Compressive behaviour of stone dust concrete composite columns confined with bamboo and thin-walled steel-tube systems

A novel bamboo plywood and thin-walled steel-tube dual-confined stone dust concrete composite column (BSDCC) is proposed to integrate the utilization of industrial solid waste and bamboo materials. Compression tests are performed on 12 BSDCC specimens. The failure mechanism is elucidated by analysing the entire process of specimen damage, the failure modes of the thin-walled steel tubes, the load-displacement curves, and the strain development patterns of different composite components. The test results show that the failure modes of the BSDCC specimens under compression were dominated by adhesive cracking failure of the bamboo plywood between the transverse binding bars in the middle of the column and the bamboo plywood on the tension side and yielding of the inner steel tube. Throughout the loading process, the specimens showed ideal elastoplastic behaviour, but the plastic capacity was not advantageous, and the plastic strengthening effect was not obvious. The bearing capacity of the stone dust concrete accounts for less than 10% of the total bearing capacity of the composite column, so the concrete strength is not the main factor improving the bearing capacity. The inner steel tube filled-with low-strength stone dust concrete can result in a composite column configuration with better mechanical properties, changes the failure mode of the composite column and the deformation mode of a cross-section, and delays the overall failure of the composite column. The setting of binding bars can effectively delay the bamboo plywood failure, and the outward partial buckling of the slab and steel tube enhances the restraint effect of the steel tube on the core concrete. Setting the binding bar in the middle of the column body can improve the column eccentric load bearing capacity and ductility. A formula for calculating the bearing capacity of BSDCCs under eccentric compression is established based on nonlinear regression analysis.