Electrical resistance tomography (ERT) is gaining recognition as an effective, affordable, and nondestructive tool for monitoring and imaging concrete structures. This paper discusses ERT’s applications, including crack detection, moisture ingress monitoring, steel reinforcement assessment, and chloride level profiling within concrete. Recent advancements, such as time-lapse ERT and artificial intelligence (AI) integration, have enhanced image resolution and provided detailed data for infrastructure monitoring. However, challenges remain regarding the need for better spatial resolution, concrete-compatible electrodes, and integration with other nondestructive testing techniques. Addressing these issues will expand the applicability and reliability of the current ERT, making it an invaluable tool for infrastructure maintenance and monitoring. 

Electrical Resistance Tomography (ERT) for Concrete Structure Applications: A Review

Owing to the ill-conditioned nature of electrical resistivity tomography and the measurement error of the hardware equipment, the reconstructed resistivity distribution image often contains artifacts of varying degrees. Other soft-field imaging technologies, such as electrical impedance tomography and electrical capacitance tomography, also encounter artifacts. Artifacts interfere with the assessment of damaged areas. To eliminate the influence of artifacts on the reconstructed image, a novel artifact elimination algorithm called the fast artifact filtering (FAF) algorithm is proposed. Based on the calculation results of existing algorithms, such as the Newton’s one-step error reconstructor (NOSER) algorithm, the FAF algorithm can remove the damaged areas with low confidence from the potentially damaged areas and only retain the damaged areas with high confidence for final imaging. Several simulation models were used to test the effectiveness of the artifact elimination algorithm proposed in this study. The test results show that the number of artifacts in the final reconstructed image is significantly reduced after the NOSER algorithm is combined with the FAF algorithm. In addition, when the number of finite element model division elements was 4802, the refresh time of a single image increased by approximately 1 ms. A structural health monitoring test for hollow structure is provided. The results show that the FAF also performs well on the measured voltage data.

Fast artifact filtering algorithm for electrical resistivity tomography

Electrical resistance tomography (ERT) serves as a non-invasive, non-destructive, non-radioactive imaging technique. It has potential applications in industrial and biological imaging. This paper presents an optimized inverse algorithm, named Newton’s Constrained Reconstruction Method (NCRM), to detect damage in cementitious materials. Several constraints were utilized in the proposed algorithm to optimize initial parameters. The range and spatial distribution of conductivities within the sample were chosen as two main constraints. Two sets of numerical and a set of experimental voltage data were used to reconstruct conductivity distribution images based on this algorithm. To evaluate the quality of reconstructed images, two image quality evaluation indicators, correlation coefficient and position error were used. Results show that the proposed algorithm NCRM has the ability to enhance the reconstructed image quality with fewer artifacts and has better positioning accuracy.

Damage detection in cementitious materials with optimized absolute electrical resistance tomography

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Damage detection in cementitious materials with optimized absolute electrical resistance tomography