Microseismic Monitoring and 3D Finite Element Analysis of the Right Bank Slope, Dagangshan Hydropower Station, during Reservoir Impounding

Liu, Xingzong; Tang, Chun’an; Li, Lianchong; Lv, Pengfei; Liu, Hongyuan

doi:10.1007/s00603-017-1191-5

Cited by 35 publications

(9 citation statements)

References 44 publications

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“…How to utilize all the multi‐source data more adequately and to explore the internal connection between each feature and the slope stability is the focus of the feature engineering in the next. More importantly, since the Jiuxianping landslide is a rock landslide, commonly used rock damage monitoring techniques, such as stress–strain monitoring (Fantini et al, 2017) and microseismic monitoring (X. Liu et al, 2017), can be applied here theoretically. We are devoted to applying the most effective monitoring techniques to rock landslides and high steep slopes in the Three Gorges reservoir area, evaluating the stability of landslides based on multi‐source heterogeneous monitoring data, and contributing to the mitigation and prevention of geological hazards.…”

Section: Resultsmentioning

confidence: 99%

Dynamic assessment of slope stability based on multi‐source monitoring data and ensemble learning approaches: A case study of Jiuxianping landslide

Kang

Chen

et al. 2022

Geological Journal

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Accurate assessment of slope stability is the most important task in geological disaster prevention and control. This study developed an ensemble learning approach based on stacking strategy and eight commonly used machine learning (ML) models, for exploring the feasibility of the factor of safety (FS) prediction using dynamic multi‐source monitoring data of slopes and landslides. Based on long‐term and dynamic field monitoring and numerical calculation, a dataset for constructing the FS prediction model for the Jiuxianping landslide was established. The dataset includes five types of monitoring data namely rainfall, reservoir water level, groundwater level, surface displacement and deep displacement for a total of nine features, and one label FS. Four regularized regression models, kernel ridge regression (KRR), lasso, elastic net and support vector regression (SVR), as well as four ensemble learning models, random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), were adopted to obtain the nonlinear association between the nine features and the label FS, respectively. Based on five repeated 5‐fold cross‐validation (CV) and successive halving (SH) hyperparameter searching method, the hyperparameters of each model were determined, and the prediction effects of each optimal model were compared. The results show that the ensemble learning models outperform the common regression models. Furthermore, with the help of the stacking ensemble learning thinking, four excellent ensemble models were combined, and the final stacking ensemble learning model was used to predict the FS of the Jiuxianping landslide. The results indicate that the stacking model has better robustness and generalization performance. Besides, the feature relative importance of four ensemble learning models was analysed, for enhancing the interpretability of ML models and pointing out the research direction of feature engineering in the future.

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Section: Resultsmentioning

confidence: 99%

Dynamic assessment of slope stability based on multi‐source monitoring data and ensemble learning approaches: A case study of Jiuxianping landslide

Kang

Chen

et al. 2022

Geological Journal

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“…The foundation in the land reclamation project in Dongjiang, Tianjin 32 was mainly composed of silty soil. The vertical drainage board used in the process of foundation reinforcement was SPB‐C, which was inserted into the foundation at the depth of 20 m. The loading method used in the project was vacuum and surcharge preloading and the loading process is shown in Figure 3.…”

Section: Validation Of the Solutionmentioning

confidence: 99%

“…The settlements calculated at different depths under the same loading process are compared with the measured values 32 in this paper and the comparison is shown in Figure 4. As can be seen in the Figure 4, the results calculated by proposed model are basically consistent with the measured values, which further validates the correctness of the model considering vacuum preloading and variable well resistance.…”

Section: Validation Of the Solutionmentioning

confidence: 99%

“…The vertical drainage board used in the process of foundation reinforcement was SPB-C, which was inserted into the foundation at the depth of 20 m. The loading method used in the project was vacuum and surcharge preloading and the loading process is shown in Figure 3. The settlement values at the depths of 0 , 5 , 10 as well as 15 m were measured in the paper 32 and the specific calculation parameters are shown in Table 2. The settlements calculated at different depths under the same loading process are compared with the measured values 32 in this paper and the comparison is shown in Figure 4.…”

Section: Validation Of the Solutionmentioning

confidence: 99%

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Variation of negative vacuum pressure in vertical drains with time and depth under variable well resistance

Li,

Guo,

Wang

2024

Num Anal Meth Geomechanics

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Most solutions for the large strain nonlinear consolidation of soils with vertical drains are based on the assumption that the negative vacuum pressure only depends on the depth. In response to the shortcomings of this assumption, a large strain nonlinear consolidation model of soils with vertical drains under vacuum preloading is developed by considering the time‐and depth‐dependent well resistance. By fitting the data under different well resistances, the influence of well resistance parameters on the negative vacuum pressure variation in vertical drains is investigated, and the relevant empirical formulas describing the negative vacuum pressure variation in vertical drains are provided. The results obtained from the empirical formulas are in good agreement with the theoretical results as well as the experimental data, by which the reliability of the proposed empirical formulas is verified. The advantages of proposed empirical model can be identified by comparing it with the conventional model in which negative vacuum pressure is only linearly distributed with depth.

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“…Additionally, the effectiveness of RFPA has been verified by many benchmarks (Xu et al 2021;Wang et al 2022). Meanwhile, RFPA has been applied in the stability assessment (Chen et al 2022;Gong et al 2022a), failure mechanism (Zhou et al 2018;Gong et al 2022b) and instability precursor (Liu et al 2017;Zhang et al 2021) of rock mass.…”

Section: The Rpfa Code Enhanced By Dicmentioning

confidence: 99%

Numerical Investigation on Anisotropy and Shape Effect of Mechanical Properties of Columnar Jointed Basalts Containing Transverse Joints

2022

Self Cite

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We studied the non-linear mechanical response and failure mechanism of columnar jointed basalts (CJBs) with transverse joints by modeling meso-mechanics, statistical damage theory and continuum mechanics. The anisotropy and shape effect of CJBs with transverse joints were captured under different lateral pressures. The digital images were transformed into heterogeneous element meshes, and the gradual fracturing process and various failure modes of CJBs were reproduced. The compressive strength (CS) and equivalent deformation modulus (EDM) of CJBs parallel and perpendicular to the column axis were studied. The results show that the U-shaped CS curve of CJB appears as the column dip angle increases, and the CS is obviously improved as the lateral pressure increases when the column dip angle is 0°–90°. When the shape of CJB changes from 6 m × 3 m, 3 m × 3 m to 1.5 m × 3 m, the CS continues to increase. Meanwhile, the transverse joints are proven to be critical for determining the mechanical properties of CJBs at the certain dip angles of columns. However, the high lateral pressure can reduce the CS difference between the CJBs with and without the transverse joints. Besides, as the elastic modulus of joints rises, the CS will grow up, and the EDM will increase first and then almost remain at the same level. The coefficient of rock residual strength has a great influence on the CS at the certain dip angles of columns. Additionally, the model boundary significantly affects the anisotropy and shape effect of mechanical properties of CJBs under compression. These conclusions will improve our knowledge of the failure mechanisms and failure patterns of CJBs containing transverse joints.

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Microseismic Monitoring and 3D Finite Element Analysis of the Right Bank Slope, Dagangshan Hydropower Station, during Reservoir Impounding

Cited by 35 publications

References 44 publications

Dynamic assessment of slope stability based on multi‐source monitoring data and ensemble learning approaches: A case study of Jiuxianping landslide

Dynamic assessment of slope stability based on multi‐source monitoring data and ensemble learning approaches: A case study of Jiuxianping landslide

Variation of negative vacuum pressure in vertical drains with time and depth under variable well resistance

Numerical Investigation on Anisotropy and Shape Effect of Mechanical Properties of Columnar Jointed Basalts Containing Transverse Joints

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