Seismic Deformation Capacity Prediction of Steel-Reinforced Concrete (SRC) Columns Based on Test Database and Machine Learning

Seismic resilience assessment of high-rise buildings heavily relies on the deformation limits and fragility data of structural components, yet such data is still lacking for steel-reinforced concrete (SRC) columns, which are widely used in high-rise structures. To address this gap, this study establishes a test database consisting of 312 SRC column specimens, including 17 input parameters and three key experimental results, i.e., failure mode, yielding drift ratio θy, and ultimate drift ratio θu. Two machine learning (ML) frameworks are proposed and four ML models are trained and compared. It is found that the two-stage framework incorporating a failure mode classification shows only a slight improvement in the model performance. Thus, an end-to-end framework is recommended due to its simplicity and avoidance of error propagation, and RF and XGBoost models are adopted and tuned for θy and θu prediction for their optimal performance. Model interpretation is carried out using permutation importance (PI) and SHAP analyses to verify consistency with domain knowledge, with the key influencing factors identified as longitudinal reinforcement strength (fyl) and axial load ratio (nt) for deformation capacity, and shear-span ratio (λ) for failure mode classification. The performance of ML models is compared with conventional data-fitting approaches, and it is proven that ML models outperform conventional formulas, with the R2 for predicting θy and θu improved by 26.5% and 32.9%, the RMSE reduced by 30.0% and 30.4%, and the MAPE reduced by 18.5% and 48.4%, respectively, thus providing a powerful data-driven tool for the seismic resilience assessment of SRC columns and expanding the fragility data of composite components.