Abstract: The existing indoor tests on soil conditioning effect have many disadvantages such as long time and high cost, as well as cannot meet prediction needs in tunnel excavation. In this paper, based on deep learning, genetic algorithm (GA) is introduced to redesign and optimize the traditional back propagation(BP) neural network to create a GABP model. The root mean square error(RMSE), the mean absolute error(MAE), and the determinable coefficients 〖WT〗R2 〖WT５《TNR》〗are selected as the research indicators to evaluate the prediction effect of the model. Finally, the prediction results of the support vector machine algorithm and the random forest algorithm in machine learning are compared with those of the GABP model. The experimental results show the following: (1) Both the traditional BP model based on deep learning and the GABP model created have higher evaluation indexes than the machine learning algorithm. (2) Compared with the prediction results of the traditional BP network, the highest relative errors of the GABP model for the prediction of internal friction angle, permeability coefficient, and slump are reduced by 7.18%, 5.02%, and 1.17% respectively. (3) The RMSE, MAE, and values of the GABP model are better than the prediction results of the traditional BP model and the machine learning algorithm. It can be concluded that deep learningbased neural networks are better at extracting correlations between data than machine learning algorithms, and the GABP model obtained after optimization by genetic algorithms improves the prediction accuracy of traditional BP models.

Key words: waterrich sandy stratum, soil conditioning, genetic algorithm, back propagation neural network, effect prediction