The purpose of this paper is to present a novel five‐electrode electrochemical corrosion sensor. Health degradation by the corrosion of steel in civil engineering is a persistent problem. Structural health monitoring (SHM) techniques, including embedded sensors, can greatly improve the quantification of the steel corrosion information, which can lead to promote assessments of structural safety and serviceability. To integrate the corrosion monitoring system in future, the corrosion sensor and the monitoring methods have been explored here in advance. Also, the corrosion monitoring system has been applied preliminarily in the investigation of reinforcing concrete (RC) beams.
First, a novel five‐electrode electrochemical corrosion sensor has been developed as the hardware to provide the platform for corrosion monitoring methods. Second, half‐cell potential of the RC beams has been measured before and after corrosion. Third, galvanostatic step method has been used to excite the steel‐concrete system, and the transient response of the system has been obtained and analyzed. Finally, wavelet transform algorithm has been established to analyze the electrochemical noise (EN) data of the steel bars in RC beams.
The results show that the corrosion sensor can be used effectively as the hardware to support the electrochemical measuring techniques. Much valuable information which is extracted by analyzing the potential response to the galvanostatic pulse excitation can be applied to determine the general corrosion state of the reinforcing steel. For pitting corrosion, the energy distribution plot of EN can be adopted as a benchmark method to identify the presence of the corrosion pit.
The paper provides the key techniques for a SHM system to realize corrosion monitoring of large‐scale RC structures in the future.
Qiao, G., Xiao, H. and Sun, G. (2011), "Identification of the reinforcing steel's corrosion state in RC beams based on electrochemical sensor", Sensor Review, Vol. 31 No. 3, pp. 218-227. https://doi.org/10.1108/02602281111139995
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