Prediction of Flexural Properties of Wood Material Reinforced with Various FRP Fabrics by Artificial Neural Networks

Recently, fiber reinforced polymer (FRP) applications have started to be used in the reinforcement of wooden structures, such as in the reinforcement of steel and reinforced concrete structures. It is necessary to strengthen the wooden structures for reasons such as removing the damages caused by external factors and earthquakes in time, increasing the load-bearing capacity of the structure by restoration, preventing early fatigue and breakages that may occur as a result of mistakes made in the design. The necessity to improve the repair and strengthening methods of the structures damaged as a result of the earthquake over time arises. In this study, the maximum load, displacement, flexural strength and modulus of elasticity of the wood material of Iroko and Ash tree species reinforced with 4 different FRP fabrics, namely carbon, glass, aramid and basalt, were determined by bending test. As a result of the experimental study, the maximum load, displacement, flexure strength and elasticity modulus values of the reinforced samples were estimated by artificial neural network (ANN). As a result, it was determined that the flexural properties of a wood material strengthened with FRP by using ANN can be predicted