Macro-microscopic study on the crack resistance of polyester fiber asphalt mixture under dry-wet cycling and neural network prediction

Asphalt mixture is a composite material with a complex multiphase dispersed system, and its macroscopic mechanical behavior is inherently related to the microstructure characteristics. To investigate the low-temperature crack resistance degradation law of polyester fiber asphalt mixture under different dry-wet cycling conditions, six polyester fiber contents (0%, 0.3%, 0.35%, 0.4%, 0.45%, and 0.5%) were dry-blended into SMA-13 asphalt mixture to prepare fiber-reinforced SCB specimens with pre-notches. Semi-circular bending tests were conducted to test the crack resistance performance of the fiber asphalt mixture after 0, 2, 4, 6, and 8 dry-wet cycles. The experimental results show that the crack resistance performance of specimens with different fiber contents decreases with the increase of dry-wet cycling times, and the influence of salt-dry-wet coupling is greater than that of water-dry-wet coupling. Under the same conditions, the crack resistance index (CRI) increases first and then decreases with the increase of polyester fiber content, reaching its maximum at a polyester fiber content of 0.4%. When the fiber is added at 0.5%, the agglomeration of polyester fibers restricts the low-temperature performance of the specimen. In addition, the DIC technology is used to analyze the trend of horizontal strain variation of specimens after different dry-wet cycles. The results indicate that with the increase of the cycle period, the strain concentration area becomes more apparent, and in the destruction stage, the full-field horizontal strain Exx in the crack concentration zone gradually increases, while the crack resistance decreases. Finally, addressing the limitations of traditional BP neural networks in solving nonlinear problems, a particle swarm optimization algorithm is proposed to optimize the BP neural network, and a PSO-BP neural network model is constructed. Through the analysis of evaluation indicators, it is found that the PSO-BP neural network has improved generalization ability compared to the traditional BP neural network model, and overfitting is reduced, making it an effective tool for predicting the low-temperature performance of polyester fiber asphalt mixtures.