Lamine kaplama kereste (LVL) rutubetinin basınç direnci üzerine etkisinin yapay zekâ ile belirlenmesi

Öz

Yapı sektöründe kullanılan ahşap malzemeler, kullanım yerine bağlı olarak farklı yükleme çeşitlerine ve farklı dirençlere maruz kalmaktadır. Yükleme türüne uygun materyal kullanımı güvenlik, performans ve maliyet gibi önemli faktörleri etkilemektedir. Yapı sektöründe kullanılan ahşap materyallerde diğer bir önemli husus, odun-su ilişkileridir. Rutubet, odunun fiziksel, mekanik ve teknolojik (sertlik, aşınma) özellikleri üzerinde önemli değişikliklere neden olmaktadır. Bu çalışmada, soyma işlemi ile elde edilen 2 mm kayın (Fagus orientalis L.) kaplamlardan 5 katmanlı LVL (Laminated Veneer Lumber) üretimi gerçekleştirilmiştir. Üretilen LVL’ler dört farklı nem (% 0, % 12, % 18 ve % 25) değerinde ve liflere dik ve parallel olmak üzere iki farklı yönde basınç direncine tabi tutulmuştur. Belirtilen rutubet değerlerinden elde edilen verilerden yararlanılarak yapay zeka ile diğer rutubet miktarlarındaki basınç direnci değerleri tahmin edilmiştir. Tahminlerde Yapay Sinir Ağları (YSA), Karar Ağaçları (KA) ve Rastgele Orman (RO) algoritmaları kullanılmıştır. Mekanik test sonuçlarına göre, en yüksek basınç direnci değeri rutubeti %0 (fırın kurusu) olan örneklerin liflere parallel yönde yapılan yüklemelerinde (51,96 N/mm²) elde edilmiştir. En düşük basınç direnci değeri (13,57 N/mm²) ise %25 rutubetli örneklerin liflere dik yönde yapılan yüklemelerinde saptanmıştır. En yüksek tahmin başarısı R2=0,984 değeri ile Rastgele Orman algoritmasından elde edilmiştir. Sonuç olarak, farklı rutubetlerde LVL'lerin basınç direncini tahmin etmek için yapay zeka tekniklerinin çözüm olarak başarılı bir şekilde kullanılabileceği belirlenmiştir.

Anahtar Kelimeler

• LVL
• Basınç direnci
• Rutubet
• Yapay zekâ
• Karar ağaçları
• Rastgele orman

Determination of the effect of laminated veneer lumber (LVL) moisture content on pressure resistance by artificial ıntelligence

Öz

Wooden materials used in the building sector are exposed to different loading types and different strength depending on the place of use. The use of materials suitable for the type of loading affects important factors such as safety, performance and cost. Another important issue in wooden materials used in the building sector is wood-water relations. Moisture causes significant changes on the physical, mechanical and technological (hardness, wear) properties of wood. In this study, 5-layer LVL (Laminated Veneer Lumber) was produced from 2 mm beech (Fagus orientalis L.) veneer obtained by peeling process. Produced LVLs were subjected to four different moisture (0%, 12%, 18% and 25%) compressio strength in two different directions, perpendicular and parallel to the fibers. Using the data obtained from the specified moisture values, the pressure resistance values in other moisture amounts were estimated by artificial intelligence. Artificial Neural Networks (ANN), Decision Trees (DT) and Random Forest (RF) algorithms are used in the predictions. According to the mechanical test results, the highest compression strength value (51.96 N/mm²) was obtained in the loading parallel to the fibers of the samples with 0% moisture (oven dry). The lowest compression strength value (13.57 N/mm²) was determined in the loading vertical direction to the fibers of 25% moisture samples. The highest prediction success was obtained from the Random Forest algorithm with a value of R2 = 0.984. As a result, it has been determined that artificial intelligence techniques can be used successfully as a solution to predict the pressure resistance of LVLs at different humidity.

Anahtar Kelimeler

• LVL
• Compression strength
• Moisture
• Artificial intelligence
• Decision trees
• Random forest

Kaynakça

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