Three-dimensional landslide evolution model at the Yangtze River

Wang, Jinge; Schweizer, Daniel; Liu, Qingbing; Su, Aijun; Hu, Xinli; Blum, Philipp

doi:10.1016/j.enggeo.2021.106275

Cited by 51 publications

(17 citation statements)

References 59 publications

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“…Tens of thin and weak layers (i.e., 0.3-1 m thick) composed of dark gray silt clay with gravel or gray-black carbonaceous shale are intercalated in this formation, which has been identified as the possible sliding surfaces of the landslide (Wang et al, 2021).…”

Section: Investigated Landslidementioning

confidence: 99%

“…The data confirms that the deformation of this layer developed synchronously with that of the main slip surface (Figures 4a and 4d). Given that the landslide sliding zones in the TGR area feature silty clay with gravel (Wang et al., 2021; Yin et al., 2016), the material composition at this level provides another evidence on whether it is a secondary slip surface. Further tracking and multi‐source sensor measurements will allow for a more comprehensive knowledge of this shallow slip surface.…”

Section: Identification Of Driving the Accelerated Movementmentioning

confidence: 99%

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Subsurface Multi‐Physical Monitoring of a Reservoir Landslide With the Fiber‐Optic Nerve System

Zhu

Wang

et al. 2022

Geophysical Research Letters

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Reservoir landslides are typical geohazards with sophisticated thermo‐hydro‐mechanical interactions. However, direct observations of subsurface multi‐physical processes in bank slopes remain rare. Herein we present the design, implementation and evaluation of an innovative fiber‐optic nerve system based on weak‐reflection fiber Bragg grating to monitor a giant landslide located in the Three Gorges Reservoir region, China. The system is capable of measuring and imaging spatiotemporal distributions of temperature, moisture and strain along boreholes in near real‐time. Such visual profiles offer unique insights into the subsurface thermo‐hydro‐mechanical link between external conditions and geotechnical deformation, making it possible to decipher the substantial trigger of accelerated movements. Multi‐physical responses at depths of interest also enable us to better understand the evolution mechanism of reservoir landslides. Fiber‐optic nerve sensing can synergize with remote sensing and surface‐based technologies to build a space‐sky‐ground‐subsurface integrated monitoring system for landslides.

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Section: Investigated Landslidementioning

confidence: 99%

Section: Identification Of Driving the Accelerated Movementmentioning

confidence: 99%

Subsurface Multi‐Physical Monitoring of a Reservoir Landslide With the Fiber‐Optic Nerve System

Zhu

Wang

et al. 2022

Geophysical Research Letters

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“…Since the reservoir was first impounded in June 2003, the toe area of the slope has experienced repeated small collapses (Figure 6). From 4 to 14 June 2004, four sliding events occurred during a period of rainfall, involving an estimated volume of 6000 m 3 . The toe area is very unstable, and slope movements at the toe affect the rest of the slope.…”

Section: Deformation Characteristicsmentioning

confidence: 99%

“…In 2019, there were approximately 6181 geological hazard events in China, causing economic losses of 2.77 billion yuan. Among these, 4220 were landslides, accounting for 68% of the geological hazards [3]. The development of more accurate and effective landslide displacement prediction methods is of great significance for the early warning of catastrophic landslide movements and is an active research area [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Model for Landslide Displacement Prediction Based on EDR Selection and Multi-Swarm Intelligence Optimization Algorithm

Zhang

Tang

Tannant

et al. 2021

Sensors

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With the widespread application of machine learning methods, the continuous improvement of forecast accuracy has become an important task, which is especially crucial for landslide displacement predictions. This study aimed to propose a novel prediction model to improve accuracy in landslide prediction, based on the combination of multiple new algorithms. The proposed new method includes three parts: data preparation, multi-swarm intelligence (MSI) optimization, and displacement prediction. In the data preparation, the complete ensemble empirical mode decomposition (CEEMD) is adopted to separate the trend and periodic displacements from the observed cumulative landslide displacement. The frequency component and residual component of reconstructed inducing factors that related to landslide movements are also extracted by the CEEMD and t-test, and then picked out with edit distance on real sequence (EDR) as input variables for the support vector regression (SVR) model. MSI optimization algorithms are used to optimize the SVR model in the MSI optimization; thus, six predictions models can be obtained that can be used in the displacement prediction part. Finally, the trend and periodic displacements are predicted by six optimized SVR models, respectively. The trend displacement and periodic displacement with the highest prediction accuracy are added and regarded as the final prediction result. The case study of the Shiliushubao landslide shows that the prediction results match the observed data well with an improvement in the aspect of average relative error, which indicates that the proposed model can predict landslide displacements with high precision, even when the displacements are characterized by stepped curves that under the influence of multiple time-varying factors.

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“…The admixtures for cement-stabilized soils are generally categorized as either mineral or chemical admixtures [5]. Fly ash [6], slag [7,8] and silica fume [9] are the typical mineral admixtures and their modification effects and corresponding mechanisms have long been investigated and are well clarified. Regarding chemical admixtures, one common type comprises the alkaline activators R 2 O-SiO 2 , ROH, R 2 SO 4 , R 2 CO 3 (R = K or Na or 0.5 Ca) and combinations of these [3,[10][11][12].…”

Section: Introductionmentioning

confidence: 99%

An Investigation on Mineral Dissolution and Precipitation in Cement-Stabilized Soils: Thermodynamic Modeling and Experimental Analysis

Hua

et al. 2022

Applied Sciences

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Thermodynamic modeling helps to reveal insights into the basic chemical kinetics of dissolution and precipitation in cementitious materials, but relevant applications to cement-stabilized soils have seldom been reported. Based on the thermodynamic database of Cemdata18 and the pore solution composition of cement-stabilized soils, this study formulated a specialized thermodynamic model, using essential thermodynamic constants for soil minerals that were calculated to ensure the model’s accuracy. Two commercial admixtures of alkaline activator and polynaphthalene sulfonate were selected for the different modification mechanisms and plain and modified cement-stabilized soils were prepared. Compressive strength was tested to determine the specimens for pore solution analysis and the influences of the admixture type and dose on dissolution and precipitation were investigated by modeling the ionic activity products and saturation indexes. An X-ray diffraction (XRD) analysis was performed to verify and complement the thermodynamic results. The major research findings were that (1) thermodynamic modeling can be reliably applied to cement-stabilized soils by providing the essential thermodynamic data and an appropriate product model, (2) the pozzolanic reaction is accelerated by increasing the OH− concentration in the pore solution, while the cement hydration is highly dependent on the dissolution of Ca(OH)2 and the relevant complexes and (3) the dissolution equilibrium of Ca(OH)2 is directly affected by the alkaline activator dissolution and is indirectly affected by the polynaphthalene sulfonate adsorption of the reactants.

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Three-dimensional landslide evolution model at the Yangtze River

Cited by 51 publications

References 59 publications

Subsurface Multi‐Physical Monitoring of a Reservoir Landslide With the Fiber‐Optic Nerve System

Subsurface Multi‐Physical Monitoring of a Reservoir Landslide With the Fiber‐Optic Nerve System

A Novel Model for Landslide Displacement Prediction Based on EDR Selection and Multi-Swarm Intelligence Optimization Algorithm

An Investigation on Mineral Dissolution and Precipitation in Cement-Stabilized Soils: Thermodynamic Modeling and Experimental Analysis

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