Applying the Machine Learning Method to Improve Calibration Quality of Time Domain Reflectometry Measuring Technique

• Autorzy: Mikušová Dominika , Suchorab Zbigniew , Paśnikowska-Łukaszuk Magdalena , Zaburko Jacek , Trník Anton

Identyfikatory

DOI

10.12913/22998624/187007

• Języki publikacji: EN

Abstrakty

EN

The article presents the application of the time domain reflectometry (TDR) technique for measuring the moisture of porous building materials used in construction. The work is focused on using the potential of artificial intelligence to improve the quality of TDR measurements through a new approach to the interpretation of data obtained from the TDR readings. Machine learning is a data analysis technique, used nowadays in many scientific disciplines. The authors performed a measurement data analysis using the artificial intelligence algorithms to assess moisture of aerated concrete samples tested with a TDR multimeter using two non-invasive sensors which differ in thickness. Data analysis was carried out using supervised machine learning to analyse a series of reflectograms obtained during the measurement. For the data achieved by the classical and machine learning method interpretation, correlation analysis was conducted to confirm the potential of artificial intelligence to improve the quality of TDR measurement. The summary of the work discusses the obtained analytical results and highlights the effectiveness of moisture assessment using the Gaussian Process Regression method, which allowed achieving a level of 0.2 - 0.3% of the RMSE errors value, which is about 10 times lower than the traditional approach.

Słowa kluczowe

EN

time domain reflectometry; artificial intelligence; machine learning; moisture content; building materials

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2024
• Tom: Vol. 18, no 3
• Strony: 270--279
• Opis fizyczny: Bibliogr. 35 poz., fig., tab.

Twórcy

autor

Mikušová Dominika

• Department of Water Supply and Wastewater Disposal, Faculty of Environmental Engineering, Lublin University of Technology, Nadbystrzycka 40B, 20-618 Lublin, Poland

• https://orcid.org/0000-0002-3862-7509

autor

Suchorab Zbigniew

• Department of Water Supply and Wastewater Disposal, Faculty of Environmental Engineering, Lublin University of Technology, Nadbystrzycka 40B, 20-618 Lublin, Poland

• https://orcid.org/0000-0001-8658-864X

autor

Paśnikowska-Łukaszuk Magdalena

• Department of Technical Informatics, Faculty of Mathematics and Information Technology, Lublin University of Technology, Nadbystrzycka 38, 20-618 Lublin, Poland

• https://orcid.org/0000-0002-3479-6188

autor

Zaburko Jacek

• Department of Technical Informatics, Faculty of Mathematics and Information Technology, Lublin University of Technology, Nadbystrzycka 38, 20-618 Lublin, Poland

• https://orcid.org/0000-0001-7762-2770

autor

Trník Anton

• Department of Physics, Faculty of Natural Sciences and Informatics, Constantine the Philosopher University in Nitra, Tr. A. Hlinku 1, 94901 Nitra, Slovakia

• https://orcid.org/0000-0001-9923-8366

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).