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Existing studies have proved that the incorporation of polypropylene fiber can effectively improve the impact resistance of concrete. In this paper, the split Hopkinson pressure bar and digital pulse shaping technology are used to further study the dynamic compression performance and constitutive relationship of concrete in the cold regions under the deteriorating effect of freeze-thaw cycle, and obtain the dynamic increase factor. Studies have shown that polypropylene fiber-reinforced high-strength concrete has a strain rate enhancement effect. The number of freeze-thaw cycle times at low strain rates has an obvious effect on concrete deterioration, and the effect is weaker at higher strain rates. By comparing three typical dynamic strength growth factor models with the test results, after revising the model, the dynamic increase factor equations under different freeze-thaw circulation times were fitted. This research provides a basis for the design of durability and robustness of building structures in cold regions.

To predict the impact resistance of steel fiber reinforced concrete (SFRC), 50 specimens with different fiber lengths and different fiber contents were loaded using ABAQUS finite element software to obtain data in this paper. Two machine learning (ML) models, backward propagation-artificial neural network (BP-ANN) and support vector machine (SVM), were used to train the data. The results show that in the prediction of the impact resistance of steel fiber reinforced concrete by this model, the deviation of the predicted values from the real values is small, and the two models are well fitted. To further optimize the model, the parameters of the prediction model were adjusted using the whale optimization algorithm (WOA) in this paper, and the accuracy of the optimized model was significantly improved. After optimization, the WOA-BP-ANN and WOA-SVM models have better generalization ability and higher prediction accuracy than the WOA-SVM model.