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Corrosion monitoring of flexural reinforced concrete members under service loads using distributed long-gauge carbon fiber sensors

Author(s):



Medium: journal article
Language(s): English
Published in: Structural Health Monitoring, , n. 2, v. 17
Page(s): 379-394
DOI: 10.1177/1475921717698973
Abstract:

Currently, corrosion of steel reinforcements is a major topic for several researchers because of the early deterioration and shortening of the service life of structures, particularly those prone to salt attacks. This study proposes a new technique for monitoring corrosion of steel reinforcements in flexural reinforced concrete structures using distributed embedded long-gauge packaged carbon fiber line sensors with self-compensation under service loads. Three different approaches including continuous-strain ratio, distributed-strain ratio, and section fiber model were proposed to evaluate corrosion levels using strain measurements. Different groups of distributed packaged carbon fiber line sensors were installed on the concrete surface and steel reinforcements of the reinforced concrete beam to verify the proposed approaches experimentally using the accelerated corrosion technique. The sensors installed on the concrete surface affected by cracks can only localize the corrosion locations but are unable to determine the actual values of corrosion levels. The degree of corrosion calculated using each approach was compared with the weight loss of an experimental corrosion model. The results showed that the continuous-strain ratio approach will be suitable to localize and evaluate the corrosion degrees if corrosion occurs before the formation of cracks, while the distributed-strain ratio is more effective in the presence of cracks. In highly damaged conditions, the section fiber model will be the most accurate method to evaluate and localize corrosion in steel reinforcements.

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: 10.1177/1475921717698973.
  • About this
    data sheet
  • Reference-ID
    10562060
  • Published on:
    11/02/2021
  • Last updated on:
    19/02/2021
 
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