Predictive Performance of Artificial Neural Network and Multiple Linear Regression Models in Predicting Adhesive Bonding Strength of Wood

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dc.authoridBARDAK, selahattin/0000-0001-9724-4762
dc.authoridAYDIN, AYTAC/0000-0001-7460-9618
dc.authoridbardak, timucin/0000-0002-1403-1049
dc.contributor.authorBardak, S.
dc.contributor.authorTiryaki, S.
dc.contributor.authorBardak, T.
dc.contributor.authorAydin, A.
dc.date.accessioned2025-03-23T19:44:03Z
dc.date.available2025-03-23T19:44:03Z
dc.date.issued2016
dc.departmentSinop Üniversitesi
dc.description.abstractThe purpose of this study was to develop artificial neural network (ANN) and multiple linear regression (MLR) models that are capable of predicting the bonding strength of wood based on moisture content, open assembly time and closed assembly time of the joints prior to pressing process. For this purpose, the experimental studies were conducted and the models based on the experimental results were set up. As a result of the experiments conducted, it was observed that bonding strength first increased and then decreased with increasing the wood moisture content and adhesive open assembly time. In addition, the increased closed assembly time caused a decrease in bonding strength of wood. The ANN results were compared with the results obtained from the MLR model to evaluate the models' predictive performance. It was found that the ANN model with the R (2) value of 97.7% and the mean absolute percentage error of 3.587% in test phase exhibits higher prediction accuracy than the MLR model. The comparison results confirm the feasibility of ANN model in terms of predictive performance. Consequently, it can be said that ANN is an effective tool in predicting wood bonding strength, and quite useful instead of costly and time-consuming experimental investigations.
dc.identifier.doi10.1007/s11223-017-9828-x
dc.identifier.endpage824
dc.identifier.issn0039-2316
dc.identifier.issn1573-9325
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85011857399
dc.identifier.scopusqualityQ4
dc.identifier.startpage811
dc.identifier.urihttps://doi.org/10.1007/s11223-017-9828-x
dc.identifier.urihttps://hdl.handle.net/11486/6858
dc.identifier.volume48
dc.identifier.wosWOS:000395207600010
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofStrength of Materials
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250323
dc.subjectartificial neural network
dc.subjectprediction
dc.subjectbonding strength
dc.subjectmultiple linear regression
dc.subjectmodel comparison
dc.titlePredictive Performance of Artificial Neural Network and Multiple Linear Regression Models in Predicting Adhesive Bonding Strength of Wood
dc.typeArticle

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