Building information modeling-based bridge health monitoring for anomaly detection under complex loading conditions using artificial neural networks

Tae Ho Kwon, Sang Ho Park, Sang I. Park, Sang Ho Lee

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

This study developed an Industry Foundation Classes (IFC) building information modeling (BIM) based framework for bridge health monitoring using behavioral prediction under complex loading conditions. The proposed framework predicts the behavior of the current bridge state under complex loading conditions then employs an anomaly detection method that compares the measured behavior of the bridge structure with the predicted normal value under the same loading condition. This behavioral prediction is accomplished using an artificial neural network (ANN) model based on structural analysis theory and trained using long-term sensor data. The proposed framework operates in an IFC-BIM environment to facilitate bridge management. The IFC spatial element provides a connection between the sensor and the bridge element and between the anomaly information and the IFC object of the bridge element. The proposed framework is then demonstrated on a field cable-stayed bridge in Korea. The results confirm the prediction accuracy of the proposed ANN model under complex loading conditions and its ability to identify element anomalies for maintenance.

Original languageEnglish
Pages (from-to)1301-1319
Number of pages19
JournalJournal of Civil Structural Health Monitoring
Volume11
Issue number5
DOIs
Publication statusPublished - 2021 Nov

Bibliographical note

Publisher Copyright:
© 2021, Springer-Verlag GmbH Germany, part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Safety, Risk, Reliability and Quality

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