Abstract: The purpose is to obtain the extreme value of mechanical parameters of road-use waterborne epoxy resin for further scientific optimization of its material composition and proportion. According to the adjustment of self-properties and proportion of different types of raw materials, a series of road-use waterborne epoxy resins were prepared. Taking tensile strength as an example, the mechanical parameter calibration test was carried out and 75 groups of effective data were obtained. Based on small sample data, different prediction models such as traditional multiple regression analysis, typical feedforward and feedback neural networks were established. The fitting effect and prediction accuracy of different mo-dels were compared and analyzed. Genetic algorithm and particle swarm optimization were used to optimize the selected prediction model, and the optimization effect of tensile strength was realized. The optimum values of property and proportion of raw materials were determined retrospectively. It provided a useful reference for scientific optimization of composition and proportion of road-use waterborne epoxy resin. The results are as follow. Compared with the nonlinear regression equation with five independent variable and back propagation (BP) model, the prediction error of Elman model is 25.94%—37.5% lower. It has significant prediction accuracy and stability. According to prediction accuracy and operation speed, Elman neural network (6×4×1) is recommended as the prediction model of tensile strength of waterborne epoxy resin for road. Genetic algorithm can quickly obtain the optimal tensile strength and its related material composition and proportion, and reduce test cost. But its accuracy still needs to be further optimized.
陈谦, 王朝辉, 傅豪, 樊振通, 刘鲁清. 路用水性环氧树脂的拉伸强度预测和极值寻优[J]. 材料导报, 2021, 35(16): 16172-16177.
CHEN Qian, WANG Chaohui, FU Hao, FAN Zhentong, LIU Luqing. Prediction and Extreme Value Optimization of Tensile Strength of Waterborne Epoxy Resin for Road. Materials Reports, 2021, 35(16): 16172-16177.
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2021, Vol. 35 
