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- Title
Parameter identification for structural health monitoring based on Monte Carlo method and likelihood estimate.
- Authors
Songtao Xue; BoWen; 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.
- Subjects
STRUCTURAL health monitoring; MONTE Carlo method; MATHEMATICAL optimization; RANDOM noise theory; MAXIMUM likelihood statistics
- Publication
International Journal of Distributed Sensor Networks, 2018, Vol 14, Issue 7, p1
- ISSN
1550-1329
- Publication type
Article
- DOI
10.1177/1550147718786888