Electrical impedance tomography (EIT) has been recently applied as a structural health monitoring (SHM) technique to many diﬀerent kinds of structures. In short, EIT is an algorithm that reconstructs the spatial conductivity response of a conductive body using only voltage measurement along its boundaries. For a conductive structure with its electrical properties being sensitive to damages and/or strains, mapping the distribution of its conductivity allows one to obtain its corresponding damage and/or strain distribution. To date, the EIT inverse problem has been solved using diﬀerent techniques. This study compared the performance of two diﬀerent approaches using four evaluation criteria. The first technique is based on EIDORS, which is an open-source EIT solver based on the maximum a posteriori (MAP) approach. It can rapidly, using a one-step linear approach, evaluate the relative impedance change of a given region when a baseline measurement (i.e., the response collected under its initial state) is provided. The second approach is a two-step iterative shrinkage thresholding (TwIST) method that compresses a signal’s sparsity in preserving sharp edges of an image. Both methods were evaluated using a 16-electrode 2D square shape with a simulated “point” damage at diﬀerent locations. The evaluation results suggested that TwIST outperforms MAP in terms of the resolution and accuracy of the reconstructed results, and MAP wins over TwIST in causing minor shape deformation and less ringing. Results from both methods exhibit position-dependency. These results are significant in promoting EIT becoming a powerful technique for in situ health monitoring.
Yingjun Zhao, Long Wang, Sumit Gupta, Kenneth J. Loh, and Martin Schagerl, "Comparison of electrical impedance tomography inverse solver approaches for damage sensing," Proc. SPIE 10169, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2017, 1016915 (Presented at SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring: March 28, 2017; Published: 19 April 2017); https://doi.org/10.1117/12.2260880.
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