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

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Listed:
  • 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
    Research & Development Centre Netrix S.A., 20-704 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)

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.

Suggested Citation

  • Grzegorz Kłosowski & Anna Hoła & Tomasz Rymarczyk & Mariusz Mazurek & Konrad Niderla & Magdalena Rzemieniak, 2023. "Using Machine Learning in Electrical Tomography for Building Energy Efficiency through Moisture Detection," Energies, MDPI, vol. 16(4), pages 1-31, February.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1818-:d:1065738
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    References listed on IDEAS

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    1. Mirco Andreotti & Dario Bottino-Leone & Marta Calzolari & Pietromaria Davoli & Luisa Dias Pereira & Elena Lucchi & Alexandra Troi, 2020. "Applied Research of the Hygrothermal Behaviour of an Internally Insulated Historic Wall without Vapour Barrier: In Situ Measurements and Dynamic Simulations," Energies, MDPI, vol. 13(13), pages 1-22, July.
    2. Tomasz Rymarczyk & Grzegorz Kłosowski & Anna Hoła & Jan Sikora & Tomasz Wołowiec & Paweł Tchórzewski & Stanisław Skowron, 2021. "Comparison of Machine Learning Methods in Electrical Tomography for Detecting Moisture in Building Walls," Energies, MDPI, vol. 14(10), pages 1-22, May.
    3. Małgorzata Jasiulewicz-Kaczmarek & Katarzyna Antosz & Ryszard Wyczółkowski & Dariusz Mazurkiewicz & Bo Sun & Cheng Qian & Yi Ren, 2021. "Application of MICMAC, Fuzzy AHP, and Fuzzy TOPSIS for Evaluation of the Maintenance Factors Affecting Sustainable Manufacturing," Energies, MDPI, vol. 14(5), pages 1-30, March.
    4. Grzegorz Kłosowski & Anna Hoła & Tomasz Rymarczyk & Łukasz Skowron & Tomasz Wołowiec & Marcin Kowalski, 2021. "The Concept of Using LSTM to Detect Moisture in Brick Walls by Means of Electrical Impedance Tomography," Energies, MDPI, vol. 14(22), pages 1-20, November.
    5. Tomasz Rymarczyk & Grzegorz Kłosowski & Anna Hoła & Jerzy Hoła & Jan Sikora & Paweł Tchórzewski & Łukasz Skowron, 2021. "Historical Buildings Dampness Analysis Using Electrical Tomography and Machine Learning Algorithms," Energies, MDPI, vol. 14(5), pages 1-24, February.
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    Cited by:

    1. Ruwen Zhao & Chuanpei Xu & Zhibin Zhu & Wei Mo, 2024. "A Blockchain-Based Secure Sharing Scheme for Electrical Impedance Tomography Data," Mathematics, MDPI, vol. 12(7), pages 1-19, April.

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