This study was conducted to evaluate the applicability of concrete-filled steel tube (CFT) columns made from high-performance construction materials. KBC2016, South Korea’s current building code, limits the maximum compressive strength of concrete at 70 MPa and the maximum yield strength of steel at 650 MPa. Similar restrictions to material properties are imposed on major composite structural design parameters in other countries worldwide. With the recent acceleration of the pace of development in the field of material technology, the compressive strength of commercial concrete has been greatly improved and the problem of low tensile strength, known to be the major limitation of concrete, is being successfully addressed by adding fiber reinforcement to concrete. Therefore, the focus of this study was to experimentally determine the strength and ductility enhancement effects, which depend on material composition. To this end, we performed concentric axial loading tests on CFT stub columns made from steel with a yield strength of 800 MPa and steel fiber-reinforced high-strength concrete. By measuring the strain at the yield point of CFT steel during the test, we could determine whether steel yields earlier than ultimate failure load of the member, which is a key design concept of composite structures. The analysis results revealed that the yield point of steel preceded that of concrete on the stress-strain curve by the concurrent action of the strain increase at the maximum strength, attributable to the high compressive strength and steel fiber reinforcement, and the strain increase induced by the confining stress of the steel tube. Additionally, we performed parametric study using ABAQUS to establish the broad applications of CFT using high-performance materials, with the width-to-thickness ratio as the main parameter. Parametric study was undertaken as experimental investigation was not feasible, and we reviewed the criteria for limiting the width-to-thickness ratio as specified in the current building code.
A genetic programming predictive model for parametric study of factors affecting strength of geopolymers
