Establishing a restoring force model is a fundamental yet critical task for the analysis of structural responses to earthquakes. Such a model has a substantial impact on the structural analysis results. Composite-shear walls with concealed bracings in steel-tube frames (composite-shear walls) offer several advantages, including convenient construction processes, high-bearing capacity, and excellent ductility. In this study, the mechanical properties of virtual test pieces were simulated when subjected to low-cyclic-reversed loading in ABAQUS. The simulation results agreed well with the experimental results. Subsequently, 24 additional virtual test pieces were obtained by adjusting the parameters of the original four test pieces, including the strength of the recycled concrete, thickness of the wallboard, and axial compression ratio. Finally, the restoring force model was validated using the experimental test results from our previous study. The results demonstrate the excellent performance of the proposed restoring force model in simulating the mechanical response of a composite shear wall. In particular, this model can accurately reflect the restoring force characteristics of the composite shear wall explored in this study. The restoring force model provides an effective theoretical support for the analysis of the elastoplastic seismic response of similar types of structures.

The determination of the bearing capacity prediction model of concrete-filled steel tubular columns is a key issue in the structural design of prefabricated buildings, which directly relates to the stability and safety of prefabricated buildings. The purpose of this paper is to study the bearing capacity model of concrete-filled steel tubular columns, and propose an explicit formula based on the Gaussian process regression algorithm to calculate the bearing capacity. In order to solve the problem of low accuracy of the traditional empirical bearing capacity model, this paper first proposes a more accurate bearing capacity prediction model based on Gaussian process regression algorithm to automatically learn and capture the characteristics of 122 groups of test data; the paper then determines the function of high sensitivity parameters and section influence parameters through the established bearing capacity prediction model, and this process gives the display formula. Compared with the implicit formula given by a machine learning model, the explicit formula proposed in this paper is more suitable for practical engineering design. In order to verify the validity of the formula, we generated the bearing capacity data through the proposed formula based on the test data and used the descriptive statistical method to verify. The results show that the proposed formula is superior to other existing methods, the error between the data generated by the proposed formula and the test data is smaller, and its accuracy reaches 93.73%, which is more suitable for calculating the bearing capacity of concrete-filled steel tubes with different cross sections.

To improve the seismic performance of steel frame buildings in rural areas, an energy-dissipating composite wall (EDCW) assembled from concrete-filled steel tubular columns and concrete sheet walls was designed. Cyclic loading tests were simulated using the finite element method (FEM) to analyse the seismic performance of the EDCW. The reliability of numerical modelling and analysis was verified by comparing the hysteretic curves obtained by the finite element model with those obtained by previous experiments. The EDCW was designed for installation in a two-storey steel frame, and the FEM was used to determine the seismic performance of the steel frame, including the deformation and failure characteristics, hysteresis curves, and skeleton curves. The numerical simulation results showed that the EDCW dissipated most of the seismic energy and thus substantially improved the seismic performance of the frame. The seismic performances of 16 frames were compared to investigate the effects of the span ratio of the steel frame to the EDCW, the installation location of the EDCW, and the stiffness of the steel frame on the seismic performance of the frame.

Steel–concrete composite components exhibit significant advantages, including reliable mechanical performance, rapid construction, cost efficiency, and low environmental impact. Existing studies on their seismic behavior have mainly focused on developing novel connection forms and enhancing joint zone strength, while systematic investigations into the post-earthquake axial compression behavior and failure mechanisms of composite joints remain limited. To address this gap, this study investigates the mechanical performance of steel–concrete composite components under strong seismic and post-earthquake conditions. Seismic damage characteristics are first analyzed based on representative case studies of conventional steel–concrete columns. Subsequently, low-cycle reversed loading tests followed by post-earthquake axial compression tests are conducted on seven beam–column joints with varying damage levels, and the damage evolution and seismic performance of joint zones under different structural configurations are systematically evaluated. In addition, the seismic performance of steel–concrete composite shear walls is further validated. The results provide a scientific basis for the seismic design, post-earthquake assessment, and repair of steel–concrete composite structures.

As a structural material characterised by low density, high strength, excellent corrosion resistance and recyclability, aluminium alloy tubes are finding increasingly widespread application in the construction sector. However, there is currently a lack of research on the prediction of the bearing capacity of aluminium alloy square tube columns. To investigate the failure behaviour of aluminium alloy square tube columns under axial and eccentric compression, this paper first designed 10 thin-walled aluminium alloy square tube column specimens with varying lengths, cross-sectional dimensions and wall thicknesses. Axial and eccentric compression tests were conducted, and the loading process and failure modes were analysed. Building on this, a hybrid load-bearing capacity prediction model combining Multi-Objective Particle Swarm Optimisation (MOPSO) with the Gaussian process regression (GPR) algorithm was proposed. This model is capable of automatically learning and capturing 236 sets of experimental data. Subsequently, using the established prediction model, the contributions of high-sensitivity parameters and cross-sectional influence parameters to the load-bearing capacity were determined. Based on the prediction results, a correction factor for the diameter-to-thickness ratio was introduced into the eccentric compression bearing capacity formula of the Chinese code to establish an improved calculation formula. Compared with the implicit formula provided by machine learning models, the explicit formula proposed in this paper is more suitable for practical engineering design. The results show that the prediction results agree well with the experimental results and can accurately predict the ultimate bearing capacity of aluminium alloy square columns. Compared with the bearing capacity calculation methods in existing codes, the proposed formula reduces the root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R²) of the dataset by 70.91%, 70.85% and 64.27%, respectively, whilst increasing the coefficient of determination (R²) from 0.8107 to 0.9830 (a relative improvement of 21.25%).