Search

The paper discusses the structural behaviour of concrete-filled steel tube columns (CFT) when applied to the top-down construction method as temporary internal supports for ceilings. Their ultimate capacity to take into account the actual boundary conditions of the column is essential for successful application in construction. The paper presents the full-scale in situ testing of four slender specimens with variable D/t ratios under concentric axial loading. The CFT columns were supported on the previously jacked concrete piles. In addition, detailed finite element numerical models in ABAQUS and PLAXIS computer programs were developed. The models include the nonlinear behaviour of materials and the nonlinear behaviour of soil. The soil–pile–column interaction and impact of the CFT column–pile connection stiffness on global column stability were considered. The numerical model was validated by comparison with the experimental results. In conclusion, the coefficient for the effective buckling length of the studied columns is proposed. Finally, the experimental results of the critical buckling forces were compared with widely used international design codes Eurocode 4-EC4, American standard-ACI and the Australian standard-AS.

Due to numerous advantages, concrete-filled steel tubular (CFST) columns have an increasingly important role in the civil engineering industry. Because of the expensive experimental testing of these members, it is beneficial to provide prognostic models. In this study, an artificial neural network (ANN) model for predicting the axial compressive strength of square CFST columns has been developed. A dataset of 1022 samples (685 stub columns and 337 slender columns) was collected from available literature in order to compare the accuracy of the fast predictive Levenberg-Marquardt algorithm (LM) and Eurocode 4 (EC4) design code. Analyses showed that the ANN model has better accuracy than EC4. Over a whole domain, the ANN model has higher coefficient of determination (R2), and lower root mean squared error (RMSE). The same conclusion is valid when two separate datasets are considered: one for stub columns and the other for slender columns. The benefit of the ANN model is its applicability in a broader range of column parameters. At the same time, EC4 puts several limitations on its use and gives satisfactory results only in limited circumstances. Empirical equations have also been proposed from the best ANN model, which is useful for engineering practice.

The composite concrete-filled steel tube columns are structural members with numerous advantages over the traditional reinforced concrete or the pure steel members. The behavior of these columns is highly nonlinear. This paper analyses the determination of the ultimate compressive capacity of circular concrete-filled tubes according to the Eurocode 4 design code and its accuracy in comparison with the experimentally available results from the literature. In order to improve the predictions of the column’s ultimate compressive capacity, two efficient machine learning algorithms are employed separately for the stub and the slender columns. The analyzed algorithms are the Decision tree and the Random forest. The research used an experimental dataset of 508 samples: 236 tests on the stub columns and 272 tests on the slender columns. It adapted the dataset to the provision of the Eurocode 4 design code. The predictions of the column’s ultimate axial capacity obtained by two ML algorithms and the Eurocode 4 are compared with the experimental test results on the validation dataset. The calculated R2 error measure has shown that the predictions obtained by the tree-based algorithms are superior compared to the design formulas offered by the Eurocode 4. The decision tree algorithm achieved the best accuracy measured with the highest value of R2 error measure between the two algorithms. The two analyzed algorithms are used to perform the sensitivity analysis on the considered problem. The sensitivity analysis used the feature importance, a technique that assigns a score to each of the input features based on their influence on the prediction of the output variable. As expected, the sensitivity analysis identified the outer diameter of the columns’ cross-section as the parameter with the most significant impact on the results.

The paper presents the numerical modelling of concrete-filled steel tube stub columns under axial compression. The nonlinear 3D finite element model for the analysis was developed using ABAQUS software. The modelling, boundary conditions, the constitutive models for steel and concrete and contact interaction between the steel tube and concrete surface are discussed in detail. Three numerical models are proposed and validated using collected experimental data results. Finally, the composite column axial capacity is compared with the Eurocode 4 calculations for composite columns.

The paper proposes an adaptive fiber section discretization scheme for inelastic frame elements. The scheme uses cubature rules for the efficient and accurate evaluation of the section response over the elastic portion of the section. As inelastic strains arise and penetrate into the section from the edges, the scheme converts the area under inelastic strains to a regular fiber discretization. This approach offers considerable advantages for the computational efficiency of large structural models with inelastic frame elements by minimizing the number of integration points in sections with limited inelastic response. The proposed scheme is presented for circular and rectangular cross sections, but the approach is applicable to other section shapes. Inelastic frame response examples demonstrate the benefits of the proposed discretization scheme for the nonlinear response history analysis of large structural models.

This paper proposes the use of artificial neural network (ANN) algorithms to estimate the ultimate compressive strength of slender circular concrete-filled steel tubular (CCFST) columns. A dataset of 1051 samples was applied to generate an appropriate ANN prognostic model. Empirical equations were also developed from the best neural network, and their results were compared with those obtained by Eurocode 4 (EC4) design code. Analyses show that the proposed ANN model has a better agreement with experimental results than those created with provisions of the EC4 design code.