Comparative Analysis of ANN-MLP, ANFIS-ACO_R and MLR Modeling Approaches for Estimation of Bending Strength of Glulam

Multiple linear regression (MLR), adaptive network-based fuzzy inference system-ant colony optimization algorithm hybrid (ANFIS-ACO(R)) and artificial neural network-multilayer perceptron (ANN-MLP) were tested to model the bending strength of Glulam (glue-laminated timber) manufactured with a plane tree (Platanus orientalis L.) wood layer adhered with different weight ratios (WR) of modified starch/urea formaldehyde (UF) adhesive containing different levels of nano-ZnO (NC) used at different levels of the press temperature (Tem) and time (Tim). According to X-ray diffraction (XRD) and stress-strain curves, some changes in the behavior of the product were seen. After selecting the best model through determining statistics such as the determination coefficient (R2) and root mean square error (RMSE), mean absolute error (MAE) and sum of squares error (SSE), the production process was optimized to obtain the highest modulus of rupture (MOR) using the Genetic Algorithm (GA) combined with MLP. It was determined that the MLP had the best accuracy in estimating the response. According to the MLP-GA hybrid, the optimum input values for obtaining the best response include: WR-49.1%, NC-3.385%, Tem-199.4 degrees C and Tim-19.974 min.