Simulation of corrosion protection methods in reinforced concrete by artificial neural networks and fuzzy logic

Original scientific paper

Authors

  • Alireza Afshar Department of Civil and Environmental Engineering, George Mason University, Fairfax, VA, USA https://orcid.org/0000-0003-4349-9203
  • Ali Shokrgozar Department of Civil and Environmental Engineering, Idaho State University, 921 S 8th Ave, Mail Stop 8060, Pocatello, Idaho, 83209 USA https://orcid.org/0000-0001-5835-8038
  • Abdollah Afshar Department of Materials Science and Engineering, Sharif University of Technology, Tehran, Iran https://orcid.org/0000-0003-4229-8039
  • Amirhossein Afshar Department of Civil and Environmental Engineering, Sharif University of Technology, Tehran, Iran https://orcid.org/0000-0003-3038-5426

DOI:

https://doi.org/10.5599/jese.1220

Keywords:

Simulation, Corrosion, Concrete, Neural Networks, Fuzzy Logic

Abstract

In this study, the effect of protection methods regarding the corrosion decrement of steel in concrete was simulated by artificial neural networks (ANNs) and fuzzy logic (FL) approaches. Hot dip galvanizing as a protective coating, Ferrogard 901 corrosion inhibitor, a pozzolanic component, such as fly ash (FA) and micro-silica (MS), and eventually rebar AISI-304 were employed in concrete. Reinforced concrete samples were held under impres­sed voltage of 30 V in 3.5 % NaCl electrolyte for 350 hours toward a stainless-steel auxiliary electrode. Corrosion currents have been modelled using feed forward back propa­gation ANNs and FL methods. The results demonstrate good consistency between cor­rosion data and simulated models. Furthermore, the correlation coefficient criterion clearly indicates using pozzolanic materials, with a combination of MS and FA, can be introduced as one of the best corrosion protection methods, with a 35 % contribution factor in reinforced concrete.

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Published

01-04-2022

Issue

Vol. 12 No. 3 (2022)

Section

Electrochemical Engineering

How to Cite

Simulation of corrosion protection methods in reinforced concrete by artificial neural networks and fuzzy logic: Original scientific paper. (2022). Journal of Electrochemical Science and Engineering, 12(3), 511-527. https://doi.org/10.5599/jese.1220

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