Historical Buildings Dampness Analysis Using Electrical Tomography and Machine Learning Algorithms
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-02-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/5/1307 |
| _version_ | 1797417497732841472 |
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| author | Tomasz Rymarczyk Grzegorz Kłosowski Anna Hoła Jerzy Hoła Jan Sikora Paweł Tchórzewski Łukasz Skowron |
| author_facet | Tomasz Rymarczyk Grzegorz Kłosowski Anna Hoła Jerzy Hoła Jan Sikora Paweł Tchórzewski Łukasz Skowron |
| author_sort | Tomasz Rymarczyk |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T06:20:36Z |
| format | Article |
| id | doaj.art-477bee6b11ca4cc6a143509c540bd5a5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T06:20:36Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-477bee6b11ca4cc6a143509c540bd5a52023-12-03T11:48:19ZengMDPI AGEnergies1996-10732021-02-01145130710.3390/en14051307Historical Buildings Dampness Analysis Using Electrical Tomography and Machine Learning AlgorithmsTomasz Rymarczyk0Grzegorz Kłosowski1Anna Hoła2Jerzy Hoła3Jan Sikora4Paweł Tchórzewski5Łukasz Skowron6Institute of Computer Science and Innovative Technologies, University of Economics and Innovation in Lublin, 20-209 Lublin, PolandFaculty of Management, Lublin University of Technology, 20-618 Lublin, PolandFaculty of Civil Engineering, Wrocław University of Science and Technology, 50-370 Wrocław, PolandFaculty of Civil Engineering, Wrocław University of Science and Technology, 50-370 Wrocław, PolandInstitute of Computer Science and Innovative Technologies, University of Economics and Innovation in Lublin, 20-209 Lublin, PolandResearch & Development Centre Netrix S.A., 20-704 Lublin, PolandFaculty of Management, Lublin University of Technology, 20-618 Lublin, PolandThe 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.https://www.mdpi.com/1996-1073/14/5/1307machine learningelectrical tomographymoisture inspectiondampness analysisnondestructive evaluationneural networks |
| spellingShingle | Tomasz Rymarczyk Grzegorz Kłosowski Anna Hoła Jerzy Hoła Jan Sikora Paweł Tchórzewski Łukasz Skowron Historical Buildings Dampness Analysis Using Electrical Tomography and Machine Learning Algorithms Energies machine learning electrical tomography moisture inspection dampness analysis nondestructive evaluation neural networks |
| title | Historical Buildings Dampness Analysis Using Electrical Tomography and Machine Learning Algorithms |
| title_full | Historical Buildings Dampness Analysis Using Electrical Tomography and Machine Learning Algorithms |
| title_fullStr | Historical Buildings Dampness Analysis Using Electrical Tomography and Machine Learning Algorithms |
| title_full_unstemmed | Historical Buildings Dampness Analysis Using Electrical Tomography and Machine Learning Algorithms |
| title_short | Historical Buildings Dampness Analysis Using Electrical Tomography and Machine Learning Algorithms |
| title_sort | historical buildings dampness analysis using electrical tomography and machine learning algorithms |
| topic | machine learning electrical tomography moisture inspection dampness analysis nondestructive evaluation neural networks |
| url | https://www.mdpi.com/1996-1073/14/5/1307 |
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