Fast-Track Code-Based Seismic Vulnerability Screening with Machine Learning: Evidence from 300 Italian Buildings

The study introduces a data-driven method for estimating a code-based seismic vulnerability index for roughly 300 Italian buildings. Detailed surveys, experimental tests, and numerical analyses produced about 15 mixed-type predictors. These inputs fed several predictive algorithms, in particular logistic regression and an artificial neural network (ANN). After rebalancing the classes by adjusting the vulnerability cut-off, the ANN classifies buildings into two risk groups with better than 85% accuracy. SHAP analysis reveals how much each feature influences the prediction, providing a transparent tool to help authorities prioritise seismic-risk mitigation.