Probabilistic Risk Assessment of unsaturated Slope Failure Considering Spatial Variability of Hydraulic Parameters

被引:1
作者
Lin Wang
Chongzhi Wu
Yongqin Li
Hanlong Liu
Wengang Zhang
Xiang Chen
机构
[1] Chongqing University,School of Civil Engineering
[2] Chongqing Three Gorges University,School of Civil Engineering
来源
KSCE Journal of Civil Engineering | 2019年 / 23卷
关键词
unsaturated slope; risk assessment; spatial variability; hydraulic parameters; random field;
D O I
暂无
中图分类号
学科分类号
摘要
Probabilistic risk assessment of slope failure evaluates the slope safety in a quantitative manner, which considers the failure probability and failure consequence simultaneously. However, risk assessment of unsaturated slope accounting for spatially variable soil-water characteristic curve (SWCC) model parameter and saturated hydraulic conductivity has been rarely reported. A probabilistic risk assessment approach is proposed in current study for rationally quantifying the unsaturated slope failure risk with the aid of Monte Carlo (MC) simulation. The SEEP/W and SLOPE/W modules contained in Geostudio software are applied to carry out deterministic analysis, where factor of safety (FS) of the unsaturated slope is calculated by Morgenstern–Price method. The spatially variable hydraulic parameters are characterized by their respective random fields that are transferred from the random void ratio field in this study, rather than generating them separately. The proposed approach is subsequently employed to an unsaturated slope example for exploring the influences of spatially variable void ratio. Results show that the unsaturated slope failure risk is considerably affected by the spatially variable void ratio, and the single exponential autocorrelation function (ACF) popularized in geotechnical engineering tends to underestimate the failure risk in the unsaturated slope risk assessment.
引用
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页码:5032 / 5040
页数:8
相关论文
共 128 条
[31]  
Li D Q(2015)Estimation of permeability function from the soil-water characteristic curve. Engineering Geology 199 148-195
[32]  
Zhang L M(2013)Multivariate adaptive regression splines for analysis of geotechnical engineering systems. Computers and Geotechnics 48 82-37
[33]  
Zhou C B(2016)Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geoscience Frontiers 7 45-2225
[34]  
Le T M H(2016)Risk assessment of slope failure considering multiple slip surfaces. Computers and Geotechnics 74 188-222
[35]  
Gallipoli D(2014)Geotechnical reliability analysis with limited data: Consideration of model selection uncertainty. Engineering Geology 181 27-undefined
[36]  
Sanchez M(2018)Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method. Landslides 15 2211-undefined
[37]  
Wheeler S(2017)Comparison of two optimized machine learning models for predicting displacement of rainfall-induced landslide: A case study in Sichuan. Engineering Geology 218 213-undefined
[38]  
Le T M H(undefined)undefined undefined undefined undefined-undefined
[39]  
Gallipoli D(undefined)undefined undefined undefined undefined-undefined
[40]  
Sánchez M(undefined)undefined undefined undefined undefined-undefined