Wanglin Wu

Multi-type data anomaly detection for structural health monitoring using interpretable machine learning methodology

This study proposes a novel interpretable data anomaly detection framework to identify both extreme events and sensor faults across various data types in SHM systems with high efficiency and accuracy.