Digital twin-based machine learning framework for predicting nonlinear seismic response of reinforced concrete shear walls using analytical data

This study proposes a digital twin (DT) and machine learning (ML) framework to predict nonlinear pushover responses of reinforced concrete (RC) shear walls using analytically derived data. Two hundred SAP2000 layered shell models were analyzed, and monotonic lateral capacity curves were processed via SPO2FRAG for bilinear parameter extraction. Key response features-initial stiffness (K₀), yield displacement ([Formula: see text]), and post-yield stiffness ratio (α)were identified. Ten input variables including wall geometry, material properties, reinforcement ratios, axial load, and opening ratio were used to train Random Forest regressors for predicting the pushover curve descriptors. Model accuracy was validated using nested cross-validation, yielding mean test R² values of 0.996 for [Formula: see text], 0.995 for [Formula: see text], and 0.925 for α, while uncertainty measures (Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), 95% confidence intervals) supported robustness. The DT surrogate reconstructs pushover curves in under 2 seconds per specimen, supporting rapid parametric analysis and seismic scenario assessments without the need for repetitive finite element simulations. The study also documents model limitations and outlines guidance for extending the approach to shear-dominated walls and experimental validation.