Seismic performance evaluation of recycled aggregate concrete-filled steel tubular columns with field strain detected via a novel mark-free vision method

Abstract

In this study, low cycle reciprocating load tests were conducted on nine recycled aggregate concrete-filled steel tube columns (RACSTCs) and a solitary ordinary concrete-filled steel tube contrast column (OCSTC). The axial compression ratio and steel-tube wall thickness were considered as the parameters of interest. Accordingly, the failure mechanism, hysteresis performance, skeleton curve, ductility coefficient, energy consumption performance, and stiffness degradation of the two types of columns were compared. A vision measurement system was developed to track the full field deformations of specimens, and a quadocular vision system was constructed to determine the concrete column deformations. The deformations were compared with those obtained from laser measurements using an image and point cloud stitching algorithm. The results show that the hysteresis curves of RACSTCs are non-pinched with large ductility coefficients for all the specimens and the equivalent viscous damping coefficients are in the range of 0.462–0.975. RACSTC specimens exhibit good seismic and deformation performance. A three-fold restoring force model was established for RACSTC specimens based on skeleton curve test data. The theoretical hysteresis curve obtained using the skeleton curve model and hysteresis rule shows good agreement with the hysteresis curve obtained from experiment. This can be used as a reference for research on the restoring force characteristics of RACSTCs. The results obtained using the vision measurement system show that the measured mean relative error values are less than 0.504%. This confirms that the multiple vision measurement method can be used to accurately measure curved surface deformations as well as the full field strain values of RACSTCs. Therefore, the proposed method can be used to evaluate the asymmetric deformation of structures subjected to vibrations, impact, and other load types, thereby providing early warning decision data.