Predicting effects of selected impregnation processes on the observed bending strength of wood, with use of data mining models

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dc.contributor.authorBardak, Selahattin
dc.contributor.authorBardak, Timuçin
dc.contributor.authorPeker, Hüseyin
dc.contributor.authorSözen, Eser
dc.contributor.authorÇabuk, Yıldız
dc.date.accessioned2025-07-10T12:14:37Z
dc.date.available2025-07-10T12:14:37Z
dc.date.issued2021
dc.departmentAÇÜ, Orman Fakültesi, Orman Endüstri Mühendisliği Bölümü
dc.description.abstractWood materials have been used in many products such as furniture, stairs, windows, and doors for centuries. There are differences in methods used to adapt wood to ambient conditions. Impregnation is a widely used method of wood preservation. In terms of efficiency, it is critical to optimize the parameters for impregnation. Data mining techniques reduce most of the cost and operational challenges with accurate prediction in the wood industry. In this study, three data-mining algorithms were applied to predict bending strength in impregnated wood materials (Pinus sylvestris L. and Millettia laurentii). Models were created from real experimental data to examine the relationship between bending strength, diffusion time, vacuum duration, and wood type, based on decision trees (DT), random forest (RF), and Gaussian process (GP) algorithms. The highest bending strength was achieved with wenge (Millettia laurentii) wood in 10 bar vacuum and the diffusion condition during 25 min. The results showed that all algorithms are suitable for predicting bending strength. The goodness of fit for the testing phase was determined as 0.994, 0.986, and 0.989 in the DT, RF, and GP algorithms, respectively. Moreover, the importance of attributes was determined in the algorithms.
dc.identifier.endpage4904
dc.identifier.issn19302126
dc.identifier.issue3
dc.identifier.scopuss2.0-85123642344
dc.identifier.scopusqualityQ3
dc.identifier.startpage4891
dc.identifier.urihttps://hdl.handle.net/11494/5744
dc.identifier.volume16
dc.indekslendigikaynakScopus
dc.institutionauthorPeker, Hüseyin
dc.language.isoen
dc.publisherNorth Carolina State University
dc.relation.ispartofBioResources
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectBending strength
dc.subjectData mining
dc.subjectMechanical properties
dc.subjectOptimization
dc.subjectWood material
dc.titlePredicting effects of selected impregnation processes on the observed bending strength of wood, with use of data mining models
dc.typeArticle

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