Locating damaged storeys in a structure based on its identified modal parameters in Cauchy wavelet domain

Wei C. Su*, Tuyen Q. Le, Chiung-Shiann Huang, Pei Y. Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Structural damage detection based on the changes of dynamic properties is a major topic for structural health monitoring. In this paper, efforts are made to extend the flexibility-based damage localization methods, especially the damage locating vectors (DLVs) method, to the case of earthquake vibration, where the finite element model and mass matrices are not available. First, a new method using continuous Cauchy wavelet transform (CCWT) and state-variable time series model is proposed to identify the modal parameters of a structure. Then the flexibility matrix can be constructed from the identified modal parameters. Second, a modified DLVs damage assessment approach is also proposed to locate damage positions in the structure through a weighted relative displacement index (WRDI). This index is calculated by using DLVs vectors determined from the change of flexibility matrix before and after damage of the structure. Numerical analyses demonstrate that the proposed process can indeed monitor the variation of stiffness for each storey. These two approaches are further applied to process the dynamic responses of three-storey and eight-storey steel frames in shaking table tests. The proposed scheme is also proved to be superior to mode shape based methods (CMS, COMAC) in monitoring the variation of stiffness for each storey.

Original languageEnglish
Pages (from-to)674-692
Number of pages19
JournalApplied Mathematical Modelling
Volume53
DOIs
StatePublished - 1 Jan 2018

Keywords

  • ARX model
  • Continuous wavelet transform
  • DLVs method
  • Structural damage detection
  • System identification

Fingerprint

Dive into the research topics of 'Locating damaged storeys in a structure based on its identified modal parameters in Cauchy wavelet domain'. Together they form a unique fingerprint.

Cite this