Abstract
Some research has been conducted on structural health monitoring (SHM) utilizing raw data to discover structural behavior changes, anomalies, or damage assessment. However, few of them employed statistical indicators represented as input data. Long-term monitoring has several advantages (before/after event comparison, detecting/tracking abrupt changes or aging effects), while the data collection process may have disadvantages (data storing issues, impractical computationally expensive analyses). Thus, the current work proposes two unsupervised machine learning techniques for detecting anomalies using statistical indicators extracted from long-term raw dynamic measurements recorded from a three-dimensional accelerometer installed on the midspan of the Osman Gazi Bridge built in Turkey. For this purpose, one-class Support Vector Machine (SVM) and Local Outlier Factor (LOF) are used as machine learning algorithms to detect anomalous instances. Comparison between these two machine learning revealed similar anomalies. The acquired results support the development of computational methods for assessing structural anomalies based on statistical indicators of acceleration measurements.
Original language | English |
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Pages (from-to) | 241-244 |
Number of pages | 4 |
Journal | International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII |
Volume | 2022-August |
Publication status | Published - 2022 |
Externally published | Yes |
Event | 11th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2022 - Montreal, Canada Duration: 8 Aug 2022 → 12 Aug 2022 |
Bibliographical note
Publisher Copyright:© 2022 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.
Keywords
- Anomaly detection
- Long-span bridges
- Long-term SHM
- Machine learning algorithms
- One-class SVM, Local outlier factor (LOF)
- Statistical indicators
- Suspension bridges