Condition assessment of reinforced concrete systems with fusion-bonded epoxy-coated rebars

Corrosion assessment of reinforced concrete (RC) structures with fusion-bonded-epoxy (FBE) coated steel rebars is a challenge because the common inspection methods and data cannot be applied or interpreted in the same way as that for the systems with uncoated rebars. If corrosion detection tools based on techniques such as half-cell potential (HCP), linear polarization resistance (LPR), or electrochemical impedance spectroscopy (EIS) are used for the assessment of systems with FBE coated steel rebars without considering the difference in the electrochemical conditions between coated and uncoated systems, then, the interpretation can result in the inability to detect ongoing corrosion. Therefore, the objective of this paper is to examine the suitability of these inspection methods and data to be applied to the RC systems with FBE coated steel rebars. For this, the suitability of test methods on HCP, LPR, and EIS for assessing corrosion conditions of RC structures was assessed using laboratory specimens and field structures. Field investigation using HCP shows that the HCP could not detect corrosion of FBE rebars unless the coating was severely disbonded due to corrosion of steel rebars. Also, the suitability of test methods based on HCP, LPR, and EIS was assessed by additional laboratory specimens. Although complex, only the EIS technique could reliably detect the corrosion conditions of the FBE coated steel rebars embedded in concrete. Therefore, a way forward to assess RC structures using EIS technique is proposed.

A Multi-Frequency Electrical Impedance Spectroscopy Technique of Artificial Neural Network-Based for the Static State of Charge

An artificial neural network (ANN) based multi-frequency electrical impedance spectroscopy (EIS) technique is proposed to estimate the static state of charge (SOC) of lithium-ion (Li-ion) battery in this paper. The proposed ANN-based multi-frequency EIS technique firstly collects the data of AC independence and their corresponding static SOC. With battery discharging current and multi-frequency EIS results, an ANN model is built and trained to estimate SOC. The measurement data is obtained using the potentiostats/galvanostats device, and the ANN is trained using the neural network toolbox in MATLAB. According to the experimental results, the performance of the proposed ANN model is dependent on the number of neurons in the hidden layer. The proposed method is validated with a set of random discharging processes. The high accuracy of SOC estimation is able to be achieved with the average error reduced to 1.92% when the number of neurons in the hidden layer is 35. Therefore, the proposed ANN-based multi-frequency EIS technique can be utilized to measure the static SOC of random discharge of Li-ion batteries.