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Time reversal damage localization in concrete based on two-dimensional meso-scale modeling

Author(s):



Medium: journal article
Language(s): English
Published in: Structural Health Monitoring, , n. 1, v. 20
Page(s): 188-201
DOI: 10.1177/1475921720918374
Abstract:

In this article, a damage localization method in concrete materials based on time reversal theory and meso-scale finite element simulation considering random heterogeneous properties is developed. In this article, concrete is regarded as a multiphase composite material consisting of cement mortar matrix, coarse aggregates, and interface transition zones. Compared to other methods, which assume that concrete is homogeneous, the meso-scale model considers the intricacies of concrete inhomogeneity and can therefore better characterize the interaction between stress waves and internal structures of concrete material. Through the meso-scale method, acoustic phenomena including reflection, transmission, and diffraction among internal structures of concrete can be modeled. Furthermore, a novel time reversal based, damage imaging method is developed using the envelope of the refocused damage scattering signal to monitor the health condition of concrete. The scattered signal received by each sensor is time reversed and reemitted via numerical computation. To decrease the dispersion effect, the autocorrelation function of the refocused signals is computed to generate an image of the estimated damage. A time correction factor is introduced to decrease the influence of the elongated wave packet. Numerical and experimental results indicate that the proposed damage imaging method can locate damage with high spatial resolution in heterogeneous concrete material. Moreover, owing to the meso-scale modeling, the propagation of high-frequency stress waves in concrete can be analyzed more accurately.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1177/1475921720918374.
  • About this
    data sheet
  • Reference-ID
    10562444
  • Published on:
    11/02/2021
  • Last updated on:
    19/02/2021
 
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