Long-term structural health monitoring of long-span suspension bridges and anomaly detection using statistical indicators

Oğuzhan Çetindemir, Esra Tepe, Abdullah Can Zülfikar, Ali Yesilyurt, Nurdan Memişoğlu Apaydin

Research output: Contribution to journalConference articlepeer-review

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 languageEnglish
Pages (from-to)241-244
Number of pages4
JournalInternational Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII
Volume2022-August
Publication statusPublished - 2022
Externally publishedYes
Event11th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2022 - Montreal, Canada
Duration: 8 Aug 202212 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

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