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Piezoelectric sensor–based damage progression in concrete through serial/parallel multi-sensing technique

Author(s):



Medium: journal article
Language(s): English
Published in: Structural Health Monitoring, , n. 2, v. 19
Page(s): 339-356
DOI: 10.1177/1475921719845153
Abstract:

Structural damage detection using unmanned Structural Health Monitoring techniques is becoming the need of the day with the technologies available presently. Sensors made of Lead Zirconate Titanate materials, due to their simplicity and robustness, are increasingly used as an effective monitoring sensor in Structural Health Monitoring. Continuous monitoring of the structures using Lead Zirconate Titanate sensors often results in a laborious data retrieval process due to the large amount of signal generated. To speed up the data retrieval process, a multi-sensing technique in which the Lead Zirconate Titanate patches are connected in series and parallel is proposed for structural damage detection. The proposed method is validated using an experimental investigation carried out on a reinforced concrete beam embedded with smart Lead Zirconate Titanate sensor units. The beam is subjected to damage, and the location of damage is identified using conductance signatures obtained from patches sensed individually and through multiplexing. This article proposes an effective methodology for selection of patches to be connected in series/parallel to maximise the efficiency of damage detection. Damage quantification using conventional statistical metrics such as root mean square deviation, mean absolute percentage deviation and cross correlations are found to be ineffective in identifying the location of damage from the multiplexed signatures. In turn, dynamic metrics such as moving root mean square deviation, moving mean absolute percentage deviation and moving cross correlation with overlapped moving blocks of data are proposed in the present work and their ability to detect the damage location from multiplexed signatures is discussed.

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: 10.1177/1475921719845153.
  • About this
    data sheet
  • Reference-ID
    10562297
  • Published on:
    11/02/2021
  • Last updated on:
    19/02/2021
 
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