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.