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Parameter identification for structural health monitoring based on Monte Carlo method and likelihood estimate

Author

Listed:
  • Songtao Xue
  • Bo Wen
  • Rui Huang
  • Liyuan Huang
  • Tadanobu Sato
  • Liyu Xie
  • Hesheng Tang
  • Chunfeng Wan

Abstract

Structural parameters are the most important factors reflecting structural performance and conditions. As a result, their identification becomes the most essential aspect of the structural assessment and damage identification for the structural health monitoring. In this article, a structural parameter identification method based on Monte Carlo method and likelihood estimate is proposed. With which, parameters such as stiffness and damping are identified and studied. Identification effects subjected to three different conditions with no noise, with Gaussian noise, and with non-Gaussian noise are studied and compared. Considering the existence of damage, damage identification is also realized by the identification of the structural parameters. Both simulations and experiments are conducted to verify the proposed method. Results show that structural parameters, as well as the damages, can be well identified. Moreover, the proposed method is much robust to the noises. The proposed method may be prospective for the application of real structural health monitoring.

Suggested Citation

  • Songtao Xue & Bo Wen & Rui Huang & Liyuan Huang & Tadanobu Sato & Liyu Xie & Hesheng Tang & Chunfeng Wan, 2018. "Parameter identification for structural health monitoring based on Monte Carlo method and likelihood estimate," International Journal of Distributed Sensor Networks, , vol. 14(7), pages 15501477187, July.
    • Handle: RePEc:sae:intdis:v:14:y:2018:i:7:p:1550147718786888
    DOI: 10.1177/1550147718786888
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    References listed on IDEAS

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    1. Bensi, Michelle & Kiureghian, Armen Der & Straub, Daniel, 2013. "Efficient Bayesian network modeling of systems," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 200-213.
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