Abstract: In view of the obvious difference in the permeability of the coarse grained soil, effected by factors such as gradation of grain and compaction degree, a prediction method for the permeability of the coarse-grained soil is proposed in this study. 93 groups of data of the coarse-grained soil are collected and obtained. Taking the full gradation(d₁₀~ d₁₀₀) and the porosity ratio as the input variables of the BP neural network, a prediction model for the permeability coefficients of the coarse-grained soil is developed on the basis of the BP neural network and genetic algorithm, by using the genetic algorithm to optimize the BP neural network′s initial weights and thresholds. The research results show that the accuracy of the GA-BP neural network meets the requirements after 55 iterations. And the mean relative error of the predicted results of 87 groups of the training samples is 5.10%. Moreover, a relative error of 75% of the training samples is less than the mean relative error. In addition, the mean relative error of 6 groups of the testing samples is 6.39%, which indicates that the generalization performance of the network model is high. It is concluded that the permeability coefficients of the coarse-grained soil can be well predicted by applying the GA-BP neural network considering the full gradation and void ratio. Moreover, The convergence rate, the prediction accuracy and the generalization performance of the GA-BP neural network are better than those of the standard BP neural network model. And the model based on the GA-BP neural network can provide technical references and support for the selection and improvement of the coarse-grained soil in practical engineering.