Chengbin Yang

In order to promptly evacuate personnel and property near the foot of the landslide and take emergency treatment measures in case of sudden danger, it is very necessary to select suitable forecasting methods for conduct short-term displacement predictions in the slope-sliding process. In this paper, we used Python to develop the landslide displacement-prediction method based on the eXtreme Gradient Boosting (XGBoost) algorithm, and optimized the hyperparameters through a genetic algorithm to solve the problem of insufficient short-term displacement-prediction accuracy for landslides. We compared the deviation, relative error (RE) and median of RE of predicted values obtained using XGBoost, SVR and RNNs, and the actual value of landslide displacement. The results show that the accuracies of slope displacement prediction using XGBoost and SVR are very high, and that using RNNs is very low during the sliding process. For large displacement values and small numbers of samples, the displacement-prediction effect of XGBoost algorithm is better than that of SVR and RNNs in the sliding process of landslide. There are generally only fewer data samples collected during the landslide sliding process, so RNNs is not suitable for displacement prediction in this scenario. If the number of data samples is large enough, using RNNs to predict the long-term displacement of the slope may also have a much higher accuracy.

The accelerated creep plays an important role in the disasters of soft-rock tunnels under high stress. However, most of previous studies only involved attenuation creep and uniform creep. Large deformation disasters of soft rock occurred during the tunneling process in the Qianzhou–Sanyangchuan Tunnel, Gusu Province, China. In the paper, we developed the nonlinear generalized Nishihara rheological model with non-stationary parameter creep (NGNRM) to simulate the accelerated creep behaviors of soft rocks under high stress, and implemented it in ABAQUS, to reveal the mechanism of large deformation of soft rock. We proposed the multi-objective back analysis method of surrounding rock mechanical parameters based on the eXtreme Gradient Boosting and the non-dominated sorting genetic algorithm-II. In addition, the orthogonal test design method was used to determine the main parameters affecting the displacement of the tunnel. Using the proposed method, we can evaluate the large deformation mechanism of deep soft rock tunnels, and scientifically determine when to reinforce to prevent a large deformation disaster of the tunnel.