Predicting Effects of Selected Impregnation Processes on the Observed Bending Strength of Wood, with Use of Data Mining Models

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dc.authoridBARDAK, selahattin/0000-0001-9724-4762
dc.contributor.authorBardak, Selahattin
dc.contributor.authorBardak, Timucin
dc.contributor.authorPeker, Huseyin
dc.contributor.authorSozen, Eser
dc.contributor.authorCabuk, Yildiz
dc.date.accessioned2025-03-23T19:30:10Z
dc.date.available2025-03-23T19:30:10Z
dc.date.issued2021
dc.departmentSinop Üniversitesi
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.doi10.15376/biores.16.3.4891-4904
dc.identifier.endpage4904
dc.identifier.issn1930-2126
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85123642344
dc.identifier.scopusqualityQ3
dc.identifier.startpage4891
dc.identifier.urihttps://doi.org/10.15376/biores.16.3.4891-4904
dc.identifier.urihttps://hdl.handle.net/11486/5033
dc.identifier.volume16
dc.identifier.wosWOS:000771534500001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherNorth Carolina State Univ Dept Wood & Paper Sci
dc.relation.ispartofBioresources
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250323
dc.subjectWood material
dc.subjectBending strength
dc.subjectMechanical properties
dc.subjectData mining
dc.subjectOptimization
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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