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

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

doi:10.58816/duzceod.1443083

TR EN

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

Öz

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.

Anahtar Kelimeler

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

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

Kaynakça

ASTM American Society for Testing and Materials (ASTM) E 72, (2014). Standard Test Methods of Conducting StrengthTests of Panels for Building Construction. West Conshohocken, A, United States.
Antanasijević, D. Z., Pocajt, V. V., Povrenović, D. S., Ristić, M. Đ., & Perić-Grujić, A. A. (2013). PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Science of the Total Environment, 443, 511- 519.
Bobadilha, G. S., Stokes, C. E., & Verly Lopes, D. J. (2021). Artificial neural networks modelling based on visual analysis of coated cross laminated timber (CLT) to predict color change during outdoor exposure. Holzforschung, 75(7), 646-654.
Brandner, R. (2018). Cross laminated timber (CLT) in compression perpendicular to plane: Testing, properties, design and recommendations for harmonizing design provisions for structural timber products. Engineering Structures, 171, 944-960.
Chen, C. X., Pierobon, F., & Ganguly, I. (2019). Life Cycle Assessment (LCA) of CrossLaminated Timber (CLT) produced in Western Washington: The role of logistics and wood species mix. Sustainability, 11(5), 1278.
Demir, A., Birinci, A. U., & Öztürk, H. (2021). Yerli Ağaç Türlerinden Üretilen Kontrplak Kaplı Perde Duvarların Yanal Yük Altındaki Performansı. Bartın Orman Fakültesi Dergisi, 23(2), 528-535.
Demir, A., Demirkir, C., Ozsahin, S., & Aydin, I. (2023). Artificial neural-network optimisation of nail size and spacings of plywood shear wall. Wood Material Science & Engineering, 18(1), 97-106.
Demirkir, C., Özsahin, Ş., Aydin, I., & Colakoglu, G. (2013). Optimization of some panel manufacturing parameters for the best bonding strength of plywood. International Journal of Adhesion and Adhesives, 46, 14-20.
Di Bella, A., & Mitrovic, M. (2020). Acoustic characteristics of cross-laminated timber systems. Sustainability, 12(14), 5612.
Dong, Q., Xing, K., & Zhang, H. (2017). Artificial neural network for assessment of energy consumption and cost for cross laminated timber office building in severe cold regions. Sustainability, 10(1), 84.

Dong, W., Wang, Z., Chen, G., Wang, Y., Huang, Q., & Gong, M. (2023). Bonding performance of cross-laminated timber-bamboo composites. Journal of Building Engineering, 63, 105526.
Follesa, M., & Fragiacomo, M. (2018). Force-based seismic design of mixed CLT/LightFrame buildings. Engineering Structures, 168, 628-642. Gagnon, S., C. Pirvu. 2012. Cross laminated timber (CLT) handbook. Vancouver, Canada: FPInnovations.
Guo, H., Liu, Y., Chang, W. S., Shao, Y., & Sun, C. (2017). Energy saving and carbon reduction in the operation stage of cross laminated timber residential buildings in China. Sustainability, 9(2), 292.
Hadigheh, S. A., & Dias-da-Costa, D. (2020). Shear strength of cross laminated timberconcrete connections reinforced with carbon fibre polymer composites. In ACMSM25: Proceedings of the 25th Australasian Conference on Mechanics of Structures and Materials, 179-185 Springer Singapore.
Hassanieh, A., Valipour, H. R., & Bradford, M. A. (2017). Experimental and numerical investigation of short-term behaviour of CLT-steel composite beams. Engineering Structures, 144, 43-57.
Hematabadi, H., Madhoushi, M., Khazaeyan, A., Ebrahimi, G., Hindman, D., & Loferski, J. (2020). Bending and shear properties of cross-laminated timber panels made of poplar (Populus alba). Construction and Building Materials, 265, 120326.
Hindman, D. P., & Golden, M. V. (2020). Acoustical properties of southern pine crosslaminated timber panels. Journal of Architectural Engineering, 26 (2), 05020004.
Kippel, M., Leyder, C., Frangi, A., Fontana, M. Lam & F. Ceccotti. (2014). Fire tests on loaded cross-laminated timber wall and floor elements. Fire Safety Science, 11, 626- 639.
Küçükönder, H., Boyaci, S., & Akyüz, A. (2016). A modeling study with an artificial neural network: developing estimationmodels for the tomato plant leaf area. Turkish Journal of Agriculture and Forestry, 40(2), 203-212.
Lie, X., Subhani, M., Ashraf, M., Kafle, B., & Kremer, P. (2020). A current-state-of-the-art on design rules vs test resistance of Cross Laminated Timber members subjected to transverse loading. In CIGOS 2019, Innovation for Sustainable Infrastructure: Proceedings of the 5th International Conference on Geotechnics, Civil Engineering Works and Structures, 185-190, Springer Singapore. 306
Luengo, E., Hermoso, E., Cabrero, J. C., & Arriaga, F. (2017). Bonding strength test method assessment for cross-laminated timber derived stressed-skin panels (CLT SSP). Materials and structures, 50, 1-12.
O'Dowd, B., Cunningham, L. S., & Nedwell, P. (2016). Briefing: Experimental and theoretical bending stiffness of cross-laminated timber panels. Proceedings of the Institution of Civil Engineers-Construction Materials, 169(6), 277-281.
Ozturk, H., Demir, A., & Demirkir, C. (2022). Optimization of pressing parameters for the best mechanical properties of wood veneer/polystyrene composite plywood using artificial neural network. European Journal of Wood and Wood Products, 80(4), 907- 922.
Özşahin, Ş. (2012). The use of an artificial neural network for modeling the moisture absorption and thickness swelling of oriented strand board. BioResources, 7(1), 1053- 1067.
Reynolds, T., Foster, R., Bregulla, J., Chang, W. S., Harris, R., & Ramage, M. (2017). Lateral-load resistance of cross-laminated timber shear walls. Journal of Structural Engineering, 143(12), 06017006.
Sandoli, A., D’Ambra, C., Ceraldi, C., Calderoni, B., & Prota, A. (2021). Sustainable crosslaminated timber structures in a seismic area: Overview and future trends. Applied Sciences, 11(5), 2078.
Soriano, F. M., Pericot, N. G., & Sierra, E. M. (2016). Comparative analysis of the reinforcement of a traditional wood floor in collective housing. In depth development with cross laminated timber and concrete. Case studies in construction materials, 4, 125-145.
Srivaro, S., Tomad, J., Shi, J., & Cai, J. (2020). Characterization of coconut (Cocos nucifera) trunk’s properties and evaluation of its suitability to be used as raw material for cross laminated timber production. Construction and Building Materials, 254, 119291.
Tannert, T., Follesa, M., Fragiacomo, M., Gonzalez, P., Isoda, H., Moroder, D., Xiong, H. & van de Lindt, J. (2018). Seismic design of cross-laminated timber buildings. Wood and Fiber Science, 50, 3-26.
Taşpınar, F., & Bozkurt, Z. (2014). Application of artificial neural networks and regression models in the prediction of daily maximum PM10 concentration in Düzce, Turkey. Fresenius Environmental Bulletin, 23, 2450-2459.
Turkish Standards Institution. (2012). TS 1265. Sawn timber (Coniferous) - For building construction. Ankara, TSE. Turkish Standards Institution. (2019). TS EN 14081. Timber structures - Strength graded structural timber with rectangular cross section - Part 1: General requirements, Ankara, TSE.
Turkish Standards Institution. (2016). TS EN 338. Structural timber - Strength classes, Ankara, TSE.
Turesson, J., Berg, S., & Ekevad, M. (2019). Impact of board width on in-plane shear stiffness of cross-laminated timber. Engineering Structures, 196, 109249.
Varol, T., Canakci, A., & Ozsahin, S. (2018). Prediction of effect of reinforcement content, flake size and flake time on the density and hardness of flake AA2024-SiC nanocomposites using neural networks. Journal of Alloys and Compounds, 739, 1005- 1014.
Wieruszewski, M., & Mazela, B. (2017). Cross Laminated Timber (CLT) as an Alternative Form of Construction Wood. Wood Industry/Drvna Industrija, 68(4), 359-367.
Yadav, V., & Nath, S. (2017). Forecasting of PM 10 using autoregressive models and exponential smoothing technique. Asian Journal of Water, Environment and Pollution, 14(4), 109-113

Ayrıntılar

Birincil Dil

Türkçe

Konular

Ahşap Esaslı Kompozitler , Ahşap Fiziği ve Mekaniği , Ahşap Yapılar ve Konstrüksiyonları

Bölüm

Araştırma Makalesi

Yazarlar

Abdullah Uğur Birinci ^*
0000-0003-3273-3615
Türkiye

Okan İlhan
0000-0001-8882-6461
Türkiye

Aydın Demir
0000-0003-4060-2578
Türkiye

Cenk Demirkır
0000-0003-2503-8470
Türkiye

Yayımlanma Tarihi

30 Haziran 2024

Gönderilme Tarihi

26 Şubat 2024

Kabul Tarihi

15 Mayıs 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 20 Sayı: 1

DOI

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

IZ

https://izlik.org/JA22EA58ZE

APA

Birinci, A. U., İlhan, O., Demir, A., & Demirkır, C. (2024). Yapay Sinir Ağları (YSA) Kullanılarak CLT Perde Duvarların Yanal Yük Altındaki Rijitliklerinin Kereste Direnç Sınıflarına Göre Tahmin Edilmesi. Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi, 20(1), 288-307. https://doi.org/10.58816/duzceod.1443083

Cited By

Evaluation of structural and thermal performance of polystyrene integrated cross laminated timber panels

Scientific Reports

https://doi.org/10.1038/s41598-026-41173-3

DÜOD'da yayımlanan makaleler Creative Commons Atıf-GayriTicari 4.0 (CC BY-NC) kapsamında lisanslanmıştır.

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

Öz

Anahtar Kelimeler

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

Abstract

Keywords

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster

Cited By

Evaluation of structural and thermal performance of polystyrene integrated cross laminated timber panels