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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]

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]

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]