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Methodology of the quantitative assessment of the moisture content of saline brick walls in historic buildings using machine learning

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Języki publikacji
EN
Abstrakty
EN
Conducting moisture tests of brick walls in buildings under conservation protection is associated with many difficulties that result from the inability to freely interfere with historic tissue. The current paradigm of conducting such research, which assumes the use of just one non-destructive method, has many limitations that affect the accuracy of obtained results. Up-to-date research concerning an alternative non-invasive method, which allows reliable test results to be obtained in the case of the quantitative assessment of the moisture content of saline brick walls in historic buildings, has shown that it is possible to reliably assess such a moisture content using machine learning and two complementary non-destructive methods. In the article, the original methodology of such a quantitative assessment is described and presented in the form of block diagrams. The methodology consists of two stages. The first stage includes carrying out experimental and archival research in selected historical buildings to create a data set. The second stage involves generating a machine learning model for assessing the moisture content based on algorithms and the data collected in the first stage. The article is illustrated with an example of the application of the developed methodology to assess the moisture content of the brick walls of the Golden Gate building in Gdańsk. The presented example shows the reliability and practical usefulness of the developed methodology.
Rocznik
Strony
art. no. e141, 2023
Opis fizyczny
Bibliogr. 33 poz., fot., rys., wykr.
Twórcy
autor
  • Faculty of Civil Engineering, Department of Materials Engineering and Construction Processes, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
Bibliografia
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  • 5. Martínez-Garrido MI, Fort R, Gómez-Heras M, Valles-Iriso J, Varas-Muriel MJ. A comprehensive study for moisture control in cultural heritage using non-destructive techniques. J Appl Geo- phys. 2018. https://doi.org/10.1016/j.jappgeo.2018.03.008.
  • 6. Válek J, Kruschwitz S, Wöstmann J, Kind T, Valach J, Köpp Ch, Lesák J. Nondestructive investigation of wet building material: multimethodical approach. J Perform Constr Facil. 2010. https:// doi.org/10.1061/ASCECF.1943-5509.0000056.
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  • 8. Adamowski J, Hoła J, Matkowski Z. Probleme und Losungen beim Feuchtigkeitsschutz des Mauerwerks von Baudenkmalern am Beispiel zweier grosser Barockbauten in Wroclaw. Bautechnik. 2005. https://doi.org/10.1002/bate.200590148.
  • 9. Rokiel M. Waterproofing in construction (in Polish), Warszawa: Grupa MEDIUM; 2006; ISBN: 978–83–64094–63–7.
  • 10. Rosina E, Ludwig N, Rosi L. Optimal environmental conditions to detect moisture in ancient buildings: case studies in Northern Italy. In: SPIE Conference Proceedings, Thermosense Doi: https:// doi.org/10.1117/12.304728.
  • 11. Hoła A, Matkowski Z, Hoła J. In-situ moisture assessment in external walls of historic building using non-destructive meth- ods. Procedia Engineering. 2017. https://doi.org/10.1016/j.proeng. 2017.02.041.
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  • 18. Balík L, Kudrnáčová L, Pavlík Z, Černý R. Application of infra- red thermography in complex moisture inspection of the Sche- bek Palace. AIP Conf Proc. 2017. https:// doi. org/ 10. 1063/1. 4994482.
  • 19. Muradov M, Kot P, Markiewicz J, Łapiński S, Tobiasz A, Onisk K, Shaw A, Hashim K, Zawieska D, Mohi-Ud-Din G. Non- destructive system for in-wall moisture assessment of cultural heritage buildings. Measurement. 2022. https://doi.org/10.1016/j. measurement.2022.111930.
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  • 23. Hoła A, Czarnecki S. Brick wall moisture evaluation in historic buildings using neural networks. Autom Constr. 2022. https://doi. org/10.1016/j.autcon.2022.104429.
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  • 25 Espinosa RM, Franke L, Deckelmann G. Phase changes of salts in porous materials. Crystallization, hydration and deliquescence. Construct Build Mater. 2008. https://doi.org/10.1016/j.conbuild- mat.2007.05.005.
  • 26. Raimondo M, Dondi M, Guardini G, Mazzanti F. Predicting the initial rate of water absorption in clay brick. Constr Build Mater. 2009. https://doi.org/10.1016/j.conbuildmat.2009.01.009.
  • 27. Kubik J. Moisture flow in building materials (in Polish). Opole: Oficyna Wydawnicza Politechniki Opolskiej; 2000; ISBN: 83–88492–56-X.
  • 28. Alsabry A. Dynamics of capillary rising in building walls (in Pol- ish) [PDF file]. Przegląd Budowlany 2010;9:46–48. Accessed 30 Sep 2022; https://www.przegladbudowlany.pl/2010/09/2010-09- PB-46-48_Alsabry.pdf.
  • 29. Monczyński B. Diagnosis of damp wall structures (in Polish). Izolacje 2019;1. Accessed 18 Sep 2022; https://www.izolacje. com.pl/artykul/osuszanie-budynkow/188809,diagnostyka-zawil goconych-konstrukcji-murowych.
  • 30. Sun H, Burton HV, Huang H. Machine learning applications for building structural design and performance assessment: State-of- the-art review. J Building Eng. 2021. https://doi.org/10.1016/j. jobe.2020.101816.
  • 31. ApostolopoulouM ArmaghaniDJ, BakolasA DouvikaMG, Moro- poulouA AsterisPG. Compressivestrengthofnaturalhydrauli- climemortarsusingsoftcomputingtechniques. Procedia Structur- alIntegrity. 2019. https://doi.org/10.1016/j.prostr.2019.08.122.
  • 32. Asteris PG, Douvika MG, Karamani CA, Skentou AD, Chlich- lia K, Cavaleri L, Daras T, Armaghani DJ, Zaoutis TE. A novel heuristic algorithm for the modeling and risk assessment of the COVID19 pandemic phenomenon. Comput Model Eng Sci. 2020. https://doi.org/10.32604/cmes.2020.013280.
  • 33. Armaghani DJ, Asteris PG. A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materi- als compressive strength. Neural Comput Appl. 2020. https://doi. org/10.1007/s00521-020-05244-4.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-94479287-573e-4523-80f5-8414ca1921a6
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