Electrical Impedance Tomography (EIT) Holds Promise for Structural Analysis of Buildings
Electrical impedance tomography (EIT), a non-destructive imaging technique, could revolutionize the field of structural analysis in buildings. By visualizing the interior of materials, EIT provides valuable insights into the health and integrity of cement-based structures. Unlike traditional imaging methods such as X-ray imaging or computed tomography, EIT offers a low-cost and less cumbersome approach without the need for large magnets or radiation.
However, accurately reconstructing the obtained information as images remains a challenge in EIT. Existing algorithms have limitations in terms of accuracy, particularly when faced with previously unseen data. To address this issue, a team of researchers from Tokyo University of Science (TUS) and Ritsumeikan University in Japan has developed a novel hybrid approach called AND, combining the benefits of the iterative Gauss-Newton (IGN) method and one-dimensional convolutional neural networks (1D-CNN).
The innovative AND method performs two-dimensional logical operations on multiple EIT images to detect small foreign objects within materials. In a study conducted by the research team, the AND method demonstrated higher accuracy in reconstructing the position and size of foreign objects compared to IGN and 1D-CNN methods. The hybrid approach proved particularly effective when the size of the foreign object was small, reducing the size error to less than 1/6th of that produced by conventional EIT methods.
Dr. Takashi Ikuno, Associate Professor at TUS, highlights the significance of this research for disaster prevention and the analysis of existing structures. The new method not only enhances the application of EIT as a non-destructive testing method but also contributes to preventing building collapses. Dr. Ikuno emphasizes the importance of the AND method in improving the resolution for detecting the size and position of foreign particles.
The research team’s findings, published in the journal AIP Advances, present a significant advancement in EIT technology, potentially making it an essential detection technique for preventing building collapses. The proposed EIT reconstruction method offers advantages in terms of cost and equipment size, allowing for easier and more regular assessment of building health. It could also facilitate rapid safety screenings following earthquakes or explosions.
Additionally, the researchers identified another approach to enhance the accuracy of EIT by altering the current injection pattern and combining it with other non-destructive evaluation techniques. This avenue of research will be a focus of their future work.
The potential for EIT to contribute to the field of structural analysis is vast. Its ability to provide non-destructive imaging at a lower cost and with reduced complexity positions it as a valuable tool for building inspections and maintenance. The AND method, with its increased accuracy in the detection of foreign objects, offers a new level of precision in the assessment of building health.
As further research and development are conducted, the field of EIT is likely to witness significant growth. This technology has the potential to transform the way we monitor and analyze buildings, ensuring their durability, safety, and longevity. By combining the power of machine learning algorithms with the benefits of EIT, researchers are paving the way for a more advanced and reliable method of structural health monitoring.
