Machine learning-based shear bearing capacity of concrete columns confined by transverse reinforcement subjected to lateral cyclic loading

Abstract

The shear bearing capacity of confined concrete columns subjected to lateral cyclic loading is an important mechanical property in investigating seismic behavior of concrete buildings. However, it is still difficult to accurately predict shear bearing capacity of confined concrete columns using traditional analysis methods owing to its complex mechanical principle and indeterminate multivariable interrelationship. In this paper, an experimental study of 15 confined concrete columns subjected to lateral cyclic loading was conducted to explore the seismic behavior of confined concrete columns. Moreover, ANN and SVR models were established to accurately estimate the shear bearing capacity of confined concrete columns based on a reliable test database consisting of 121 specimens conducted in this study and published literatures. Nine key parameters were considered as input variables, including cross-sectional area of core concrete, unconfined concrete compressive strength, shear span ratio, axial compression ratio, volumetric ratio of transverse reinforcement, yield strength of transverse reinforcement, longitudinal reinforcement ratio, yield strength of longitudinal reinforcement, and confinement type. Additionally, the model sensitivity analysis was conducted to investigate the impact of parameters on shear bearing capacity of confined concrete columns. Finally, the ANN and SVR models were evaluated by comparing with five existing predicted methods and experimental results indicating that the ANN and SVM models have enough accuracy and reliability in predicting shear bearing capacity of confined concrete columns subjected to lateral cyclic loading.