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Damage detection in steel structures using expanded rotational component of mode shapes via linking MATLAB and OpenSees

  • Toorang, Zahra (Structural Engineering Research Center, International Institute of Earthquake Engineering & Seismology (IIEES)) ;
  • Bahar, Omid (Structural Engineering Research Center, International Institute of Earthquake Engineering & Seismology (IIEES)) ;
  • Elahi, Fariborz Nateghi (Structural Engineering Research Center, International Institute of Earthquake Engineering & Seismology (IIEES))
  • Received : 2021.04.21
  • Accepted : 2021.09.01
  • Published : 2022.01.25

Abstract

When a building suffers damages under moderate to severe loading condition, its physical properties such as damping and stiffness parameters will change. There are different practical methods besides various numerical procedures that have successfully detected a range of these changes. Almost all the previous proposed methods used to work with translational components of mode shapes, probably because extracting these components is more common in vibrational tests. This study set out to investigate the influence of using both rotational and translational components of mode shapes, in detecting damages in 3-D steel structures elements. Three different sets of measured components of mode shapes are examined: translational, rotational, and also rotational/translational components in all joints. In order to validate our assumptions two different steel frames with three damage scenarios are considered. An iterative model updating program is developed in the MATLAB software that uses the OpenSees as its finite element analysis engine. Extensive analysis shows that employing rotational components results in more precise prediction of damage location and its intensity. Since measuring rotational components of mode shapes still is not very convenient, modal dynamic expansion technique is applied to generate rotational components from measured translational ones. The findings indicated that the developed model updating program is really efficient in damage detection even with generated data and considering noise effects. Moreover, methods which use rotational components of mode shapes can predict damage's location and its intensity more precisely than the ones which only work with translational data.

Keywords

References

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