Using Machine Learning in Electrical Tomography for Building Energy Efficiency through Moisture Detection

Grzegorz Kłosowski (), Anna Hoła, Tomasz Rymarczyk, Mariusz Mazurek, Konrad Niderla and Magdalena Rzemieniak
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Grzegorz Kłosowski: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Anna Hoła: Faculty of Civil Engineering, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
Tomasz Rymarczyk: Institute of Computer Science and Innovative Technologies, WSEI University, 20-209 Lublin, Poland
Mariusz Mazurek: Institute of Philosophy and Sociology of the Polish Academy of Sciences, 00-330 Warsaw, Poland
Konrad Niderla: Institute of Computer Science and Innovative Technologies, WSEI University, 20-209 Lublin, Poland
Magdalena Rzemieniak: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland

Energies, 2023, vol. 16, issue 4, 1-31

Abstract: 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.

Keywords: building energy saving; electrical tomography; moisture imaging; nondestructive evaluation; machine learning; neural networks; image processing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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