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The well-known slide-prone red-stratum mudstones in the Three Gorges reservoir (TGR) area are currently undergoing sharp changes in (i) water level driven by the impounding/discharging operation of the Three Gorges dam and (ii) the rain/water H+ concentration driven by recent rapid industrial/agricultural development, and these changes are expected to impact geohazards. This potential impact emphasizes the importance of understanding the role of pH in the drying-wetting disintegration of red-stratum mudstones. Although many investigations have focused on the role of pH in mudstone disintegration, the red-stratum mudstones in the TGR area differ from mudstones elsewhere, and the role of pH has seldom been studied in these rocks. This work adds to the limited body of research by (1) experimentally investigating the possible role of pH in red-stratum mudstone drying-wetting disintegration and (2) revealing the micromechanisms responsible for this role. In the experiment, six disintegration performance indexes and two mechanical indexes, i.e. particle size gradation, slake durability index, decay constant, surface energy, box dimension, fractal dimension, cohesion and friction angle, were monitored throughout the disintegration process. A tri-piecewise linear fractal model is proposed to better describe the particle size distribution. This research shows that the acid enhancements stimulate the disintegration of red-stratum mudstones and significantly affect shear strength. In comparison, alkaline environments inhibit the mudstone fragmentation and slightly degrade the shear strength. Two possible micromechanisms and three potential effects of acid or alkaline agents are presented to account for the role of pH. The findings provide insights into the potential geohazard impacts of sharp changes in the exposure environment (i.e., water level and H+ concentration) of the red-stratum mudstones in the TGR area. • Established a multiphysics database of pH versus disintegration performance indexes. • Revealed the role of pH in drying-wetting disintegration of red-stratum mudstones. • Clarified the micromechanisms of the pH role in red-stratum mudstone disintegration. • Proposed a tri-piecewise linear fractal model of particle size distribution. • Challenged the previous understanding of the pH role/mechanisms in disintegration. [ABSTRACT FROM AUTHOR]

• New energy-evolution perspective is provided on failure mechanism of b4 mudstones. • Energy-parameters estimation methods under cyclic loading-unloading are presented. • A parameter to measure proportion of CDE is introduced, which is defined as CDE/TAE. • Influence of stress path on mechanical properties and energy evolution is revealed. Purplish-red mudstones from Section Four of the Badong Formation are broadly distributed in the Three Gorges Reservoir area (TGRA) and exhibit special characteristics distinct from the mudstones elsewhere. This paper investigates the failure mechanism of such mudstones from a new perspective of energy evolution. Initially, the complete failure process under three stress paths (i.e., loading, unloading, and cyclic loading-unloading) is monitored, and the related data are acquired. Methods to estimate the energy parameters (i.e., total absorption energy (TAE), cumulative dissipation energy (CDE), and elastic strain energy (ESE)) under cyclic loading-unloading conditions are presented. Two new parameters, namely, the proportion of CDE (PCDE) and proportion of elastic energy dissipation (PEED), are introduced. Using the data, the mechanical properties and energy properties are analyzed, and the energy evolution is revealed. The results show that the failure mechanism under the three stress paths is different from an energy evolution perspective. The mechanical parameters deteriorate under unloading in comparison with those under loading. The increase in the CDE is similar to that in the TAE, and the ESE-strain relationship features a step-shaped rise. For cyclic loading-unloading, the TAE and the PCDE nearly linearly increase with cycle times. This work provides a new perspective on the failure mechanism of the purplish-red mudstones from the TGRA. [ABSTRACT FROM AUTHOR]

The impoundment of the 660-km long reservoir behind the huge Three Gorges Dam, the world's largest hydropower station, increased regional seismicity and reactivated severe geohazards. Before the reservoir filling was initiated in 2003, the region had approximately two earthquakes per year with magnitudes between 3.0 and 4.9; after the full impoundment in 2008, approximately 14 earthquakes per year occurred with magnitudes between 3.0 and 5.4. In addition, hundreds of landslides were reactivated and are now in a state of intermittent creep. Many landslides exhibit step-like annual pattern of displacement in response to quasi-regular variations in seasonal rainfall and reservoir level. Additional problems include rock avalanches, impulse waves and debris flows. The seriousness of these events motivated numerous studies that resulted in 1) Better insight into the behavior and evolution mechanism of geohazards in the Three Gorges Reservoir Area (TGRA); 2) Implementation of monitoring and early-warning systems of geohazards; and 3) Design and construction of preventive countermeasures including lattice anchors, stabilizing piles, rock bolts, drainage canals and tunnels, and huge revetments. This paper reviews the hydro-geologic setting of TGRA geohazards, examines their occurrence and evolution in the past few decades, offers insight learned from extensive research on TGRA geohazards, and suggests topics for future research to address the remaining challenges. [ABSTRACT FROM AUTHOR]

Reservoir landslide evolution is a complex multi-fields dynamic process which involves the interaction of the seepage field, stress field, displacement field and temperature field. After installing the stabilizing structure into landslide, the landslide and stabilizing structure will establish the landslide-stabilization system and lead to a more complex multi-fields evolution process under the reservoir operation. To study this complex problem, this paper designs and establishes a comprehensive in-situ multi-fields information monitoring platform which has been installed on Majiagou landslide in Three Gorges Reservoir area. The multi-fields information monitoring platform has four components: the landslide-stabilization system, the in-situ multi-field monitoring system, the data transmission and receiving system, and the database management system. The landslide-stabilization system includes Majiagou landslide and two experimental reinforced concrete piles. The in-situ monitoring system consists of landslide surface deformation monitoring system, experimental piles monitoring system and boreholes monitoring system. The landslide surface deformation monitoring system includes GPS stations and fiber lines. The experimental pile monitoring system consists of earth pressure cells around piles, stress and strain gauges on steel bars, and strain and temperature fiber sensors in piles. The borehole monitoring system includes boreholes with inclinometers, water pressure and water level gauges, fiber sensors. Therefore, the in-situ monitoring system can monitor the seepage, stress, displacement and temperature fields in the landslide, as well as the force and deformation in the experimental piles. Currently, the data transmission and management system is under construction and will be finished soon. Current monitoring results show that the landslide moves along three slip surfaces and the deformation of the experimental piles is influenced by reservoir operation. This platform can provide multi-fields data for scholars to study the evolution of landslide-stabilization system. [ABSTRACT FROM AUTHOR]

Abstract Geological disasters have occurred frequently in the Three Gorges Reservoir area. Therefore, the stability and failure mode research on riverside landslides has become increasingly important. Taking the toe of Liangshuijing landslide as a case study and combining the geological conditions of the Three Gorges Reservoir area, a centrifuge slope model is constructed. In addition, the fluctuation of the water level is simulated in the centrifuge model test. Based on the digital photography, laser displacement sensors (LDS), pore pressure transducers (PPT) and earth pressure sensors (E), multi-physical data are obtained. Finally, numerical simulation is carried out to analyze the stability and deformation characteristics of the slope model. The results indicate that: (1) At the water level rise stage, the model is dominated by the vertical displacement. And the horizontal displacement is much larger than the vertical displacement at the drainage stage. (2) Cracks first appear at the toe of the slope and extend to the middle and rear of the model gradually. Then, deformation occurs in the middle platform of the slope. (3) At the water level rise stage, the factor of safety increases rapidly. When the reservoir water level remains stable, the stability of the slope decreases slowly. And at the drainage stage, the factor of safety decreases first and then increases. (4) There is a significant positive linear correlation between the decline rate of the safety factor and the drainage rate over a certain test range. And the stability of the landslides on the reservoir banks would become worse gradually during the long-term cyclic operation of the Three Gorges Reservoir. Highlights • The fluctuation of the water level is simulated in the centrifuge model test. • Qualitative and quantitative method are combined to analysis the evolution of the landslide. • The failure mode and the deformation mechanism of the slope are revealed by the centrifuge model test. [ABSTRACT FROM AUTHOR]

Landslide early warning systems can be implemented based on the monitoring and prediction of landslide displacements. The internal mechanisms of landslides are very complex, and precise mechanistic models of landslides are difficult to obtain; therefore, data-driven models are usually applied. From the perspective of dynamic system theory, landslide development should be considered a dynamic process. Because traditional models, such as feed-forward neural networks, can only express static relationships, the applicability of these static models is quite limited in landslide prediction tasks. In our study, recurrent neural networks are established and trained into dynamic predictors of landslide displacement using a training algorithm named reservoir computing. A shortcoming of the reservoir computing algorithm is that the models will not perform well with insufficiently large training sets. Because practical landslide displacement recordings are usually quite short, interpolation techniques are employed to enhance the training of the dynamic predictors. The method proposed in this paper is applied to predict the developments of three landslides in the Three Gorges area, and the predicted values are close to the actual measurements. Improvements relative to sophisticated static predictors and basic dynamic predictors are obvious. [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]