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Bayesian structural damage detection of steel towers using measured modal parameters

  • Lam, Heung-Fai (Department of Architecture and Civil Engineering, City University of Hong Kong) ;
  • Yang, Jiahua (Department of Architecture and Civil Engineering, City University of Hong Kong)
  • Received : 2014.04.04
  • Accepted : 2014.09.18
  • Published : 2015.04.25

Abstract

Structural Health Monitoring (SHM) of steel towers has become a hot research topic. From the literature, it is impractical and impossible to develop a "general" method that can detect all kinds of damages for all types of structures. A practical method should make use of the characteristics of the type of structures and the kind of damages. This paper reports a feasibility study on the use of measured modal parameters for the detection of damaged braces of tower structures following the Bayesian probabilistic approach. A substructure-based structural model-updating scheme, which groups different parts of the target structure systematically and is specially designed for tower structures, is developed to identify the stiffness distributions of the target structure under the undamaged and possibly damaged conditions. By comparing the identified stiffness distributions, the damage locations and the corresponding damage extents can be detected. By following the Bayesian theory, the probability model of the uncertain parameters is derived. The most probable model of the steel tower can be obtained by maximizing the probability density function (PDF) of the model parameters. Experimental case studies were employed to verify the proposed method. The contributions of this paper are not only on the proposal of the substructure-based Bayesian model updating method but also on the verification of the proposed methodology through measured data from a scale model of transmission tower under laboratory conditions.

Keywords

Bayesian approach;damage detection;steel tower;modal identification;model updating

Acknowledgement

Supported by : Council of the Hong Kong Special Administrative Region

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