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

Birinci, Abdullah Ugur; Ilhan, Okan; Demir, Aydın; Demirkir, Cenk

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

2024

Birinci, Abdullah Ugur | Ilhan, Okan | Demir, Aydın | Demirkir, Cenk

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.

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