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Anti-dip slope that fails in the form of flexural toppling is commonly encountered in rock engineering. This paper presents an approach to calculate the local failure probability of the anti-dip slope susceptible to flexural toppling. The realization of this approach consists of four major steps: (1) establishing geo-mechanical model of the slope and generating physical parameters randomly using Monte Carlo simulation, (2) determining the demarcation of the toppling and sliding failure zones, (3) evaluating the stability state of each single column, and (4) deriving the local failure probability by an iterative calculation. A case study of the Mari landslide is analyzed by employing the proposed approach, and the Maxwell model is applied to reduce physical parameters with time. The results show that the instability grade increases gradually throughout the whole evolution process of the slope. In the incipient stage, particular attention should be paid to the middle–upper columns, which have the highest failure probabilities. However, as the slope deformation increases, all the columns should be taken into account since they have approximately the equal possibility of failure. A simplified slope model is employed to perform a parametric analysis. The results show that (1) the local failure probability of the slope tends to decrease with increases in the rock friction angle, rock tensile strength or interface friction angle, while increasing the rock unit weight leads to a higher probability of local failure; (2) the computation results are more sensitive to the uncertainty in the interface friction angle than that in the other three above-mentioned parameters; and (3) the maximum failure probability depends on the geometric parameters, including the column interface angle, slope angle, crest angle, slope height and column thickness. [ABSTRACT FROM AUTHOR]

In recent years, a large number of bank destruction occur in the reservoir area under the effect of water fluctuation, which may be lead to reservoir accumulative landslide geological hazards finally. The paper conducted the bank destruction forecasting study for accumulative landslides in the Three Gorges Reservoir Region, China utilizing back-propagation (BP) neural network approach. A representative scenario of Jinle landslide is then taken for analysis purposes. On the basis of the existing data sets of bank destruction cases, the BP neural network forecasting model and the corresponding programs for bank destruction are both presented, whose forecasting result is validated by two independent approaches, namely empirical method and numerical modeling method. Furthermore, the BP neural network model had obvious advantages over the convention approaches in the aspects of the fast calculation speed and high convenience. According to the bank destruction forecasting scale presented above, the corresponding revetment measures can be proposed to prevent the occurring of the bank destruction, whose effectiveness has been further validated by the actual engineering practice. [ABSTRACT FROM AUTHOR]

Data preprocessing is an indispensable step for landslide displacement forecasting, which is an effective approach for predicting the deformation and failure behaviors of landslides. However, most studies focus on the construction of displacement forecast models and ignore the influence of data preprocessing on the forecasting results. Data normalization is an important part of data preprocessing; however, the selection of a data normalization method is subjective and arbitrary. In this study, four types of normalization methods for data preprocessing are presented, and these methods are applied in forecasting the displacement of bank landslides in the Three Gorges Reservoir area with various deformation mechanisms for comparison. The results demonstrate that (1) the selected normalization method substantially influences the forecast performance; (2) the normalization method is closely related to the selected forecasting model and is less dependent on the landslide deformation mechanism; and (3) the commonly used max–min normalization approach is not the optimal method, and the zero-mean normalization method is optimal for the particle swarm optimizer of support vector machine (PSO-SVM) method, while the logarithmic normalization method is optimal for the extreme learning machine method. The obtained results suggest that the data preprocessing methods must be carefully selected in landslide displacement forecasting. [ABSTRACT FROM AUTHOR]

Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD-ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD-ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China. [ABSTRACT FROM AUTHOR]