Edinburgh Napier University

  This paper presents the design and application of a low-cycle reciprocating loading test on 23 recycled aggregate concrete-filled steel tube columns (RACSTC) and 3 ordinary concrete-filled steel tube columns (OCSTC). Additionally, a systematic study on the influence of parameters (e.g., slenderness ratio, axial compression ratio, etc.) was conducted on the seismic performance of the specimens. The results show that all the specimens have good hysteresis performance and a similar development trend of skeleton curve. The influence of slenderness ratio on the seismic index of the specimens is more significant than that of the axial compression ratio and the steel pipe wall thickness. Furthermore, artificial intelligence was applied to estimate the influence of parameter variation on the seismic performance of concrete columns. Specifically, Random Forest (RF) with hyperparameters tuned by Firefly Algorithm (FA) was chosen. The prediction results showed acceptable accuracy from the high correlation coefficients (R) and low Root Mean Square Error (RMSE) values. In addition, sensitivity analysis was applied to rank the influence of the aforementioned input variables on the seismic performance of the specimens. The research results can provide experimental reference for the application of steel tube recycled concrete in earthquake areas.