Autonomous corrosion assessment of reinforced concrete structures : Feasibility study

Woubishet Zewdu Taffese, Ethiopia Nigussie*

*Corresponding author for this work

Research output: Contribution to journalReview ArticleScientificpeer-review

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Abstract

In this work, technological feasibility of autonomous corrosion assessment of reinforced concrete structures is studied. Corrosion of reinforcement bars (rebar), induced by carbonation or chloride penetration, is one of the leading causes for deterioration of concrete structures throughout the globe. Continuous nondestructive in-service monitoring of carbonation through pH and chloride ion (Cl) concentration in concrete is indispensable for early detection of corrosion and making appropriate decisions, which ultimately make the lifecycle management of RC structures optimal from resources and safety perspectives. Critical state-of-the-art review of pH and Cl sensors revealed that the majority of the sensors have high sensitivity, reliability, and stability in concrete environment, though the experiments were carried out for relatively short periods. Among the reviewed works, only three attempted to monitor Cl wirelessly, albeit over a very short range. As part of the feasibility study, this work recommends the use of internet of things (IoT) and machine learning for autonomous corrosion condition assessment of RC structures.

Original languageEnglish
Article number6825
Pages (from-to)1-25
Number of pages25
JournalSensors (Switzerland)
Volume20
Issue number23
DOIs
Publication statusPublished - 1 Dec 2020
MoE publication typeA2 Review article in a scientific journal

Keywords

  • Autonomous corrosion assessment
  • Corrosion
  • Deep learning
  • Intelligent data analytics
  • Internet of things
  • Machine learning
  • Reinforced concrete
  • Sensors

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