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Historical Buildings Dampness Analysis Using Electrical Tomography and Machine Learning Algorithms

Author

Listed:
  • 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)

  • Jerzy 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)

  • Paweł Tchórzewski

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

  • Łukasz Skowron

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

Abstract

The article deals with the problem of detecting moisture in the walls of historical buildings. As part of the presented research, the following four methods based on mathematical modeling and machine learning were compared: total variation, least-angle regression, elastic net, and artificial neural networks. Based on the simulation data, the systems for the reconstruction of “pixel by pixel” tomographic images were trained. In order to test the reconstructive algorithms obtained during the research, images were generated based on real measurements and simulation cases. The method comparison was performed on the basis of three indicators: mean square error, relative image error, and image correlation coefficient. The above indicators were applied to four selected variants that corresponded to various parts of the walls. The variants differed in the dimensions of the tested wall sections, the number of electrodes used, and the resolution of the 3D image meshes. In all analyzed variants, the best results were obtained using the elastic net algorithm. In addition, all machine learning methods generated better tomographic reconstructions than the classic Total Variation method.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1307-:d:507074
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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.
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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.
    2. 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.
    3. 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.
    4. Nikolaos M. Manousakis, 2022. "Advanced Electrical Measurements Technologies," Energies, MDPI, vol. 15(9), pages 1-6, April.
    5. Piotr Łapka & Łukasz Cieślikiewicz, 2021. "Efficiency Comparison between Two Masonry Wall Drying Devices Using In Situ Data Measurements," Energies, MDPI, vol. 14(21), pages 1-14, November.
    6. Jan Porzuczek, 2021. "Multifrequency Impedance Tomography System for Research on Environmental and Thermal Processes," Energies, MDPI, vol. 14(19), pages 1-17, October.
    7. Michał Styła & Bartłomiej Kiczek & Grzegorz Kłosowski & Tomasz Rymarczyk & Przemysław Adamkiewicz & Dariusz Wójcik & Tomasz Cieplak, 2022. "Machine Learning-Enhanced Radio Tomographic Device for Energy Optimization in Smart Buildings," Energies, MDPI, vol. 16(1), pages 1-20, December.
    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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