Steel open-web sandwich floors (SOSFs) have been applied in several pioneering industrial and public buildings. The traditional square tube shear key (STSK) is changed into a flanged cruciform section shear key (FCSK) to further improve the bearing capacity of SOSFs. A half-scale test model is designed. A graded static loading test is performed to measure deflection and strain. The calculation formula of the intersection beam analogy method (IBAM) considering the influence of shear stiffness is proposed. A finite element (FE) analysis was also performed. The results of the test, IBAM and FE method are compared. The maximum deviation of the deflection obtained by them is less than 5%, and the maximum strain error is less than 7%. These figures prove that all three methods have high precision in the elastic range. Compared SOSFs use a different shear keys with the same grid size and chords, the maximum Mises stress is reduced by 55.9% and the maximum displacement is reduced by 36.1%. The new shear key imparts the structure with improved bearing capacity and safety margin.

The gymnasium of Fule International School in Mianyang, Sichuan are multi-storey large-span buildings with a maximum span of 36 m and use a new system-steel grid cassettle structure. In this paper, the structure form, prefabricated construction method and support unloading process of the gymnasium are introduced in detail. To ensure smooth construction unloading, the deflection deformation of the large span floor and the field splicing node are monitored. Results show that the prefabricated construction method of the Mianyang Fule International School Gymnasium and the steel column-scaffold combination support system are affordable. After unloading, the floor has the traits of plates and relatively greater rigid reserve. Thus, it is applicable for large-span and high-load floor structure. The stress in the splicing node in the inflection point (L/2, L is the size of the grid) is relatively small, the bolt and the shearing-resistance plate have no slump, and the field splicing node is safe and reliable. The findings provide a certain reference value for the research and innovation in the process of assembly construction of multi-storey and large-span building.

Steel smelting and production produces a large amount of exhaust gas, which is damaging to the environment. Prestressed spatial arch-supported partial single-layer reticulated shells (PSASPSRSs) are introduced to promote sustainable development in the construction industry. An optimization strategy based on uniform design experiments and iterations is proposed with respect to the design of PSASPSRSs. The optimization aims to reduce steel consumption as much as possible. The optimization constraint takes into account the stability coefficient, frequency, and deflection of the structures. The search space gradually shrinks around the local optimal solution and moves toward the global optimal solution during the optimization process. The optimization procedure stops when the error between local optimal solutions is less than the permitted error of 5%. The tensile force of the prestressed cable, the unified design stress ratio of the members, and the radial grid number of the single-layer reticulated shells act as optimization variables in the finite element model. The parametric analysis revealed that the radial grid number of single-layer reticulated shells significantly affected steel consumption, which was reduced by 13% in the optimized structure. The effectiveness and the practicality of the proposed optimization strategy in the initial design of complicated space grid structures are systematically illustrated.

To effectively adapt to seismic actions in complex earthquake environments and enhance the seismic isolation performance of building structures, this paper divides the disk spring group of traditional disk spring isolation bearings into an upper disk spring group and a lower disk spring group. By arranging different stacking layers, an improved Vertical Variable Stiffness Disk Spring Isolation (VVSDSI) bearing is proposed. The operational performance of this bearing is thoroughly investigated through a theoretical analysis, experimental testing, and numerical simulations. The research results demonstrate the following: (1) under ultimate load conditions, the working stages of the VVSDSI bearing can be divided into two phases. During the isolation phase, the vertical stiffness of the bearing is relatively low, with a value of 11.14 kN/mm, whereas in the protection phase, the vertical stiffness increases significantly to 35.34 kN/mm. (2) During the isolation phase, the preloading displacement exhibits a positive correlation with equivalent stiffness and an equivalent damping ratio, while the loading amplitude shows a negative correlation with these parameters. (3) The theoretical hysteresis model prediction value and numerical model simulation value of the VVSDSI bearing are in good agreement with the measured data, with the maximum errors of the peak load being 6.78% and 10.07%, respectively, which can accurately simulate the mechanical properties of the bearing. Therefore, this research result can provide a theoretical basis and experimental support for the design of vertical seismic isolation systems.

Structural damage detection is of significance for maintaining the structural health. Currently, data-driven deep learning approaches have emerged as a highly promising research field. However, little progress has been made in studying the relationship between the global and local information of structural response data. In this paper, we have presented an innovative Convolutional Enhancement and Graph Features Fusion in Transformer (CGsformer) network for structural damage detection. The proposed CGsformer network introduces an innovative approach for hierarchical learning from global to local information to extract acceleration response signal features for structural damage representation. The key advantage of this network is the integration of a graph convolutional network in the learning process, which enables the construction of a graph structure for global features. By incorporating node learning, the graph convolutional network filters out noise in the global features, thereby facilitating the extraction to more effective local features. In the verification based on the experimental data of four-story steel frame model experiment data and IASC-ASCE benchmark structure simulated data, the CGsformer network achieved damage identification accuracies of 92.44% and 96.71%, respectively. It surpassed the existing traditional damage detection methods based on deep learning. Notably, the model demonstrates good robustness under noisy conditions.