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Wet foundations and walls of buildings significantly increase the energy consumption of buildings, and the drying of walls is one of the priority activities as part of thermal modernization, along with the insulation of the facades. This article discusses the research findings of detecting moisture decomposition within building walls utilizing electrical impedance tomography (EIT) and deep learning techniques. In particular, the focus was on algorithmic models whose task is transforming voltage measurements into spatial EIT images. Two homogeneous deep learning networks were used: CNN (Convolutional Neural Network) and LSTM (Long-Short Term Memory). In addition, a new heterogeneous (hybrid) network was built with LSTM and CNN layers. Based on the reference reconstructions' simulation data, three separate neural network algorithmic models: CNN, LSTM, and the hybrid model (CNN+LSTM), were trained. Then, based on popular measures such as mean square error or correlation coefficient, the quality of the models was assessed with the reference images. The obtained research results showed that hybrid deep neural networks have great potential for solving the tomographic inverse problem. Furthermore, it has been proven that the proper joining of CNN and LSTM layers can improve the effect of EIT reconstructions. [ABSTRACT FROM AUTHOR]

• The use of a double-stage neural network in electrical tomography. • Using an autoencoder-like LSTM architecture to improve image quality. • A new way of using LSTM neural networks for sequential data. • Original algorithmic approach that improves the detection of moisture in masonry walls. 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. [ABSTRACT FROM AUTHOR]