Damage Detection Using Flexibility Proportional Coordinate Modal Assurance Criterion

  • Luciana Balsamo
  • Suparno Mukhopadhyay
  • Raimondo Betti
  • Hilmi Lus
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)


In the recent years, vibration-based identification techniques have attracted the attention of the civil engineering community, as these methods can be naturally incorporated into automated continuous structural health monitoring procedures. It is a generally accepted approach to model the damage and deterioration of a structural element through stiffness reduction. For this reason, a feature tailored so as to be well correlated to the expected differences between the undamaged and damaged flexibility matrices, such as the recently proposed Flexibility Proportional Coordinate Modal Assurance Criterion (FPCOMAC), is ideally suited to be exploited as damage sensitive feature. We present a statistical pattern recognition based damage detection method that employs FPCOMAC as damage sensitive feature. The proposed methodology is executed according to the training and testing phases typical of the pattern recognition framework. Particular effort is devoted to test the ability of the method to correctly identify the damage when response time histories used in the training are measured in different environmental conditions. The formulation is derived considering a shear-type structural system. Results obtained by considering a 7 DOFs shear-type system prove the efficiency of the method in detecting and locating the damage, irrespective of damage severity and environmental effects, under the conditions that the damage amount is greater than the structural variations caused by the external factors and the amount of data is reasonably large.


Statistical pattern recognition Structural damage detection FPCOMAC 


  1. 1.
    Sohn H, Farrar CR, Hunter NF, Worden K (2001) Structural health monitoring using statistical pattern recognition techniques. J Dyn Syst Meas Control 123:706–711Google Scholar
  2. 2.
    Gul M, Catbas FN (2011) Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering. J Sound Vib 330:1196–1210Google Scholar
  3. 3.
    Mukhopadhyay S, Lus H, Hong AL, Betti R (2012) Propagation of mode shape errors in structural identification. J Sound Vib 331(17):3961–3975Google Scholar
  4. 4.
    Hong A (2010) Model order determination in stochastic system identification for civil infrastructure systems. Columbia University, New YorkGoogle Scholar
  5. 5.
    Mukhopadhyay S, Betti R, Lus H (2013) Output only structural identification with minimal instrumentation. In: Proceedings of the 31st international modal analysis conference, Garden, California, USAGoogle Scholar

Copyright information

© The Society for Experimental Mechanics 2014

Authors and Affiliations

  • Luciana Balsamo
    • 1
  • Suparno Mukhopadhyay
    • 1
  • Raimondo Betti
    • 1
  • Hilmi Lus
    • 2
  1. 1.Department of Civil Engineering and Engineering MechanicsColumbia UniversityNew YorkUSA
  2. 2.Department of Civil EngineeringBogazici UniversityIstanbulTurkey

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