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Comparison of Machine Learning Methods in Electrical Tomography for Detecting Moisture in Building Walls

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  • Tomasz Rymarczyk

    (Institute of Computer Science and Innovative Technologies, University of Economics and Innovation in Lublin, 20-209 Lublin, Poland
    Research & Development Centre Netrix S.A., 20-704 Lublin, Poland)

  • 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)

  • Jan Sikora

    (Institute of Computer Science and Innovative Technologies, University of Economics and Innovation in Lublin, 20-209 Lublin, Poland)

  • Tomasz Wołowiec

    (Institute of Computer Science and Innovative Technologies, University of Economics and Innovation in Lublin, 20-209 Lublin, Poland)

  • Paweł Tchórzewski

    (Research & Development Centre Netrix S.A., 20-704 Lublin, Poland)

  • Stanisław Skowron

    (Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland)

Abstract

This paper presents the results of research on the use of machine learning algorithms and electrical tomography in detecting humidity inside the walls of old buildings and structures. The object of research was a historical building in Wrocław, Poland, built in the first decade of the 19th century. Using the prototype of an electric tomograph of our own design, a number of voltage measurements were made on selected parts of the building. Many algorithmic methods have been preliminarily analyzed. Ultimately, the three models based on machine learning were selected: linear regression with SVM (support vector machine) learner, linear regression with least squares learner, and a multilayer perceptron neural network. The classical Gauss–Newton model was also used in the comparison. Both the experiments based on real measurements and simulation data showed a higher efficiency of machine learning methods than the Gauss–Newton method. The tomographic methods surpassed the point methods in measuring the dampness in the walls because they show a spatial image of the interior and not separate points of the examined cross-section. Research has shown that the selection of a machine learning model has a large impact on the quality of the results. Machine learning has a greater potential to create correct tomographic reconstructions than traditional mathematical methods. In this research, linear regression models performed slightly worse than neural networks.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2777-:d:553011
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    References listed on IDEAS

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    1. 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.
    2. 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. Tao Liu & Jiayuan Yu & Yuanjin Zheng & Chao Liu & Yanxiong Yang & Yunfei Qi, 2022. "A Nonlinear Multigrid Method for the Parameter Identification Problem of Partial Differential Equations with Constraints," Mathematics, MDPI, vol. 10(16), pages 1-12, August.
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    3. 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.
    4. Bartosz Przysucha & Dariusz Wójcik & Tomasz Rymarczyk & Krzysztof Król & Edward Kozłowski & Marcin Gąsior, 2023. "Analysis of Reconstruction Energy Efficiency in EIT and ECT 3D Tomography Based on Elastic Net," Energies, MDPI, vol. 16(3), pages 1-22, February.
    5. Grzegorz Kłosowski & Tomasz Rymarczyk & Konrad Niderla & Magdalena Rzemieniak & Artur Dmowski & Michał Maj, 2021. "Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography," Energies, MDPI, vol. 14(21), pages 1-20, November.
    6. Tomasz Rymarczyk & Krzysztof Król & Edward Kozłowski & Tomasz Wołowiec & Marta Cholewa-Wiktor & Piotr Bednarczuk, 2021. "Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks," Energies, MDPI, vol. 14(23), pages 1-35, December.
    7. Dariusz Majerek & Tomasz Rymarczyk & Dariusz Wójcik & Edward Kozłowski & Magda Rzemieniak & Janusz Gudowski & Konrad Gauda, 2021. "Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography," Energies, MDPI, vol. 14(22), pages 1-19, November.
    8. 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.

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