in masonry walls to improve accuracy and efficiency. A sample database is constructed using laboratory-captured images of cracked masonry walls, augmented through rotation, mirroring, gamma correction, contrast adjustment, and brightness modification. A dual-scale CNN architecture is designed by integrating the characteristics of VGG-16 and GoogleNet, enabling multi-scale feature extraction for load-bearing masonry wall images. The network adopts two input scales: a modified VGG-16 branch with 100 × 100 inputs and an optimized GoogleNet branch with 50 × 50 inputs. Both branches undergo feature extraction and classifier training. Leveraging transfer learning, the model is pre-trained on the large-scale ImageNet dataset and fine-tuned using our self-built masonry wall database. Multi-scale features from dual branches are weighted and fused to generate final crack recognition results. Experimental results demonstrate a training accuracy of 94.33%, precision of 94.78%, and F1-score of 94.68%, outperforming traditional CNN models. The proposed method achieves high recognition accuracy for masonry load-bearing wall cracks and provides reliable technical support for practical engineering applications.

Nonstructural components are instrumental in maintaining the post‐earthquake functionality of buildings. However, many such components behave like freestanding rigid blocks that are highly susceptible to overturning under pulse‐like ground motions, posing the risks of economic losses and casualties. With the growing demand for seismically resilient buildings, the protection of nonstructural components has become a critical concern. Previous studies have primarily focused on translational excitations acting on freestanding blocks, and the influence of high‐rise and multi‐dimensional isolated structures, which exhibit pronounced rotational effects under seismic excitations, on the freestanding blocks has rarely been reported. To address this issue, this study investigated the influence of structural rotation on the overturning of freestanding blocks. Finite element simulations were conducted to quantify the contribution of such rotations; this was followed by the development of a dynamic model of the rocking bodies by incorporating structural rotation. The model was validated using shaking table tests and numerical simulations. Finally, the overturning acceleration spectra under one‐sine pulse excitation were derived. The results indicate that structural rotation increases the overturning limits of a block in the low‐frequency regime. Conversely, at larger frequency ratios ω _p / p , it renders the blocks more prone to overturning.

The spatial variability of ground motion (SVGM) has been shown to induce additional torsion and amplify seismic responses in nuclear structural analyses, necessitating the incorporation of SVGM into design and assessment. However, dense long‐term strong motion arrays are rarely available at specific nuclear power plant (NPP) sites, and coherence functions statistically derived from other arrays lack transferability. Therefore, an engineering approach for site‐specific coherence function is required. This study develops a random field finite element model for a 2D site using a multi‐scale random sampling technique: a coarse‐scale standard normal field is first generated, its statistical characteristics are mapped to fine‐scale elements, and an inverse of the cumulative distribution function (CDF) is applied to achieve the target distributions. Time domain site response analyses are then performed for multiple model samples of a hypothetical site; cross power spectral densities computed from the simulated records are normalized to yield ensemble‐averaged coherence functions. The proposed multi‐scale random field model balances sampling accuracy and efficiency and effectively reproduces heterogeneous site features. For a hypothetical pulse‐type incident wave site, the coherence functions in the horizontal and vertical directions are estimated. The coherence functions for different station spacings show that the coherence decreases with increasing spacing and frequency, with local peaks close to the resonant frequencies. The workflow provides an operational method for constructing site‐specific coherence functions for NPP sites in the absence of array data, laying the technical foundation for incorporating ground‐motion incoherence into seismic analyses of NPP structures at complex sites.

This study aims to enhance understanding of seismic damage patterns of RC wall structural system (RCW) by monitoring the mechanical behavior of coupling beams. The coupling beam, configured with elastic steel truss and friction dampers, is able to consume massive energy during earthquakes, and continuously get the complete histories of sustained forces and deformations by measuring strains of truss diagonal web members and the sliding of friction dampers, respectively. The histories of sliding and force are useful to comprehensively understand the status of dampers and the lateral deformation of coupled wall systems, which further determine the seismic damage state of the overall structure and whether the dampers shall be replaced after an earthquake. In this paper, a series of quasi‐static tests were first performed on the proposed coupling beams. The results from the tests indicated that the force contribution of truss chord members gradually increased in a nonlinear pattern with respect to the deformation of coupling beam. This was attributed to the axial force sustained by the coupling beam. Then a theoretical model was proposed to build up the relationship between the strain of diagonal web members and the sustained shear force. The coefficient was given by the equation derived from the simplified model, which provides a prediction with the largest error of 10%. Subsequently, the measured sliding displacements of friction dampers were correlated to the lateral deformation of RC walls considering the bending, shearing, and axial compression of coupled walls. This relationship was examined by a quasi‐static test on a three‐story coupled wall substructure, and acceptable prediction results can be obtained. These correlations make it possible to assess the damage state of the overall building. The findings of this study not only contribute to the development of a more accurate theoretical model for coupling beams but also offer a practical monitoring technique.