Measurement of Reinforcement Corrosion in Concrete Adopting Ultrasonic Tests and Artificial Neural Network

Yidong Xu, Ruoyu Jin

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Limited research has been performed in testing and measuring the reinforcement corrosion levels using non-destructive tests. This research applied ultrasonic-based non-destructive test and artificial neural network to the diagnosis and prediction of rebar’s non-uniform corrosion-induced damage within reinforced concrete members. Ultrasonic velocities were tested by applying ultrasonic to reinforced concrete prisms before and after the rebar corrosion. Input parameters including concrete strength, ultrasonic velocity, and the specimen dimension-related variable were used for the prediction of reinforcement corrosion level adopting artificial neural network models. Using totally 50 experimental observations, Radial Basis Function-based model was found with higher accuracy in predicting corrosion levels compared to Back Propagation-based model. This study leads to future research in high-accuracy non-destructive measurement of reinforcement corrosion in concrete.
Original languageEnglish
Pages (from-to)125-133
JournalConstruction and Building Materials
Volume177
DOIs
Publication statusPublished - 4 Jun 2018

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Reinforcement
Ultrasonics
Concretes
Corrosion
Neural networks
Ultrasonic velocity
Reinforced concrete
Prisms
Backpropagation
Testing

Bibliographical note

© 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

Cite this

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Measurement of Reinforcement Corrosion in Concrete Adopting Ultrasonic Tests and Artificial Neural Network. / Xu, Yidong; Jin, Ruoyu.

In: Construction and Building Materials, Vol. 177, 04.06.2018, p. 125-133.

Research output: Contribution to journalArticleResearchpeer-review

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