Application of Multivariate Statistically Based Algorithms for Civil Structures Anomaly Detection

  • Masoud Malekzadeh
  • Mustafa Gul
  • F. Necati Catbas
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)


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.


Structural health monitoring Fiber Bragg Grating Sensors Advanced multivariate statistics Damage detection 



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.


  1. 1.
    Aktan AE, Catbas FN, Grimmelsman KA, Tsikos CJ (2000) Issues in infrastructure health monitoring for management. J Eng Mech ASCE 126(7):711–724CrossRefGoogle Scholar
  2. 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. 149Google Scholar
  3. 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):647CrossRefGoogle Scholar
  4. 4.
    Kwon IB, Baik SJ, Im K, Yu JW (2002) Development of fiber optic BOTDA sensor for intrusion detection. Sens Actuators A 101:77–84CrossRefGoogle Scholar
  5. 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 147:150–164CrossRefGoogle Scholar
  6. 6.
    Worden K (1997) Structural fault detection using a novelty. J Sound Vib 201(1):85–101MathSciNetCrossRefGoogle Scholar
  7. 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):613–626Google Scholar
  8. 8.
    Posenato D, Lanata F, Inaudi D, Smith IFC (2008) Model-free data interpretation for continuous monitoring of complex structures. Adv Eng Inform 22:135–144CrossRefGoogle Scholar
  9. 9.
    Gul M, Catbas FN (2008) Ambient vibration data analysis for structural identification and global condition assessment. J Eng Mech ASCE 134(8):650–662CrossRefGoogle Scholar
  10. 10.
    Zaurin R, Catbas FN (2011) Structural health monitoring using computer vision and influence lines. Struct Health Monit J SAGE Publications 10(3):309–332CrossRefGoogle Scholar
  11. 11.
    Zaurin R, Catbas FN (2010) Integration of computer imaging and sensor data for structural health monitoring of bridges. J Smart Mater Struct (19) 015019:15Google Scholar

Copyright information

© The Society for Experimental Mechanics, Inc. 2013

Authors and Affiliations

  • Masoud Malekzadeh
    • 1
  • Mustafa Gul
    • 2
  • F. Necati Catbas
    • 1
  1. 1.Department of Civil, Environmental and Construction EngineeringUniversity of Central FloridaOrlandoUSA
  2. 2.Department of Civil and Environmental EngineeringUniversity of AlbertaEdmontonCanada

Personalised recommendations