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Identification of Wind Loads on Structures Based on Modal Kalman Filter with Unknown Inputs

Author(s):

ORCID
Medium: journal article
Language(s): English
Published in: Buildings, , n. 7, v. 12
Page(s): 1003
DOI: 10.3390/buildings12071003
Abstract:

Wind loads on structures are difficult to directly measure, so it is practical to identify structural wind loads based on the measurements of structural responses. However, this inversed problem is challenging compared with conventional load identification as wind loads are time-space coupled and spatially distributed dynamic loads on structures. An improved method is proposed for identifying wind loads on structures using only partial measurements of structural acceleration responses in this paper. First, the wind loads on a structure are decomposed by proper orthogonal decomposition as a series of time-space decoupled sub-distributed dynamic loads with independent basic spatial distribution functions and time history functions. Herein, structural modes are adopted as the basic spatial distribution functions and structural modes of discretized and continuous structural systems are investigated. Then, a history function of the decomposed wind load is identified in the modal domain based on modal Kalman filter with unknown inputs, which is proposed by the authors. Finally, the distributed wind loads are reconstructed for discrete or continuous structural systems. The feasibility of the proposed algorithm is verified by two numerical examples of identification of wind loads on a discrete shear frame and a wind turbine tower, respectively.

Copyright: © 2022 by the authors; licensee MDPI, Basel, Switzerland.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10688597
  • Published on:
    13/08/2022
  • Last updated on:
    10/11/2022
 
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