A novel approach for detection of anomalies using measurement data of the Ironton-Russell bridge

Fan Zhang; Mehdi Norouzi; Victor Hunt; Arthur Helmicki

doi:10.1117/12.2083996

Abstract

Data models have been increasingly used in recent years for documenting normal behavior of structures and hence detect and classify anomalies. Large numbers of machine learning algorithms were proposed by various researchers to model operational and functional changes in structures; however, a limited number of studies were applied to actual measurement data due to limited access to the long term measurement data of structures and lack of access to the damaged states of structures. By monitoring the structure during construction and reviewing the effect of construction events on the measurement data, this study introduces a new approach to detect and eventually classify anomalies during construction and after construction. First, the implementation procedure of the sensory network that develops while the bridge is being built and its current status will be detailed. Second, the proposed anomaly detection algorithm will be applied on the collected data and finally, detected anomalies will be validated against the archived construction events.

Citation Download Citation

Fan Zhang, Fan Zhang, Mehdi Norouzi, Mehdi Norouzi, Victor Hunt, Victor Hunt, Arthur Helmicki, Arthur Helmicki, "A novel approach for detection of anomalies using measurement data of the Ironton-Russell bridge", Proc. SPIE 9437, Structural Health Monitoring and Inspection of Advanced Materials, Aerospace, and Civil Infrastructure 2015, 943717 (28 April 2015); doi: 10.1117/12.2083996; https://doi.org/10.1117/12.2083996

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