Poster: Application of LSTM Network with Multi-frequency Measurement Sequences in Electrical Tomography for Moisture Detection in Buildings

Damp walls are a significant problem that affects not only historical buildings. The effects of moisture inside walls include premature degradation of the structure and paintwork as well as health hazards for people staying inside the rooms (fungi, microorganisms, allergens). To effectively remove moisture, it is necessary to identify the areas where it occurs. Tomography is the only nondestructive method that allows for imaging the interior of walls. It is not common due to the low image resolution [1]. The aim of the research presented is to present a new concept of impedance tomography, taking into account many measurement sequences at different frequencies of electric current. A neural network with LSTM (Long Short-Term Memory) layers was used to transform the measurements into images. A comparison of the results of the new approach proves the advantage of the multi-frequency method over the traditional method, which brings closer the breakthrough moment in the dissemination of tomography as the main method of imaging moisture in walls.