The objective of this study was to implement a supervised machine learning method that utilizes the radial basis function neural network for 3D electrical impedance tomography conductivity distribution reconstruction of complex cellular lattice structures. This data-driven algorithm, which was trained by a variety of damaged cases, is significantly faster than conventional EIT while enabling greater accuracy of 3D conductivity distribution reconstruction. Both numerical simulations and experimental results are presented in this work, and the machine learning based EIT results are compared with those obtained using conventional EIT.
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