Deep learned Electrical Resistance Tomography Applications in Structural Health Monitoring

In recent studies, electrical resistance tomography (ERT) has been explored as a non-destructive testing imaging modality in conjunction with structural health monitoring (SHM). This imaging modality has been shown to be able to locate cracks in cement-based materials as well as reconstruct strain and stress distributions in nano-composite materials. However, due to the ill-conditioned nature of the ERT inverse problem, the computational cost of solving such problems can be high. In order to reduce the overall computational cost of solving the ERT inverse problem in practical applications, we propose using a deep learning approach to address this challenge. The deep-learned ERT frameworks have been successfully implemented and validated using simulation and experimental data for various materials relevant to SHM. The results indicate that the deep-learned ERT frameworks are feasible for implementation in SHM applications.