Multiscale pattern learning for vibration-based structural anomaly detection: A benchmark dataset and validation study

Vibration measurements from civil structures inherently contain fundamental dynamic characteristics and are widely used to monitor structural behavior to inform condition assessment. Machine learning, particularly unsupervised deep learning models, has demonstrated strong potential in automatically learning patterns from baseline vibration data and detecting anomalous structural behaviors. Complex multiscale temporal patterns may arise in structural responses, particularly under rapidly varying external loads, potentially leading to obscured anomalies and reduced anomaly detection accuracy. The primary contribution of the study is the establishment of a benchmark dataset from vibration tests of a laboratory-scale truss bridge. The benchmark vibration dataset contains structural responses under stationary and moving-load excitations, including multilocation and multilevel structural anomaly scenarios. The established dataset can be further used as a benchmark for moving load identification, modal identification, structural model updating, and structural anomaly detection, among others. Additionally, a multiscale pattern learning approach for vibration-based structural anomaly detection by integrating the TimeMixer network with the one-class support vector machine (TimeMixer-OCSVM) is introduced. The methodological contribution lies in establishing a TimeMixer-OCSVM validation baseline, which demonstrates the capability of the benchmark dataset to support anomaly detection studies. The TimeMixer model is adopted to learn multiscale patterns from structural vibration data, improving the anomaly detection performance under varying external excitations. The constructed benchmark dataset is used to validate the proposed method, and the results demonstrate the effectiveness of the proposed method in identifying structural anomalies. Compared to the other typical methods, the proposed method achieves higher structural anomaly detection accuracy.