Electrochemical Deposition Treatment (EDT) as a Comprehensive Rehabilitation Method for Corrosion-Induced Deterioration in Concrete with Various Severity Levels

Evaluation of Early Concrete Damage Caused by Chloride-Induced Steel Corrosion Using a Deep Learning Approach Based on RNN for Ultrasonic Pulse Waves

The objective of this study is to explore the feasibility of using ultrasonic pulse wave measurements as an early detection method for corrosion-induced concrete damages. A series of experiments are conducted using concrete cube specimens, at a size of 200 mm, with a reinforcing steel bar (rebar) embedded in the center. The main variables include the water-to-cement ratio of the concrete (0.4, 0.5, and 0.6), the diameter of the rebar (10 mm, 13 mm, 19 mm, and 22 mm), and the corrosion level (ranging from 0% to 20% depending on rebar diameter). The impressed current technique is used to accelerate corrosion of rebars in concrete immersed in a 3% NaCl solution. Ultrasonic pulse waves are collected from the concrete specimens using a pair of 50 kHz P-wave transducers in the through-transmission configuration before and after the accelerated corrosion test. Deep learning techniques, specifically three recurrent neural network (RNN) models (long short-term memory, gated recurrent unit, and bidirectional long short-term memory), are utilized to develop a classification model for early detection of concrete damage due to rebar corrosion. The performance of the RNN models is compared to conventional ultrasonic testing parameters, namely ultrasonic pulse velocity and signal consistency. The results demonstrate that the RNN method outperforms the other two methods. Among the RNN methods, the bidirectional long short-term memory RNN model had the best performance, achieving an accuracy of 74% and a Cohen's kappa coefficient of 0.48. This study establishes the potentiality of utilizing deep learning of ultrasonic pulse waves with RNN models for early detection of concrete damage associated with steel corrosion.

New Self-Repairing System for Brittle Matrix Composites Using Corrosion-Induced Intelligent Fiber

Brittle matrix composites such as concrete are susceptible to damage in the form of cracks. Most of the current self-repair and self-healing techniques have repair limits on crack widths or high costs of an external stimulator, or have an unfavorable effect on the composite’s strength. This paper proposes a new concept of corrosion-induced intelligent fiber (CIF) and a new self-repairing system that uses the CIFs to close cracks in brittle matrix composites within a corrosive environment without external help, and without compromising the strength. The CIF comprises an inner core fiber and an outer corrodible coating that are in equilibrium, with the core fiber in tension and the corrodible coating in compression. The preparation steps and shape recovery mechanism of the CIF and the self-repair mechanism of the CIF composites are explained. Based on these concepts, this paper also describes several mechanical models built to predict the magnitude of pre-stress stored in the core fiber, and the maximum pre-stress released to the matrix composites, and the minimum length of the reliable anchor ends of CIF. The sample calculation results show that the recovery strain was 0.5% for the CIF with the steel core fiber and 12.7% for the CIF with the nylon core fiber; the maximum crack closing force provided by the CIF to concrete can be increased by increasing the amount of the CIFs in concrete and the initial tensile stress of the core fiber. This paper provides some suggestions for enhancing the self-repair capability of brittle composites in complex working environments.