Condition-aware AI framework for automated structural health monitoring

This study presents an AI-powered framework for automated structural health monitoring that integrates modal identification, anomaly detection, and damage localization under varying environmental and operational conditions. The approach combines stochastic subspace identification with frequency–spatial domain decomposition for automated modal extraction and a condition-aware anomaly detector based on a conditional variational autoencoder. A secondary SSA–OC-SVM module verifies and localizes damage. The methodology is validated on a laboratory-scale structure through 500 one-hour tests under temperature variations up to 35 °C and diverse loading conditions. The identified modes exhibit MAC = 0.99–1.00, confirming reliable automated identification. The CVAE reconstructs healthy-state modal frequencies with MAPE = 0.23%, RMSE = 0.027 Hz, and R 2 = 0.836, effectively distinguishing environmental effects ( ≤ 0 . 27 pp) from genuine structural changes. The integrated framework further accurately localizes all induced damage scenarios across nine structural zones, demonstrating high accuracy, robustness, and scalability for next-generation SHM automation. • End-to-end AI SHM pipeline automates modal identification, diagnosis, and localization. • Hybrid SSI-Cov and FSDD with adaptive bands and isolation forest extracts stable modes • Condition-aware CVAE and Gaussian thresholds classify health. • SSA and sensor 3D OC-SVM stage verifies anomalies and maps sub-element damage.