Historical Buildings Dampness Analysis Using Electrical Tomography and Machine Learning Algorithms

Tomasz Rymarczyk, Grzegorz Kłosowski, Anna Hoła, Jerzy Hoła, Jan Sikora, Paweł Tchórzewski and Łukasz Skowron
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Tomasz Rymarczyk: Institute of Computer Science and Innovative Technologies, University of Economics and Innovation in Lublin, 20-209 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

Energies, 2021, vol. 14, issue 5, 1-24

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.

Keywords: machine learning; electrical tomography; moisture inspection; dampness analysis; nondestructive evaluation; neural networks; elastic net (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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