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Damage detection method for large structures using static and dynamic strain data from distributed fiber optic sensor

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Abstract

A method for damage detection applicable to large slender steel structures such as towers of large-scale wind turbines, long-span bridges, and high-rise buildings is presented. This method is based on continuous strain data obtained by distributed fiber optic sensor (FOS) and neural network (NN) analysis. An analytical model for cracked beam based on an energy balance approach was used to train a NN. The continuous static strains and the natural frequencies obtained from the distributed FOSs were used as the input to the trained NN to estimate the crack depths and locations. An experimental study was carried out on a cracked cantilever beam to verify the present method for damage identification. The cracks were inflicted on the beam, and static and free vibration tests were performed for the intact case and the damage cases. The distributed FOSs were used to measure the continuous strains. The damage estimation was carried out for the 5 damage cases using the NN technique. It has been found that the identified crack depths and locations agree reasonably well with the inflicted cracks on the structure.

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Authors and Affiliations

  1. Department of Architectural Engineering, Namseoul University, Choongnam, 331-707, Korea

    Jong-Won Lee & Kwang-Ho Choi

  2. Department of System Dynamics, Korea Institute of Machinery and Materials, Daejeon, 305-343, Korea

    Young-Cheol Huh

Authors
  1. Jong-Won Lee
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  2. Kwang-Ho Choi
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  3. Young-Cheol Huh
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Correspondence to Jong-Won Lee.

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Lee, JW., Choi, KH. & Huh, YC. Damage detection method for large structures using static and dynamic strain data from distributed fiber optic sensor. Int J Steel Struct 10, 91–97 (2010). https://doi.org/10.1007/BF03249515

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  • DOI: https://doi.org/10.1007/BF03249515

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