The prestressed column, where unbonded posttensioned tendons are arranged along the axis of the RC column, showed self-centering behavior after lateral loading. To investigate the detailed hysteretic response, nine prestressed RC columns with different axial loads and transverse reinforcement arrangements were tested and subjected to the combined action of cyclic horizontal loading and constant axial loading. The experimental results revealed the failure modes, and the drift capacity was analyzed using the ductility and plastic hinge zone length. The controlling effects on residual displacements varied over time during testing, and their mechanism was explained from the point of view of energy dissipation capacity.

The yield performances and failure mode of reinforced concrete (RC) columns, which are critical structural performances to the design and research of engineering structures, have a significant impact on the dynamic response, the performance level, and the design of seismic ductility. The traditional empirical theoretical method used to predict the yield performances and failure mode leads to large dispersions in most cases. To better estimate the yield performances and failure mode of RC columns, this paper developed a novel neural network method. Empirical theoretical models are used to determine the input parameters of the neural network by analyzing the factors that affect the yield performance and failure mode of RC columns, and the rationality of these parameters is verified by sensitivity analysis. The back-propagation (BP) neural network method was adopted. The influence of the number of hidden neurons was studied to improve the model accuracy. Comparative analysis revealed that the prediction results of the neural network are in good agreement with the experimental results and are more accurate than other traditional empirical theoretical models. The initial parameters were optimized using particle swarm optimization (PSO), which has been proven to be superior to the genetic algorithm (GA) and sparrow search algorithm (SSA) optimization methods in terms of effectiveness and computation time. The high generalization ability of the prediction model was calibrated using the test and validation sets and another eight additional sets of experimental data. The proposed method provides a new way to predict the structural performance under seismic actions when experimental data are insufficient.