Search

Rainfall represents one of the most significant cause of landslide hazards. With runoff in the saturated zone of shallow soil considered, an improved Green–Ampt model was proposed to depict the rainfall infiltration process. Furthermore, a formula for calculating the depth of wetting front was derived, and the slope stability was evaluated accordingly. Finally, a formula for calculating the safety factor of multilayer slopes above different surfaces was obtained via the limit equilibrium method. An illustration is a multilayer colluvial slope in Guangdong Province of China, the results indicated that the downward speed of wetting front decreases gradually within the same layer with continuous rainfall but increases significantly when it extends to the interfaces between the soil layers. Safety factor of the slope decreases sharply at the initial stage of rainfall but then more slowly. Slope failure is more likely to occur along the first interface between the soil layers than along the surface of wetting front. The result from the proposed model is closer to the result of the physical model test than from the existed Green–Ampt model. The improved Green–Ampt model for multilayer slopes can accurately evaluate the infiltration process and related stability of multilayer slopes under rainfall. [ABSTRACT FROM AUTHOR]

Monitoring data show that many landslides in the Three Gorges region, China, undergo step-like displacements in response to the managed, quasi-sinusoidal annual variations in reservoir level. This behavior is consistent with motion initiating when the reservoir water level falls below a critical level that is intrinsic to each landslide, with the subsequent displacement rate of the landslide being proportional to the water depth below that critical level. Most motion terminates when the water level rises back above the critical level, so the annual step size is the time integral of the instantaneous displacement rate. These responses are incorporated into a differential equation that is easily calibrated with monitoring data, allowing prediction of landslide movement from actual or anticipated reservoir level changes. Model successes include (1) initiation and termination of the annual sliding steps at the critical reservoir level, producing a series of steps; (2) prediction of variable step size, year to year; and (3) approximate prediction of the shape and size of each annual step. Annual rainfall correlates poorly with step size, probably because its effect on groundwater levels is dwarfed by the 30 m annual variations in the level of the Three Gorges Reservoir. Viscous landslide behavior is suggested. [ABSTRACT FROM AUTHOR]

Zigui Basin is a major landslide-prone region in the Three Gorges Reservoir region of China, and the stabilizing pile is an effective and widely employed countermeasure to reinforce landslides in this region. However, stabilizing piles are mostly designed using deterministic and stability-oriented methods, which generally ignore the system performance and cost-effectiveness. Using the Majiagou landslide reinforced with stabilizing piles as a case study, a probabilistic multi-objective optimization framework for the design of stabilizing piles is proposed and illustrated. Specifically, performance objectives related to failure probability, system robustness and life-cycle cost of the landslide-stabilizing pile system with feasible designs are evaluated, then the best compromised design is obtained by means of Pareto optimality. Expert knowledge and professional judgment are required to set necessary restrictions and finally determine the optimal design. The results show that there is a better design of stabilizing piles than the existing one, with which acceptable reinforcement effectiveness, compromised life-cycle cost and robust system performance can be realized. The optimal design will also vary with the concerned performance objectives and knowledge-based judgment. Further relationships and interpretations between design parameters and system responses are discussed through parametric analyses. [ABSTRACT FROM AUTHOR]

Variations of reservoir water level and seasonal precipitation have resulted in significant movement and destabilization of landslides in the Three Gorges Reservoir (TGR) region of China since reservoir impoundment in 2003. An example is the Baijiabao landslide, a large, actively creeping landslide located in the steep lower valley of the Xiangxi River, about 55 km upstream of the TGR dam in the Yangtze River. Twelve years of monthly monitoring at four GPS stations and routine, monthly field observations show cumulative GPS displacements as large as >1.5 m and widely developed ground cracks. GPS monitoring results show that most movement takes place in rapid steps that coincide with the rainy season and the period of annual reservoir drawdown, with particularly large steps in 2009, 2012 and 2015. This step-like pattern of displacement is also shown by daily data from an automatic monitoring system installed in 2017. The total period of acceleration shown by these daily data was about six weeks long, with rapid movement starting during rapid reservoir drawdown, and terminating when the reservoir began to rise again. In particular, most of the 2018 displacement occurred in only two weeks. Different subzones of the landslide move at different rates and exhibit different features of deformation. The neighborhood rough set theory is used to identify the triggering factors responsible for landslide deformation. The most important triggering factors vary between different sites, data types and the time interval used to define them. The surface deformation and ground crack widening are controlled by the combination of rainfall and variations in the reservoir water levels, whereas the deformation of the sliding zone is most sensitive to the latter. The results show that daily data are needed to capture important, short-term landslide responses. The neighborhood rough set theory for determination of triggering factors is suggested for deformation prediction, stability evaluation, and prevention and control of reservoir landslides in this and other regions. • Spatiotemporal deformation characteristics of Baijiabao landslide over a twelve-year period are analyzed in detail. • Different acceleration periods and threshold hydraulic conditions are observed from daily and monthly monitoring data. • Neighborhood rough set theory is used to identify the triggering factors responsible for landslide deformation. • The most important triggering factors vary between different sites, data types and the time interval used to define them. [ABSTRACT FROM AUTHOR]

Based on the machine learning algorithms, prediction models for landslide displacement with step-like behavior in the reservoir area were established for landslides prevention and reduction; these models could predict a given test set very well. However, due to the length and the sequence of the training set in prediction models, the predictive ability of these prediction models could not be evaluated accurately if just validated with a given test set. To solve the problem, a hybrid reliability model was proposed. Complimentary ensemble empirical mode decomposition (CEEMD) algorithm was used to decompose the accumulated displacement into the trend displacement and the periodic displacement firstly. The Gauss function was used to predict the trend displacement, and the random forest (RF) and back propagation neural network (BPNN) algorithms were employed to predict the periodic displacement. Furthermore, a novel performance function for the reliability analysis of the displacement prediction model was derived to address failure probabilities in different cases. The Baijiabao landslide in the Three Gorges Reservoir Area was taken as an example for reliability analysis of the prediction model for landslide displacement with step-like behavior, and the predictive ability of the CEEMD-RF model and the CEEMD-BPNN model were compared. The results indicated that the CEEMD-RF model and CEEMD-BPNN model both can accurately predict the accumulated displacement of the given test set; the predictive value obtained with the CEEMD-RF model or the CEEMD-BPNN model showed uncertainties of the prediction model for landslide displacement and the predictive ability of the CEEMD-RF model was more reliable than the CEEMD-BPNN model under different cases. The failure probability proposed in the paper could evaluate the predictive ability of the model more accurately and comprehensively compared with the existing assessment indices. [ABSTRACT FROM AUTHOR]

Understandings of reinforcement mechanisms of landslide-stabilizing pile system are important for long-term safety of reservoir landslides installed piles. The paper proposes a framework to study deformation response and identify triggering factors for landslide–pile system by using geographic detector technology and uncertainty of monitoring data. Majiagou landslide, a representative reservoir landslide installed stabilizing and test piles, is selected as the case study. Firstly, monitoring data of monthly rainfall, variations of reservoir water level, deformation of landslide surface and piles' head were preprocessed. The random deformation data were generated considering uncertainty of deformation monitoring data. Meanwhile, the deformation response of landslide–pile system is analyzed through studying the monitoring deformation data and random deformation data using the clustering algorithm. Finally, geographic detector technique was used to identify main triggering factors of landslide surface deformation and explore interaction types of any two factors. The uncertainty of monitoring data was used to identify the most important triggering factor. Influences of different error and uncertainty of monitoring data on the influence degrees of each factor were further discussed. The comparison with improved Apriori algorithm shows that the presented framework can intuitively measure influence degrees of each factor and analyze interaction types of any two factors. The main conclusions by studying Majiagou landslide indicate that the anti-slide performance and action range of piles gradually decrease with the increase of hydraulic cycle; the deterioration of geomaterials' properties are the most important triggering factor leading to the deformation of landslide–pile system and the degradation of piles' performance, supported by most random deformation groups. [ABSTRACT FROM AUTHOR]

The construction of the huge Three Gorges reservoir affected a large region, and the resultant geological and environmental impacts have caused global concern. The remarkable, 30-m annual fluctuation in the reservoir water level poses a significant threat to slope stability in this area. Four hundred sixty-two landslides were identified in the Zigui basin using historical records, satellite images, field investigations and unmanned aerial vehicle (UAV) observations, enabling the construction of a complete landslide database and distribution map. Three failure modes of landslides in the Zigui basin are used to illustrate the major factors that govern reservoir-induced landslides. The results show that (1) > 99% of identified landslides occur on slopes angle <47°, while >80% occur at elevations below 600 m; (2) Jurassic Niejiashan Formation is highly prone to landslides; (3) Low reservoir levels of 145 m to 155 m greatly reduce slope stability. Based on the information entropy method that use the conditional probability of different influencing factors and principal component analysis as inputs, the relative contributions of various influencing factors are quantified and a landslide susceptibility map was drawn. This susceptibility map helps define the countermeasures that will best reduce fatalities and property losses for areas having different landslide conditions and susceptibilities. • Database of 462 reservoir-induced landslides in Zigui basin, TGR was created. • Identifying failure modes and quantifying contributions of influential factors. • Conditional probability and information entropy model yields landslide susceptibility. • Susceptibility map, failure modes, and strategies suppressing the slope instability. [ABSTRACT FROM AUTHOR]