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The frost heave characteristics of artificial frozen soil are very important information for underground freezing engineering. It is found that both the frost heaving force and rate of the same soil layer increase with the decrease of freezing temperature. In addition, due to the comprehensive influence of freezing temperature, natural water content, dry density, and tidal flow peak value, the frost heave characteristics of different soil samples are evidently uncertain. With the aim of improving the deficiency of traditional BP neural network algorithms in solving fuzzy random engineering problems, random factor and mean square error between layers are used to modify the evaluation function of the network model. On this basis, taking tidal flow peak value, freezing temperature, natural water content, and dry density as inputs, the frost heaving force and rate of frozen soil as output values, and setting the number of hidden layer elements as 4, an improved fuzzy random BP network prediction model for frost heave characteristics was established. The new network prediction model has a smaller weight and bias, and the response tends to be smoother than the traditional one, which greatly reduces the overfitting phenomenon. The engineering example shows that the improved BP neural network prediction model can make the predicted value of frost heaving force and rate basically coincide with the measured value after effective training, and the error is controlled within 8%. Therefore, the prediction model can be used as an effective tool to predict frost heaving characteristics in Nantong metro freezing construction, and the corresponding model and method can also be extended to similar engineering cases.

Fuzzy Random Prediction Model of Frost Heave Characteristics of Horizontal Frozen Metro Contact Channel in Coastal Area