Comparison between Multiple Regression Analysis, Polynomial Regression Analysis, and an Artificial Neural Network for Tensile Strength Prediction of BFRP and GFRP

Abstract

In this study, multiple regression analysis (MRA) and polynomial regression analysis (PRA), which are traditional statistical methods, were applied to analyze factors affecting the tensile strength of basalt and glass fiber-reinforced polymers (FRPs) exposed to alkaline environments and predict the tensile strength degradation. The MRA and PRA are methods of estimating functions using statistical techniques, but there are disadvantages in the scalability of the model because they are limited by experimental results. Therefore, recently, highly scalable artificial neural networks (ANN) have been studied to analyze complex relationships. In this study, the prediction performance was evaluated in comparison to the MRA, PRA, and ANN. Tensile strength tests were conducted after exposure for 50, 100, and 200 days in alkaline environments at 20, 40, and 60 °C. The tensile strength was set as the dependent variable, with the temperature (TP), the exposure day (ED), and the diameter (D) as independent variables. The MRA and PRA results showed that the TP was the most influential factor in the tensile strength degradation of FRPs, followed by the exposure time (ED) and diameter (D). The ANN method provided the best correlation between predictions and experimental values, with the lowest error and error rate. The PRA method applied to the response surface method outperformed the MRA method, which is most commonly used. These results demonstrate that ANN can be the most efficient model for predicting the durability of FRPs.

Thermal aging effect on mechanical properties of polyamide 6 matrix composites produced by TFP and compression molding

“…In one study, the tensile strength test results of GFRP exposed to solution environments at different times and temperatures were analyzed using regression analysis. The results showed that temperature had the greatest effect on the deterioration of tensile strength in GFRPs, followed by exposure time 18 . Similarly, another study using ANOVA tables on tensile tests performed on GFRP laminates at temperatures ranging from −80°C to 100°C found that temperature had a statistically significant contribution and should be considered when evaluating the tensile strength properties of GFRP materials under varying temperatures 20 .…”

“…The results showed that temperature had the greatest effect on the deterioration of tensile strength in GFRPs, followed by exposure time. 18 Similarly, another study using ANOVA tables on tensile tests performed on GFRP laminates at temperatures ranging from À80 C to 100 C found that temperature had a statistically significant contribution and should be considered when evaluating the tensile strength properties of GFRP materials under varying temperatures. 20 As can be seen, regression and ANOVA analysis play a critical role in determining mechanical parameters.…”

“…Srinivasan et al modeled the tensile behavior of composite materials produced by additive manufacturing with response surface methodology (RSM) and revealed that infill density and layer thickness were the most significant factor in the responses 17 . Kim et al used regression analysis and RSM to determine the most influential parameters for the tensile strength properties of fiber laminate composites 18 . Bazli et al statistically investigated the effect of different environmental conditions on the tensile properties of GFRP laminates with analysis of variance and regression analysis.…”

“…17 Kim et al used regression analysis and RSM to determine the most influential parameters for the tensile strength properties of fiber laminate composites. 18 Bazli et al statistically investigated the effect of different environmental conditions on the tensile properties of GFRP laminates with analysis of variance and regression analysis. They also proposed a prediction model based on regression analysis for experimental results.…”

Optimization of a Permanent Magnet Synchronous Motor for e-Mobility Using Metamodels

“…The multi-layer perception (MLP) model, comprising hidden and output layers, minimizes the error function using weights and biases [27]. The polynomial regression (PR) model, which uses the regression equation, can reduce errors and improve prediction performance [28]. Finally, the radial basis function (RBF) model can approximate the underlying model using the training dataset, and it can also generate a regression (Reg.)…”