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A Backpropagation Neural Network-based Flexural-tensile Strength Prediction Model for Asphalt Mixture in Cold Regions under Cyclic Thermal Stress

Received: 18 May 2019 Accepted: 20 July 2019 This paper attempts to disclose how the varying cold season temperature affects the performance of asphalt mixture i=n cold regions, and create a model to predict the flexuraltensile strength under cyclic thermal stress. For this purpose, the author investigated the influencing factors of asphalt mixture performance in cold regions, such as temperature level and variation in temperature difference, and employed the backpropagation neural network (BPNN) to learn, train and verify 120 samples of SBS AC-13 database. On this basis, a BPNN-based prediction model was established for the flexural-tensile strength of asphalt mixture under cyclic thermal stress. Next, the predicted results were compared with the actual results through regression analysis. The comparison shows that our model output a correlation coefficient (R) of 0.9706, an evidence for good prediction accuracy. This means our model can effectively predict the flexural-tensile strengths of asphalt mixture under cyclic thermal stress in cold regions. The research findings provide a good reference for similar studies on asphalt mixture.