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