In order to establish an effective early warning system for landslide disasters, accurate landslide displacement prediction is the core. In this paper, a typical step-wise-characterized landslide (Caojiatuo landslide) in the Three Gorges Reservoir (TGR) area is selected, and a displacement prediction model of Extreme Learning Machine with Gray Wolf Optimization (GWO-ELM model) is proposed. By analyzing the monitoring data of landslide displacement, the time series of landslide displacement is decomposed into trend displacement and periodic displacement by using the moving average method. First, the trend displacement is fitted by the cubic polynomial with a robust weighted least square method. Then, combining with the internal evolution rule and the external influencing factors, it is concluded that the main external trigger factors of the periodic displacement are the changes of precipitation and water level in the reservoir area. Gray relational degree (GRG) analysis method is used to screen out the main influencing factors of landslide periodic displacement. With these factors as input items, the GWO-ELM model is used to predict the periodic displacement of the landslide. The outcomes are compared with the nonoptimized ELM model. The results show that, combined with the advantages of the GWO algorithm, such as few adjusting parameters and strong global search ability, the GWO-ELM model can effectively learn the change characteristics of data and has a better and relatively stable prediction accuracy.

Shallow and asymmetrically loaded tunnels are very common in mountain tunnel portals, and localized excessive deformation, cracking, or even collapse often occur during the construction process. In this study, a shallow and asymmetrically loaded tunnel entrance is used as an example to discuss the primary support failure mechanism by means of field observations and monitoring and three-dimensional (3D) numerical simulation. Asymmetrical loading, rainfall, and mismanagement were the three main triggers of failure events. The continuous rainfall infiltration significantly reduced the quality of the surrounding rock, resulting in an increase in the initially high asymmetrical loading. The retaining wall and surrounding rock failed to provide sufficient counterpressure on the shallowly buried side, leading to large deformation of the primary support at the beginning of excavation. The deformation increased continuously and quickly exceeded the allowable value; however, the project was not stopped in time, and further excavation occurred due to a communication failure between the monitoring and construction crews. Thus, severe damage to the primary support occurred when the tunnel face reached a distance of 24 m from the entrance. To avoid more hazardous damage, a temporary support mainly composed of 20b I-steel was installed immediately to prevent deformation; 30 days later, a cutting slope was created on the deeply buried side to reduce the asymmetrical loading, and the excavated rock mass was stacked at the shallow buried side to increase the counterpressure. Then, the temporary support was removed, and a secondary lining was applied in a timely fashion. The monitoring data showed that good results had been achieved. This study provides useful recommendations for the construction safety of shallow and asymmetrically loaded tunnel portals.

Landslide susceptibility assessment is considered the first step in landslide risk assessment, but current studies mostly rely on GIS platforms or other software for data preprocessing. The modeling process is relatively complicated and multi-models cannot be integrated. With regard to this issue, this study develops a Python system for automatic assessment of regional landslide susceptibility. The Python system implements landslide susceptibility assessment through three modules: geographic data processing, machine learning modeling and result evaluation analysis. For geographic data processing, ten landslide influencing factors can be used to construct an evaluation factor dataset and reclassify the thematic maps based on the frequency ratio method. Four built-in machine learning models (logistic regression (LR), multi-layer perceptron (MLP), support vector machine (SVM) and extreme gradient boosting (XGBoost)) are integrated into the system to complete susceptibility modeling and calculation. Additionally, receiver operating characteristic (ROC) curves can be automatically generated to evaluate the accuracy. The system was then applied into Lantian County in Shaanxi Province as a demonstration example. The results show that the areas under the ROC curve (AUC) of the four models are 0.838 (LR)、0.882 (SVM)、0.809 (MLP) and 0.812 (XGBoost), respectively, indicating that the SVM model was the most suitable model for landslide susceptibility assessment in Lantian County in the Loess Plateau of China. The system has now been made open source on Github, which can effectively improve the efficiency of regional landslide susceptibility assessment, especially provide tools for data processing and modeling for non-professionals.