Structural damage detection using Bayesian inference and seismic interferometry
Name
SCHM_2019.pdf
Description
Accepted version
Size
15.46 MB
Format
Unknown
Checksum (MD5)
f357f4cbc27f30bd8ad8642d89e4b43c
Author(s)
Uzun, Murat
Sun, Hao
Smit, Dirk
Buyukozturk, Oral
Date Issued
2019
Journal
Structural Control and Health Monitoring
Publisher
Wiley
Version
Author's final manuscript
Abstract
© 2019 John Wiley & Sons, Ltd. We present a computational methodology for structural identification and damage detection via linking the concepts of seismic interferometry and Bayesian inference. A deconvolution-based seismic interferometry approach is employed to obtain the waveforms that represent the impulse response functions with respect to a reference excitation source. Using the deconvolved waveforms, we study the following two different damage detection methods that utilize shear wave velocity variations: the arrival picking method and the stretching method. We show that variations in the shear wave velocities can be used for qualitative damage detection and that velocity reduction is more evident for more severely damaged states. Second, a hierarchical Bayesian inference framework is used to update a finite element model by minimizing the gap between the predicted and the extracted time histories of the impulse response functions. Through comparison of the model parameter distributions of the damaged structure with the updated baseline model, we demonstrate that damage localization and quantification are possible. The performance of the proposed approach is verified through two shake table test structures. Results indicate that the proposed framework is promising for monitoring structural systems, which allows for noninvasive determination of structural parameters.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1002/STC.2445
