Global Buckling Behavior of CFST Columns with Assembling Errors

Concrete-filled steel tubular (CFST) column has been widely used in engineering practice. In the process of assembling two columns to form a slender member, assembling errors (AE) are inevitably produced at the section of connection. When the AE are too large, the global buckling resistance of slender column would be significantly affected. Therefore, it is necessary to investigate the influence of AE on the stability performance of slender CFST columns. In this study, an axial compressive test involving three CFST columns with AE (AE-CFST columns) was conducted. A refined finite element (FE) model is established for further parametric analysis. Based on a simplified analytical model by analyzing the isolated steel connecting plate, a theoretical formula is proposed for predicting the critical thickness [Formula: see text] of the connecting plate. When the thickness [Formula: see text] of the connecting plate meets its requirement, the failure at the section of connection caused by AE could be effectively prevented. Stability design curves considering the influence of AE ratio (the ratio between assembling error and sectional depth of column) are proposed based on numerous FE examples. It is found that the proposed design curves are reliable for the design of AE-CFST columns with different AE ratios.

Eccentric compression behaviour of rectangular concrete-filled steel tube columns with self-compacting lower expansion concrete

The use of a self-compacting lower expansion concrete in a concrete-filled steel tube (CFST) structure not only promotes the quality of concrete pouring but also improves the bond behaviour between the steel and the concrete. In combination with the actual stress state of the columns in the engineering structure, it is necessary to study the eccentric compression behaviour of the column. In this study, experimental studies involving both uniaxial and biaxial bending tests of rectangular self-compacting lower expansion CFST columns were carried out. The variation laws of the load–displacement curves, the lateral deflection curves and the stress–strain curves during the loading phase were analysed. Furthermore, the failure modes and the mechanical properties of the specimens under eccentric compression loads were investigated. Subsequently, the numerical models of CFST columns with self-compacting lower expansion concrete were considered and established. In order to verify the rationality of the finite element modelling, the numerical calculation results were compared with test results. Then, a parametric analysis of the compression and the bending bearing capacities of each column was carried out by changing the eccentricity of the load, and the N–M curves or N-Mx-My surfaces describing the ultimate bearing capacity of the column were obtained. Finally, by the parametric finite element analysis of the rectangular CFST columns regarding to the bearing capacity under the same eccentricity, a conclusion was obtained: when the expansion agent content γ of a specimen increased from 0% to 10%, the bearing capacity of the columns increases significantly, but when continue increasing the expansive agent content, the expansion agent content has little effect on the compression–bending bearing capacity.

Seismic Behavior of Elliptical Concrete-Filled Steel Tubular Columns under Combined Axial Compression and Cyclic Lateral Loading

Elliptical concrete-filled steel tubular (CFST) column is a new form of CFST columns, consisting of an outer elliptical tube filled with concrete. Although the study on mechanical performance of the elliptical CFST members is receiving more and more attention, they have been limited to static behavior. Against this background, an experimental study on elliptical CFST columns was carried out under combined axial compression and cyclic lateral loading. The failure modes, hysteretic curves, skeleton curves, load carrying capacity, deformability, stiffness degradation, and energy dissipation ability was obtained and discussed. The test results indicated that the elliptical CFST columns possess excellent seismic performance and ductility. Valuable experimental data were provided for the formulation of the theoretical hysteresis model of the elliptical CFST columns.

Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence

The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Gene Expression Program (GEP). The database for this study contains 1667 datapoints in which 702 are short CFST columns and 965 are long CFST columns. The input parameters are the geometric dimensions of the structural elements of the column and the mechanical properties of materials. The target parameters are the bearing capacity of columns, which determines their life cycle. A Multiphysics model was developed, and various statistical checks were applied using the three artificial intelligence techniques mentioned above. Parametric and sensitivity analyses were also performed on both short and long GEP models. The overall performance of the GEP model was better than the ANN and ANFIS models, and the prediction values of the GEP model were near actual values. The PI of the predicted Nst by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for Nlg these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding OF values are 0.2300, 0.1200, and 0.090 for Nst, and 0.1000, 0.2700, and 0.1500 for Nlg. The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on practical experimental work and does not rely on prior solutions. It is concluded that the GEP model can be used to predict the bearing capacity of circular CFST columns to avoid any laborious and time-consuming experimental work. It is also recommended that further research should be performed on the data to develop a prediction equation using other techniques such as Random Forest Regression and Multi Expression Program.