Experimental and Transformer-Based Study on Seismic Behavior and Plastic Hinge Length of RC Columns Reinforced with End-Fixed Ultra-High Strength Rebars

The application of machine learning (ML) in structural engineering is receiving increasing attention recently. This paper experimentally studies three self-restoring reinforced concrete (SRRC) columns reinforced with low-bond ultra-high strength rebars, to first discuss the reliability and evaluation of the SRRC columns under multiple reversed cyclic (MRC) loads induced by strong earthquakes, and to also first introduce the Transformer method into the analysis and discussion of structural tests. The tests confirmed the superior seismic behavior and high self-centering performance of the columns and presented how MRC loads affect the seismic performance of SRRC columns in terms of the lateral load-carrying capacity and energy dissipation capacity. Superior to conventional methods, a high-accuracy Transformer-based model is proposed to evaluate the plastic hinge height (PHL) of the tested SRRC columns compared with the other three algorithms (MLP, KNN, and XGBoost). Furthermore, the Shapley Additive exPlanations (SHAP) approach is adopted to explain the insight relationship between the structural parameters and PHL of the columns.