Kunlong Yin

Modeling of landslide generated impulsive waves considering complex topography in reservoir area

The impulsive wave is considered as one of the most notably secondary hazards induced by landslides in reservoir areas. The impulsive wave with considerable wave amplitude is able to cause serious damage to the dam body, shoreline properties and lives. To investigate and predict the wave characteristics, many experimental studies employed the generalized channels rather than the realistic topography. Deviation from the idealized geometries may result in non-negligible effects due to the wave refraction or reflection with complex topography. To consider the topography effect, a prototype scaled experiment was conducted. A series of tests with different collocation of parameters were performed. The experimental results were then summarized to propose empirical equations to predict the maximum wave amplitude, and wave decay in channel direction. The generalized empirical equations can obtain better results for wave features prediction by compared with those derived from the idealized models. Furthermore, a 3D numerical modeling corresponding to the physical experiment was conducted based on the SPH method. The wave characteristics in the sliding and channel directions were investigated in detail including the maximum wave amplitude, wave run-up, wave arrival time and wave crest amplitude decay. The comparison between the simulation and experiment indicates the promising accuracy of the SPH simulation in determining the general features even with complex river topography. Finally, the limitation and applicability of both the experimental and numerical methods in analyzing the practical engineering problems were discussed. Combination of the both methods can benefit the hazard prevention and reduction for landslide generated impulsive waves in reservoir area.

Influencing factor analysis and displacement prediction in reservoir landslides − a case study of Three Gorges Reservoir (China)

Subject review The developmental tendencies of cumulative displacement time series associated with reservoir landslides influenced by large water reservoirs must be effectively predicted. However, traditional methods do not encompass the dynamic response relationships between landslide deformation and its influencing factors. Therefore, a new approach based on the exponential smoothing (ES) and multivariate extreme learning machine methods was introduced to reveal the influencing factors of landslide deformation and to forecast landslide displacement values. First, the influencing factors of reservoir landslide deformation were analysed. Second, the ES method was used to predict the trend term displacement and obtain the periodic term displacement by determining the trend term from the cumulative displacement. Next, multivariate influencing factors were analysed to explain the periodic term displacement. Then, an extreme learning machine (ELM) model was established to predict the periodic term displacement based on the multivariable analysis of influencing factors. Finally, cumulative displacement prediction values were obtained by adding the trend and periodic displacement prediction values. The Bazimen and Baishuihe landslides in Three Gorges Reservoir Area (TGRA) were selected as case studies. The proposed ES-multivariate ELM (ES-MELM) model was compared to the ES-univariate ELM (ES-ELM) model. The results show that reservoir landslide deformation is mainly influenced by periodic reservoir water level fluctuations and heavy rainfall. Additionally, the proposed model yields more accurate predictions than the ES-ELM model.

Landslide displacement prediction using discrete wavelet transform and extreme learning machine based on chaos theory

Landslide displacement system is generally characterized by non-stationary and nonlinear characteristics. Traditionally, many artificial neural network (ANN) models have been proposed to forecast landslide displacement. However, the underlying non-stationary characteristics in the landslide displacement are not captured, and the input–output variables of the ANN models are not selected nonlinearly. To overcome these drawbacks, this paper proposes the chaos theory-based discrete wavelet transform (DWT)–extreme learning machine (ELM) model to predict landslide displacement. The DWT method is adopted to decompose the landslide displacement into several low- and high-frequency components to address the non-stationary characteristics. And chaos theory is used to determine the input–output variables of the ELM model. The cumulative displacement time series of the Baishuihe and Baijiabao landslides in the Three Gorges Reservoir Area, China, are used as data sets. The results show that the chaotic DWT-ELM model accurately predicts landslide displacement. The chaotic DWT–support vector machine (SVM), chaotic DWT–back-propagation neural network (BPNN) and single chaotic ELM models are used for comparisons. The comparison results show that the chaotic DWT-ELM model achieves higher prediction accuracy than do the chaotic DWT-SVM, chaotic DWT-BPNN and the single chaotic ELM models.

The Anlesi Landslide in Wanzhou, China: Characteristics and Mechanism of a Gentle Dip Landslide

Many gentle dip landslides have taken place in Wanzhou, located in the Three Gorges Reservoir area. In order to study the mechanism of the gentle dip landslides, the authors selected the Anlesi landslide as a typical gentle dip landslide to study in detail. Field investigations show that the slip zones of the Anlesi landslide formed from a white mudstone in Jurassic red strata by compressive stress. The X-ray diffraction and infrared ray analysis reveal that the main mineral components of the slip zone are composed of montmorillonite, illite, feldspar, and quartz. A set of tests were conducted on the slip zone specimens to obtain the physicomechanical characteristics. Test results show that the slip zone soils are silty clay, of medium swelling potential, as shear strength becomes very low once the slip zone attracts water to saturation.

A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms

Landslide displacement prediction is considered as an essential component for developing early warning systems. The modelling of conventional forecast methods requires enormous monitoring data that limit its application. To conduct accurate displacement prediction with limited data, a novel method is proposed and applied by integrating three computational intelligence algorithms namely: the wavelet transform (WT), the artificial bees colony (ABC), and the kernel-based extreme learning machine (KELM). At first, the total displacement was decomposed into several sub-sequences with different frequencies using the WT. Next each sub-sequence was predicted separately by the KELM whose parameters were optimized by the ABC. Finally the predicted total displacement was obtained by adding all the predicted sub-sequences. The Shuping landslide in the Three Gorges Reservoir area in China was taken as a case study. The performance of the new method was compared with the WT-ELM, ABC-KELM, ELM, and the support vector machine (SVM) methods. Results show that the prediction accuracy can be improved by decomposing the total displacement into sub-sequences with various frequencies and by predicting them separately. The ABC-KELM algorithm shows the highest prediction capacity followed by the ELM and SVM. Overall, the proposed method achieved excellent performance both in terms of accuracy and stability.

Effect of over-consolidation and shear rate on the residual strength of soils of silty sand in the Three Gorges Reservoir

Estimation of the residual strength of the soil on the landslide sliding surface is essential for analyzing reactivated landslides. This study investigated the influence of over-consolidation ratio (OCR) and shear rate on the residual strength of SM-type (silty sand) landslide soils in the Three Gorge Reservoir using ring shear tests under drained conditions. A series of ring shear tests were conducted to measure the drained residual strength under over-consolidation ratios of 1–12 and shear rates of 0.06–30.00 mm/min. Test results showed that residual strengths of SM-type landslide soils were not affected significantly by the over-consolidation process. The effect of shear rate on residual strength did not exhibit a regular pattern at shear rates of 0.06–10.00 mm/min, and behaved negatively at a high shear rate of 30 mm/min. The reduction in residual strength at higher shear rates may be attributable to increases in the water content of the shear zone and the amount of finer particles, due to particle breakage and/or larger grains being pushed from the shear zone.

Annual variation of landslide stability under the effect of water level fluctuation and rainfall in the Three Gorges Reservoir, China

Landslides in the Three Gorges Reservoir (TGR) are widely distributed and are a serious threat to the environment and the local people. Since the impoundment of the reservoir in 2003, many of the landslides have been reactivated which were triggered by water level fluctuation and rainfall. Taking the Maliulin landslide in the TGR as a case study, field investigation and displacement monitoring are conduced to study the characteristics of the landslide. According to the annual variation, the fluctuation of reservoir water is divided into four periods. The cumulative rainfall corresponding to different rainfall return periods is computed by Gumbel model. The variation of landslide stability and failure probability under the effect of water level fluctuation and rainfall in a complete annual cycle is calculated in terms of the Morgenstern–Price and Monte Carlo model. Based on the monitoring by inclinometer, a secondary shallow sliding surface is detected which controls its current activities. The annual variation of landslide stability tends to coincide with the change of reservoir water level. The minimum factor of safety occurs during the period of water level drawdown. Combining with the effect of extreme rainfall 50-year return period and water level dropdown, the calculated minimum factor of safety is below unit and the landslide is unstable. The scenario of annual failure probability of landslide is completed in the paper that is the basis for further risk evaluations.

Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method

Landslide displacement prediction is an essential component for developing landslide early warning systems. In the Three Gorges Reservoir area (TGRA), landslides experience step-like deformations (i.e., periods of stability interrupted by abrupt accelerations) generally from April to September due to the influence of precipitation and reservoir scheduled level variations. With respect to many traditional machine learning techniques, two issues exist relative to displacement prediction, namely the random fluctuation of prediction results and inaccurate prediction when step-like deformations take place. In this study, a novel and original prediction method was proposed by combining the wavelet transform (WT) and particle swarm optimization-kernel extreme learning machine (PSO-KELM) methods, and by considering the landslide causal factors. A typical landslide with a step-like behavior, the Baishuihe landslide in TGRA, was taken as a case study. The cumulated total displacement was decomposed into trend displacement, periodic displacement (controlled by internal geological conditions and external triggering factors respectively), and noise. The displacement items were predicted separately by multi-factor PSO-KELM considering various causal factors, and the total displacement was obtained by summing them up. An accurate prediction was achieved by the proposed method, including the step-like deformation period. The performance of the proposed method was compared with that of the multi-factor extreme learning machine (ELM), support vector regression (SVR), backward propagation neural network (BPNN), and single-factor PSO-KELM. Results show that the PSO-KELM outperforms the other models, and the prediction accuracy can be improved by considering causal factors.

Estimation of probability distribution of shear strength of slip zone soils in Middle Jurassic red beds in Wanzhou of China

Due to the heterogeneity of geological materials, the shear strength of slip zone soils varies randomly. In general, the probability distribution of shear strength is determined empirically or tested by the few numbers of collected soil samples. However, the calculated failure probability of landslide could not be reliable due to oversimplified estimation of the shear strength. Thereby, it is necessary to analyze the random distribution types of shear strength systematically. This paper aims to analyze random properties of shear strength of slip zone soils in Middle Jurassic red beds which are the typical “Slip Prone Strata”. The shear strength of Jurassic red beds varies spatially due to the complexity of bedding history and tectonics. Two thousand eight hundred five results of shear tests are collected from 44 landslides in the Middle Jurassic red beds in Wanzhou of China. The goodness-of-fit test was applied to determine probability distribution of soils. The minimum acceptant level values of natural friction angle peak, natural residual friction angle, saturated friction angle peak, and saturated residual friction angle in three distribution types are 0.739, 0.75, 0.319, and 0.858. And natural cohesion peak, natural residual cohesion, saturated residual cohesion, and saturated cohesion peak are 0.819, 0.67, 0.888, and 0.225. Results indicate that friction angle fits normal distribution perfectly, and cohesion matches log-normal distribution very well except that saturated cohesion peak agrees beta distribution best. The findings obtained from this study are very useful in the probabilistic analysis of slope in similar areas with the same backgrounds.

Landslide Hazard Zonation of the New Badong County Seat Based on GIS

In the Three Gorges reservoir, considering damage to residents and man-made structures by natural hazards, landslide is the most important disaster in the new county seat of BaDong. For this status, landslide hazard zonation is an effective solution for land management. Firstly, based on the analysis of the landslide geological background, by reducing the material composition and historical geological environment before the occurring of landslides, nine evaluation indexes were obtained. Secondly, in order to make the evaluation units have more tangible geological significance and more consummate geologic structure, by perfecting the division method of irregular unit based on the terrain condition, the study area was divided into slope evaluation units. Finally, based on the screening of evaluation indexes by correlation analysis between the indexes and past landslide events, the weighted dynamic clustering analysis, which can consider the different contributions of different indexes to landslides, was proposed to obtain the hazard zonation of study area. The analysis show that sixteen point four percent area of the study area belongs to high risk area, and mainly distributes along the bank of Changjiang River. The hazard zonation of the new county seat of BaDong can provide useful advices for land use planning and give basis for early warning of landslide hazard.