Skip to main content

Application of Multivariate Statistically Based Algorithms for Civil Structures Anomaly Detection

  • Conference paper
  • First Online:

Abstract

Two multivariate statistics based damage detection algorithms are explored in conjunction with optical fiber sensors for long-term application of Structural Health Monitoring. Two newly developed data driven methods are investigated, for bridge health monitoring, here based on strain data captured by Fiber Bragg Grating (FBG) sensors from 4-span bridge model. The most common and critical damage scenarios were simulated on the representative bridge model equipped with FBG sensors. Acquired strain data were processed by both Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA). The efficiency of FBG sensors, MPCA and MCCA for detecting and localizing damage is explored. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to observe and detect both local and global damage implemented on the bridge structure.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   349.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   449.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   449.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Aktan AE, Catbas FN, Grimmelsman KA, Tsikos CJ (2000) Issues in infrastructure health monitoring for management. J Eng Mech ASCE 126(7):711–724

    Article  Google Scholar 

  2. Ferdinand P, Magne S, Dewynter-Marty V, Martinez C, Rougeault S, Bugaud M (1997) Applications of Bragg Grating Sensors in Europe. In: Proceedings of the 12th International Conference on Optical Fibre Sensors, Williamsburg, USA, p. 149

    Google Scholar 

  3. Hill KO, Fuji Y, Johnson DC, Kawasaki BS (1978) Photosensitivity in optical fiber waveguides: application to reflection fiber fabrication. Appl Phys Lett 3(2):647

    Article  Google Scholar 

  4. Kwon IB, Baik SJ, Im K, Yu JW (2002) Development of fiber optic BOTDA sensor for intrusion detection. Sens Actuators A 101:77–84

    Article  Google Scholar 

  5. Majumder M, Gangopadhyay TK, Chakraborty AK, Dasgupta K, Bhattacharya DK (2008) Review: Fibre Bragg gratings in structural health monitoring—present status and applications. Sens Actuators A 147:150–164

    Article  Google Scholar 

  6. Worden K (1997) Structural fault detection using a novelty. J Sound Vib 201(1):85–101

    Article  MathSciNet  Google Scholar 

  7. Catbas FN, Gokce HB, Gul M (2012) Nonparametric analysis of structural health monitoring data for identification and localization of changes: Concept, lab, and real-life studies. Structural Health Monitoring, 11(5):613–626

    Google Scholar 

  8. Posenato D, Lanata F, Inaudi D, Smith IFC (2008) Model-free data interpretation for continuous monitoring of complex structures. Adv Eng Inform 22:135–144

    Article  Google Scholar 

  9. Gul M, Catbas FN (2008) Ambient vibration data analysis for structural identification and global condition assessment. J Eng Mech ASCE 134(8):650–662

    Article  Google Scholar 

  10. Zaurin R, Catbas FN (2011) Structural health monitoring using computer vision and influence lines. Struct Health Monit J SAGE Publications 10(3):309–332

    Article  Google Scholar 

  11. Zaurin R, Catbas FN (2010) Integration of computer imaging and sensor data for structural health monitoring of bridges. J Smart Mater Struct (19) 015019:15

    Google Scholar 

Download references

Acknowledgment

The authors would like to acknowledge Dr. Il-Bum Kwon from KRISS Korea for his expertise and support for the fiber optic sensing development and work at the University of Central Florida. For this, the authors are grateful to Dr. Kwon for his guidance and know-how. The research project described in this paper is supported by the Federal Highway Administration (FHWA) Cooperative Agreement Award DTFH61-07-H-00040. The authors would like to express their profound gratitude to Dr. Hamid Ghasemi of FHWA for his support of this research. The authors would also like to acknowledge the contributions of their research collaborators and their research team. The opinions, findings, and conclusions expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsoring organization.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to F. Necati Catbas .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 The Society for Experimental Mechanics, Inc.

About this paper

Cite this paper

Malekzadeh, M., Gul, M., Catbas, F.N. (2013). Application of Multivariate Statistically Based Algorithms for Civil Structures Anomaly Detection. In: Catbas, F., Pakzad, S., Racic, V., Pavic, A., Reynolds, P. (eds) Topics in Dynamics of Civil Structures, Volume 4. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6555-3_32

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-6555-3_32

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-6554-6

  • Online ISBN: 978-1-4614-6555-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics