Slope reliability analysis through Bayesian sequential updating integrating limited data from multiple estimation methods

Yao, Wenmin; Li, Changdong; Yan, Changbin; Zhan, Hongbin

doi:10.1007/s10346-021-01812-4

Cited by 9 publications

(5 citation statements)

References 67 publications

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“…The single factor analysis method is used to study the effects of inverted T-type retaining wall structure dimensions and mechanical parameters of filling, in which the width of the heel bottom plate b 1 , the width of bottom plate b 3 , the bottom plate thickness H 2 , the soil–wall interface element reduction coefficient K, the soil interface friction angle φ and the soil cohesion C are selected influencing factors for the failure characteristics of sliding surface and the earth pressure distribution law of retaining wall in active limit state. Geometric dimensions of the retaining wall and mechanical parameters for working conditions are shown in Table 2 , in which the retaining wall’s height H = 6m, the wall stem is entirely vertical with a width of b 2 = 0.5m, the buried depth at the bottom of the wall D is always greater 0.5m than the bottom plate thickness; a correlation of the investigated range of soil parameters to conventional values for retaining wall design would be insightful; the values of soil parameters are determined according to the engineering geological manual on soil properties [ 28 ] and other relevant retaining wall papers [ 29 – 32 ]. In Group 1, the wall heel width b 1 is 1m, 2m, 3mand 4m, respectively; In Group 2, the wall toe width b 3 is 1m, 2m, 3m and 4m, respectively; In Group 3, the bottom plate thickness H 2 is 0.5m, 0.75m, 1.0m and 1.25m, respectively; In Group 4, the soil–wall interface element reduction coefficient K is 0, 0.333, 0.666 and 1, respectively; In Group 5, the soil cohesion C is 5KPa, 15KPa, 25KPa and 35KPa, respectively; In Group 5, the soil internal friction angle φ is 10°, 20°, 30° and 40°, respectively.…”

Section: Failure Mechanism Analysis Against Inverted T-type Retaining...mentioning

confidence: 99%

“…0.5m, the buried depth at the bottom of the wall D is always greater 0.5m than the bottom plate thickness; a correlation of the investigated range of soil parameters to conventional values for retaining wall design would be insightful; the values of soil parameters are determined according to the engineering geological manual on soil properties [28] and other relevant retaining wall papers [29][30][31][32]. In Group 1, the wall heel width b 1 is 1m, 2m, 3mand 4m, respectively; In Group 2, the wall toe width b 3 is 1m, 2m, 3m and 4m, respectively; In Group 3, the bottom plate thickness H 2 is 0.5m, 0.75m, 1.0m and 1.25m, respectively; In Group 4, the soilwall interface element reduction coefficient K is 0, 0.333, 0.666 and 1, respectively; In Group 5, the soil cohesion C is 5KPa, 15KPa, 25KPa and 35KPa, respectively; In Group 5, the soil internal friction angle φ is 10˚, 20˚, 30˚and 40˚, respectively.…”

Section: Plos Onementioning

confidence: 99%

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Study on the failure characteristics of sliding surface and stability analysis of inverted t-type retaining wall in active limit state

Zeng,

Hu,

Chen

et al. 2024

PLoS ONE

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This paper investigates the sliding surface failure characteristics, earth pressure distribution law and stability safety factor of inverted T-type retaining wall by using the finite element limit analysis software OptumG2, the effects of width of wall heel plate, width of wall toe plate, thickness of bottom plate, soil–wall interface friction angle, soil cohesion and soil internal friction angle of filling on the failure characteristics of sliding surface, the earth pressure distribution law and stability safety factor of retaining walls are analyzed, The stability safety factor of the retaining wall showed a gradually increasing trend as the width of wall heel plate and wall toe plate increased; as the bottom plate thickness increases, the stability safety factor of the retaining wall gradually increases; as the soil-wall interface element reduction coefficient rises, that is, the internal friction angle of the soil-wall gradually increases to the soil internal friction angle, the stability safety factor of the retaining wall gradually increases; as the soil cohesion and internal friction angle increase, the stability safety factor of the retaining wall progressively increases. The safety factor of retaining wall increases by 0.45 for every 0.5m increase in the width of the wall heel plate; the safety factor of the retaining wall increases by 0.29 when the width of the wall toe plate increases by 0.5m; for every 0.5m increase in the width of wall plate thickness, the safety factor of the retaining wall is increased by 0.62; for every 0.25 increase in soil-wall interface element reduction coefficient, the safety factor of the retaining wall increases by 0.29; for every increase of 5KPa in soil cohesion, the safety factor of the retaining wall increased by 1.16; for every 5° increases in soil internal friction angle, the safety factor of retaining wall increases by 0.6. The research is significant for studying the failure laws and stability of retaining walls and providing references for retaining wall design.

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Section: Failure Mechanism Analysis Against Inverted T-type Retaining...mentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Study on the failure characteristics of sliding surface and stability analysis of inverted t-type retaining wall in active limit state

Zeng,

Hu,

Chen

et al. 2024

PLoS ONE

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“…Generally speaking, meteorological, geological, hydrological, and human factors are the main causes of landslides (Harris et al 2009;Fan et al 2020; Xian et al 2022;Yao et al 2022). In recent years, remote sensing technology has been widely used in landslide deformation monitoring, risk assessment, and landslide identi cation (Kääb 2008; Hu and Shan 2016; López-Vinielles et al 2020), and has gradually become an important means of landslide research.…”

Section: Introductionmentioning

confidence: 99%

Process and numerical simulation of landslide sliding caused by permafrost degradation and seasonal precipitation

Zhang,

Ma,

Shan

et al. 2024

Nat Hazards

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There are few types of research on the occurrence mechanism and deformation characteristics of landslides induced by permafrost degradation. With the aggravation of climate warming, landslides are more and more common in permafrost regions. A slow landslide (the K178 + 530 landslide) in the permafrost region of the Xiao Xing'an Mountains in Northeast China was monitored for a long time. The deformation characteristics and occurrence mechanism of the landslide were studied using eld investigation, on-site drilling, sensor monitoring, laboratory test, Google satellite image, unmanned aerial vehicle (UAV) photogrammetry, high-density electrical method, and ground-penetrating radar. In addition, a hydro-thermal-mechanical coupling model of frozen soil under saturated conditions was established to simulate the deformation process, pore water pressure change, and effective stress distribution of the slope, and the simulation results were veri ed according to the monitored data. The results show that the meltwater recharge caused by permafrost degradation reduced the cohesion and internal friction angle of the soil near the trailing edge of the landslide, thus providing dynamic and mechanical conditions for slope deformation. The melting of the continuous segregation ice in the active layer contributed to the formation of a sliding surface and provided deformation conditions for the start of the landslide. The combination of these two factors nally led to the occurrence of the landslide. According to its deformation mechanism, it can be judged that the landslide is a thrust-type landslide. In addition, the melting of ice lenses in the seasonally frozen layer is the main source of soil strength damage, and the landslide sliding rate reached the maximum when the ice lens melted completely. The K178 + 530 landslide is a typical case of landslides caused by permafrost degradation. This study provides a reference for the identi cation, early warning, and prevention measures of this type of landslide.

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“…China is one of the countries where geological disasters are most frequently happened in the world, the slopes and landslides with potential damage risks are widely distributed in the natural environment (Dai et al, 2002). Once landslides or slopes failure occur, the consequences can be catastrophic, which greatly threaten human life and property (Yao et al, 2022). Therefore, landslide or slope stability analysis is one of the most important problems in the field of geological hazards and geotechnical engineering.…”

Section: Introductionmentioning

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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Slope reliability analysis through Bayesian sequential updating integrating limited data from multiple estimation methods

Cited by 9 publications

References 67 publications

Study on the failure characteristics of sliding surface and stability analysis of inverted t-type retaining wall in active limit state

Study on the failure characteristics of sliding surface and stability analysis of inverted t-type retaining wall in active limit state

Process and numerical simulation of landslide sliding caused by permafrost degradation and seasonal precipitation

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

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