UHPC Infilled Circular steel Tube Column Design

Previous researchers studied the behavior of Ultra-High Performance Concrete (UHPC) columns confined with steel tubes. However, predicting the influence of the confinement effect and the compressive capacity of these columns has yet to be further examined. Currently, the Canadian design code limits for reinforced concrete do not reach the strength nor the strain produced by using UHPC. This project uses the Canadian design methods for a cross-section of UHPC to form a column interaction curve and compared it with six test specimens. The effects of steel tube confinement will also be examined. Additionally, the Eurocode 4 (EC4) method, which includes the strengths of UHPC and confinement of steel tube, was used to formulate another column interaction curve. The results show that the Canadian code severely underestimates the design strength of confined UHPC while the EC4 provides much more accurate results.

UHPC Infilled Circular steel Tube Column Design

Previous researchers studied the behavior of Ultra-High Performance Concrete (UHPC) columns confined with steel tubes. However, predicting the influence of the confinement effect and the compressive capacity of these columns has yet to be further examined. Currently, the Canadian design code limits for reinforced concrete do not reach the strength nor the strain produced by using UHPC. This project uses the Canadian design methods for a cross-section of UHPC to form a column interaction curve and compared it with six test specimens. The effects of steel tube confinement will also be examined. Additionally, the Eurocode 4 (EC4) method, which includes the strengths of UHPC and confinement of steel tube, was used to formulate another column interaction curve. The results show that the Canadian code severely underestimates the design strength of confined UHPC while the EC4 provides much more accurate results.

Prediction of axial strength in circular steel tube confined concrete columns using artificial intelligence

In recent years, together with the boom of the industrial revolution 4.0, terms such as artificial intelligence (AI) are gradually gaining popularity engineering domain. This study proposed a number of AI models for predicting the axial strength in circular steel tube confined concrete (STCC) columns. Particularly, artificial neural networks (ANNs), support vector regression (SVR), linear regression (LR), and M5P were applied in this study. This study applied 136 samples of short and intermediate STCC columns infilled with normal strength concrete, high strength concrete, or ultimate high strength concrete to evaluate the AI models. Compressive strengths of concrete cylinders was ranged from 23.20 Mpa to 188.10 Mpa. The AI models were assessed by statistical indexes including MAPE, MAE, RMSE, and R. The analytical results revealed that the M5P the most effective AI model comparing to others. Comparing with the other models, predicted data obtained by the M5P model show the highest agreement with the actual data in predicting the axial strength of STCC columns. Particularly, the MAPE and R of M5P models were 10.62% and 0.977 respectively. Similarly, the RMSE by the M5P model was 330.38 kN which is the lowest value among 419.39 kN by the LR model, 337.84 kN by the ANNs model, and 857.11 kN by the SVR model. Therefore, the M5P model can be considered as a useful tool to accurately predict the compressive capacity of the STCC columns. Keywords: artificial intelligence; circular steel tube confined concrete columns; axial strength; support vector regression.

Seismic Performance of Recycled Concrete Filled Circular Steel Tube Columns

This study was conducted to experimentally investigate the behavior of recycled concrete-filled circular steel tube (RCFST) columns subjected to cyclic loading. Ten specimens were prepared and tested. Four parameters were used to characterize seismic behavior: the replacement percentage of recycled coarse aggregate, slenderness ratio, axial compression level, and steel ratio. A novel calculation method for the bearing capacity for RCFST columns is established. The failure processes and modes of RCFST columns are found to be similar to normal concrete-filled steel tube columns. Varying the replacement percentage of recycled coarse aggregate has little effect on the hysteresis curves of the RCFST columns. The RCFST columns also show seismic performance similar to that of concrete-filled steel tubes. The displacement ductility of all specimens is larger than 3.0 and the equivalent viscous damping coefficients corresponding to the ultimate load range from 0.305 to 0.460.