The objective of this study is to review, evaluate, and compare the existing research and practices on electrical resistivity as a nondestructive technique in evaluating chloride-induced deterioration of reinforced concrete elements in buildings and civil infrastructure systems. First, this paper summarizes the different measurement techniques for gathering electrical resistivity (ER) values on concrete. Second, comparison analyses are performed to review the correlation of ER to different parameters representing corrosive environment and activity of steel corrosion in concrete, such as degree of water saturation, chloride penetration and diffusivity, and corrosion rate. In addition, this research enumerates and individually discusses the different environmental and interference factors that are not related to the electrochemical process of steel corrosion in concrete but directly affect the ER measurements, including temperature, the presence of steel reinforcement, cracks and delamination defects, specimen geometry, and concrete composition. Lastly and most importantly, discussions are made to determine the current gap of knowledge, to improve the utilization of this method in field and laboratory measurements, and future research.

The main objectives of this research are to evaluate the effects of delamination defects on the measurement of electrical resistivity of reinforced concrete slabs through analytical and experimental studies in the laboratory, and to propose a practical guide for electrical resistivity measurements on concrete with delamination defects. First, a 3D finite element model was developed to simulate the variation of electric potential field in concrete over delamination defects with various depths and lateral sizes. Second, for experimental studies, two reinforced concrete slab specimens (1500 mm (width) by 1500 mm (length) by 300 mm (thickness)) with artificial delamination defects of various dimensions and depths were fabricated. Third, the electrical resistivity of concrete over delamination defects in the numerical simulation models and the two concrete slab specimens were evaluated by using a 4-point Wenner probe in accordance with AASHTO (American Association of State Highway and Transportation Office) T-358. It was demonstrated from analytical and experimental studies in this study that shallow (50 mm depth) and deep (250 mm depth) delamination defects resulted in higher and lower electrical resistivity (ER) values, respectively, as compared to measurements performed on solid concrete locations. Furthermore, the increase in size of shallow defects resulted in an increase in concrete resistivity, whereas the increase in sizes of deep delamination defects yielded opposite results. In addition, measurements done directly above the steel reinforcements significantly lowered ER values. Lastly, it was observed from experimental studies that the effect of delamination defects on the values of electrical resistivity decreases as the saturation level of concrete increases.

The main objectives of this study are to evaluate the effect of geometrical constraints of plain concrete and reinforced concrete slabs on the Wenner four-point concrete electrical resistivity (ER) test through numerical and experimental investigation and to propose measurement recommendations for laboratory and field specimens. First, a series of numerical simulations was performed using a 3D finite element model to investigate the effects of geometrical constraints (the dimension of concrete slabs, the electrode spacing and configuration, and the distance of the electrode to the edges of concrete slabs) on ER measurements of concrete. Next, a reinforced concrete slab specimen (1500 mm (width) by 1500 mm (length) by 300 mm (thickness)) was used for experimental investigation and validation of the numerical simulation results. Based on the analytical and experimental results, it is concluded that measured ER values of regularly shaped concrete elements are strongly dependent on the distance-to-spacing ratio of ER probes (i.e., distance of the electrode in ER probes to the edges and/or the bottom of the concrete slabs normalized by the electrode spacing). For the plain concrete, it is inferred that the thickness of the concrete member should be at least three times the electrode spacing. In addition, the distance should be more than twice the electrode spacing to make the edge effect almost negligible. It is observed that the findings from the plain concrete are also valid for the reinforced concrete. However, for the reinforced concrete, the ER values are also affected by the presence of reinforcing steel and saturation of concrete, which could cause disruptions in ER measurements

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.