Use of the double-stage LSTM network in electrical tomography for 3D wall moisture imaging

Abstract

This paper deals with a new two-step algorithmic method to improve the accuracy of imaging moisture in building walls using electrical impedance tomography (EIT). The problem of assessing dampness in buildings is important both from the point of view of the national cultural heritage, economics, safety, and aesthetics of real estate and the health of people inside buildings. The main impediment to EIT development is solving the ill-posed and inverse problem that makes obtaining reliable images of high-resolution moisture distribution difficult. To resolve this problem, we propose the new double-stage neural network approach. First, the method's originality is determined by using the second neural network, which improves the images obtained thanks to the first network. Using the first network, training images are generated, constituting inputs for the second network, while both neural networks are trained on a similar set of pattern output images. The approach to input measurements and images is also original, which, thanks to the conversion into vectors (sequences), made it possible to use the LSTM (long short-term memory) network. The research proved the effectiveness of the new method.