Structural damage detection and localization via an unsupervised anomaly detection method
提出一种无监督机器学习框架，结合动态图卷积神经网络和Transformer，从传感器时间序列数据中检测并定位结构损伤，在钢结构和斜拉桥数据上验证了有效性。
This study introduces an unsupervised machine learning framework for damage detection and localization in Structural Health Monitoring (SHM), leveraging dynamic graph convolutional neural networks and Transformer networks. This approach is specifically tailored to overcome the challenge of limited labeled data in SHM, enabling precise analysis and feature synthesis from sensor-derived time series data for accurate damage identification. Incorporating a novel ‘localization score’ enhances the framework’s precision in pinpointing structural damages by integrating data-driven insights with a physics-informed understanding of structural dynamics. Extensive validations on diverse structures, including a benchmark steel structure and a real-world cable-stayed bridge, underscore the framework’s effectiveness in anomaly detection and damage localization, showcasing its potential to safeguard critical infrastructure through advanced data-effective machine learning techniques. • An unsupervised machine learning method for structural damage detection and localization. • It utilizes advanced techniques including Graph CNN and Transformer. • A novel, normalized, quantitative metric is proposed for accurate damage localization. • Extensive evaluations on both synthetic and real data show its effectiveness.