Yapay Sinir Ağları (YSA) Kullanılarak CLT Perde Duvarların Yanal Yük Altındaki Rijitliklerinin Kereste Direnç Sınıflarına Göre Tahmin Edilmesi

Year 2024, , 288 - 307, 30.06.2024

Abdullah Uğur Birinci , Okan İlhan , Aydın Demir , Cenk Demirkır

https://doi.org/10.58816/duzceod.1443083

Abstract

Bu çalışmada, öncelikle yapay sinir ağları (YSA) kullanarak ağaç türü ve kereste direnç sınıfları gibi üretim parametrelerinin CLT perde duvarların yanal yük altındaki rijitlik performansı üzerine etkilerinin belirlenmesi amaçlanmıştır. Daha sonra, analizler sonuncunda elde edilen YSA tahmin modellerini kullanarak, CLT perde duvarlar için en yüksek rijitlik değerini verecek orta ve dış tabakalarda kullanılan kereste direnç sınıflarına ait optimum tabaka kombinasyonlarının ortaya konulması amaçlanmıştır. Bu çalışmada, ladin ve kızılağaç kerestelerin kullanıldığı CLT paneller ile bu iki ağaç türünün kombinasyonlarından oluşturulan hibrit paneller üretilmiştir. Kerestelerin direnç sınıfları TS EN 338 standardına göre hasarsız olarak belirlenmiş ve ladin için C16, C22, C30, kızılağaç için ise D18, D30, D40 grubu keresteleri, çalışma kapsamında CLT üretiminde kullanılmak üzere seçilmiştir. Ağaç türü ve direnç sınıfı kombinasyonlarından oluşan 30 farklı test grubu için CLT paneller üretilmiştir. CLT panellerden oluşturulan perde duvarların analizleri ASTM E 72 standardına göre gerçekleştirilmiş ve elde edilen maksimum yük ile bu yükteki yer değiştirme miktarlarından rijitlikler hesaplanmıştır. YSA modellemeleri sonucunda, deneysel verilerden yola çıkarak tahmin değerleri elde edilmiş ve bu verilerle optimum tabaka kombinasyonları belirlenmiştir. Buna göre, CLT perde duvarlar için elde edilen optimum kereste direnç sınıfları ve tabaka kombinasyonları, ladinde C30-C18-C30, kızılağaçta D30-D35-D30, hibritlerde C30-D24-C30 ve D30-C30-D30 olarak tespit edilmiştir.

Keywords

Çapraz Lamine Ahşap (CLT), Kereste Direnç Sınıfı, Yapay Sinir Ağları (YSA), Perde Duvar, Rijitlik

Supporting Institution

Türk Bilimsel ve Teknik Araştırma Kurumu (TÜBİTAK)

Project Number

220O012

Thanks

Yazarlar 220O012 nolu proje için sağladığı finansal destek için Türk Bilimsel ve Teknik Araştırma Kurumu’na (TÜBİTAK) teşekkürü bir borç bilir.

Predicting Stiffness of CLT Shear Walls Under Lateral Loads According to Timber Strength Classes Using Artificial Neural Network (ANN)

Abstract

In this study, it was primarily aimed to determine the effects of production parameters such as wood species and timber strength classes on the stiffness performance of CLT shear walls under lateral load using artificial neural network (ANN). Then, using the ANN prediction models obtained as a result of the analyses, it was aimed to reveal the optimum layer combinations of the timber strength classes used in the middle and outer layers that will give the highest stiffness value for CLT shear walls. In this study, spruce, alder and these two wood species hybrid CLT panels incorporating were produced. The timber strength classes were determined as undamaged according to the TS EN 338 standard, and C16, C22, C30, group timbers for spruce and D18, D30, D40 group timbers for alder were selected to be used in CLT production within the scope of the study. CLT panels were produced for 30 different test groups consisting of wood species and strength class combinations. The shear walls formed from CLT panels was analyzed according to the ASTM E 72 standard, the stiffness was calculated from the maximum load obtained and the displacement amounts at this load. As a result of ANN modeling, prediction values were obtained based on experimental data and optimum layer combinations were determined with these data. According to this, the optimum timber strength classes and layer combinations for CLT shear walls were determined as C30-C18-C30 for spruce, D30-D35-D30 for alder, and C30-D24-C30 and D30-C30-D30 for hybrids.

Keywords

Cross Laminated Timber (CLT), Artificial Neural Networks (ANN), Shear Wall, Rigidity, Timber Strength Class

Project Number

220O012

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