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Two new approaches in anomaly detection with field data from bridges both in construction and service stages

Zhang, Fan
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439561983

Abstract Details

2015, MS, University of Cincinnati, Engineering and Applied Science: Electrical Engineering.
The University of Cincinnati Infrastructure Institute has been dedicated to Structural Health Monitoring for about 20 years. UCII establishes a whole set of monitoring system including sensors, data acquisition equipment and a customer website for each bridge that is to be monitored. The Ironton-Russell Bridge Replacement is the first bridge that UCII has monitored since the bridge’s construction stage. At the heart of UCII’s monitoring system is the ability to detect any anomalies; among these anomalies might be damages caused by structural changes due to creep, shrinkage, crack and so forth. The existing anomaly detection algorithm assumes a linear relationship between strain and temperature. To complement the anomaly detection, an Autoregressive Model based algorithm is proposed which doesn’t rely on the relationship between strain and temperature. Also proposed is a probabilistic approach which employs t-distribution to identify anomalies, moreover, this approach is promising in discerning anomalies that are caused by temperature change from those not related to temperature. These two approaches are proved to be applicable for both in-construction and in-service bridges.
Arthur Helmicki, Ph.D. (Committee Chair)
H. Howard Fan, Ph.D. (Committee Member)
Victor Hunt, Ph.D. (Committee Member)
106 p.
Engineering
Structural health monitoring; anomaly detection; bridge in construction

Recommended Citations

Citations

  • Zhang, F. (2015). Two new approaches in anomaly detection with field data from bridges both in construction and service stages [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439561983

    APA Style (7th edition)

  • Zhang, Fan. Two new approaches in anomaly detection with field data from bridges both in construction and service stages. 2015. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439561983.

    MLA Style (8th edition)

  • Zhang, Fan. "Two new approaches in anomaly detection with field data from bridges both in construction and service stages." Master's thesis, University of Cincinnati, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439561983

    Chicago Manual of Style (17th edition)

ucin1439561983
568
© 2015, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.