Study on the Infiltration Performance of Colluvial Gravel Based on Neural Network Model
In this paper,the 28-day permeability coefficient data set of CSG was formed by the experimental determi-nation of permeability through 119 sets of experimental test blocks with cemented sand gravel(CSG)as the research ob-ject.There are 119 sets of permeability coefficient data in the dataset,of which the minimum value is 3.41×10-5cm/s and the maximum value is 27.812×10-5cm/s.The data are mainly concentrated in the range of 3×10-5 to 22×10-5 cm/s,accounting for about 97%of the total number of samples.Based on the box line plot to remove the outliers and by the results of the skew kurtosis test,K-S test,and distribution diagram,it can be concluded that the permeability coeffi-cient data of CSG materials obey the law of normal distribution.Based on this,the BP and GABP neural network models were used to predict the permeability coefficients,and the prediction accuracy of the two models was compared.The re-sults show that the accuracy of the GABP model was slightly better than that of the BP model.The actual values of CSG permeability coefficients agreed better with the predicted values,which indicated that the prediction was better.
万方数据
维普
