Prediction of Dynamic Compressive Strength of Hydraulic Asphalt Concrete Based on BP Neural Network and Response Surface Fitting - XIN Zhenke (Renmin Zhujiang)
To verify the applicability of back-propagation (BP) neural network models in predicting the compressive strength of hydraulic asphalt concrete,we designed 32 groups of cylinder specimens for uniaxial compression tests from the two dimensions of strain rate (10-5 s-1~10-2 s-1) and temperature (-20~30 ℃). MATLAB was employed to establish a BP neural network model for the prediction of the compressive strength of hydraulic asphalt concrete.The response surface fitting model was established by the Levenberg-Marquardt method and the general global optimization algorithm,the prediction results of which were then compared with those of the BP neural network.The results show that the correlation coefficient r between the value predicted by the BP neural network model (the response surface fitting model) and the test value is 1.099 5 (1.114 2).Compared with the response surface fitting model having specific expressions,the BP neural network model has higher prediction accuracy.The BP neural network prediction model can be used as an auxiliary means for related experimental research and numerical analysis.