Suizi Jia

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%).

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.

Aluminum alloy tube–concrete composite columns have received increasing attention owing to their high strength-to-weight ratio and superior corrosion resistance compared with conventional steel–concrete composite columns. In this study, a refined finite element model is established to investigate the axial compression behavior of aluminum alloy tube–concrete long columns. The results indicate that the axial bearing capacity and deformation characteristics are strongly governed by the confinement effect provided by the aluminum alloy tube, which varies significantly with different cross-sectional configurations. Circular and square aluminum alloy tubes exhibit distinct confinement mechanisms, leading to different stress distributions and damage evolution patterns in the core concrete. Enhanced confinement effectively improves the utilization of concrete strength and delays local buckling of the aluminum alloy tube, thereby contributing to an increase in axial bearing capacity. Furthermore, parametric analyses clarify the combined influence of material properties and geometric parameters on the confinement efficiency and overall axial compression performance of the composite columns.

The land section of the Huangdao end of the Jiaozhou Bay Second Submarine Tunnel is extensively underlain by the Quaternary Loose Accumulation Layer. The tunnel passes through a weathered granite fracture zone with well-developed rock joints beneath the buildings. The tunnel excavation process significantly disturbs the buildings above, making them prone to settlement, cracking, and tilting. This research conducts numerical simulations of three tunnel excavation methods, and based on the results, compares the deformation behaviors of the ground surface and buildings under various conditions. The findings show that the double side-wall guide pit method has better adaptability in controlling surface settlement and building deformation than the vertical or curved CD method. Moreover, the removal of temporary supports significantly affects building settlement and tilt; this risk can be effectively reduced by controlling the stress relief ratio during the removal phase of the temporary supports in the tunnel. The significance of the study lies in the fact that by choosing an appropriate tunnel excavation support scheme, the disturbance impact on the overlying buildings can be minimized, and the construction safety and stability of the surrounding buildings can be guaranteed. The results of this study can provide initial guidance for constructing shallow-buried tunnels beneath existing buildings.

Effective temperature control measures are crucial for achieving temperature regulation and preventing cracking in the dam body during the construction of large-volume concrete gravity dams. Due to the low ambient temperatures in winter, it is especially important to focus on temperature control measures for concrete dam construction in cold regions. This paper employs a numerical simulation method that takes into account dam temperature control measures to simulate and predict the overall temperature and stress fields of the Guanmenzuizi Reservoir Dam, and validates these simulations with field monitoring results. This study finds that the ambient temperature significantly impacts the temperature and stress of the dam body’s concrete. The internal temperature of the dam reaches its highest value approximately 7 days after pouring, followed by periodic fluctuations, with the dam body’s temperature changes exhibiting a certain lag compared to the ambient temperature. The interior of the dam is under compression, while the upstream and downstream surfaces experience significant tensile stress. This project adopts targeted temperature control measures for the cold environmental conditions of the region, which are reasonably implemented and effectively reduce the temperature rise of the concrete during construction, achieving the temperature control objectives. This study also explores the impact of the cooling water pipe density on the dam body. The research results offer valuable references for the implementation of temperature control measures and the establishment of temperature control standards for concrete gravity dams in cold regions.

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.

The research on free surface flow is of great interest in fluid mechanics, with the primary task being the tracking and description of the motion of free surfaces. The development of numerical simulation techniques has led to the application of new methods in the study of free surface flow problems. One such method is the Neural Particle Method (NPM), a meshless approach for solving incompressible free surface flow. This method is built on a Physics-Informed Neural Network (PINN), which allows for training and solving based solely on initial and boundary conditions. Although the NPM is effective in dealing with free surface flow problems, it faces challenges in simulating more complex scenarios due to the lack of additional surface recognition algorithms. In this paper, we propose an improved Neural Particle Method (INPM) to better simulate complex free surface flow. Our approach involves incorporating alpha-shape technology to track and recognize the fluid boundary, with boundary conditions updated constantly during operation. We demonstrate the effectiveness of our proposed method through three numerical examples with different boundary conditions. The result shows that: (1) the addition of a surface recognition module allows for the accurate tracking and recognition of the fluid boundary, enabling more precise imposition of boundary conditions in complex situations; (2) INPM can accurately identify the surface and calculate even when particles are unevenly distributed. Compared with traditional meshless methods, INPM offers a better solution for dealing with complex free surface flow problems that involve random particle distribution. Our proposed method can improve the accuracy and stability of numerical simulations for free surface flow problems.