An integrated texture analysis and machine learning approach for durability assessment of lightweight cement composites with hydrophobic coatings modified by nanocellulose
Artykuł w czasopiśmie
MNiSW
200
Lista 2021
| Status: | |
| Autorzy: | Barnat-Hunek Danuta, Omiotek Zbigniew, Szafraniec Małgorzata, Dzierżak Róża |
| Dyscypliny: | |
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| Rok wydania: | 2021 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Wolumen/Tom: | 179 |
| Numer artykułu: | 109538 |
| Strony: | 1 - 20 |
| Impact Factor: | 5,131 |
| Web of Science® Times Cited: | 13 |
| Scopus® Cytowania: | 26 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Finansowanie: | This work was financially supported by the Ministry of Education and Science – Poland, within the grant number FD-IL-003, FD-EE-315 and FD-EE-302. |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| The aim of the study was to determine a set of image texture features of the lightweight cementitious composites (LLC) with hydrophobic coatings modified with nanocellulose and use them to assess the materials' durability. A novel method based on a combination of image texture analysis and machine learning methods was proposed. Textural features were extracted from the images obtained with a scanning microscope. The best classification model was built by the Support Vector Machine method using 16 features selected by the Sequential Forward Selection algorithm. The model recognizes one of the four ranges of the contact angle, which is closely related to the degree of resistance of the analyzed material, with an accuracy of 82%. The results obtained show a relationship between the effectiveness of hydrophobic coatings in LCC and images of their surfaces. This relationship can be used with machine learning methods for conducting strength diagnostics of building materials |
