A new method was proposed to cope with the earth slope reliability problem under seismic loadings. The algorithm integrates the concepts of artificial neural network, the first order second moment reliability method a...A new method was proposed to cope with the earth slope reliability problem under seismic loadings. The algorithm integrates the concepts of artificial neural network, the first order second moment reliability method and the deterministic stability analysis method of earth slope. The performance function and its derivatives in slope stability analysis under seismic loadings were approximated by a trained multi-layer feed-forward neural network with differentiable transfer functions. The statistical moments calculated from the performance function values and the corresponding gradients using neural network were then used in the first order second moment method for the calculation of the reliability index in slope safety analysis. Two earth slope examples were presented for illustrating the applicability of the proposed approach. The new method is effective in slope reliability analysis. And it has potential application to other reliability problems of complicated engineering structure with a considerably large number of random variables.展开更多
Slope reliability analysis considering inherent spatial variability(ISV)of soil properties is timeconsuming when response surface method(RSM)is used,because of the"curse of dimensionality".This paper propose...Slope reliability analysis considering inherent spatial variability(ISV)of soil properties is timeconsuming when response surface method(RSM)is used,because of the"curse of dimensionality".This paper proposes an effective method for identification of representative slip surfaces(RSSs)of slopes with spatially varied soils within the framework of limit equilibrium method(LEM),which utilizes an adaptive K-means clustering approach.Then,an improved slope reliability analysis based on the RSSs and RSM considering soil spatial variability,in perspective of computation efficiency,is established.The detailed implementation procedure of the proposed method is well documented,and the ability of the method in identifying RSSs and estimating reliability is investigated via three slope examples.Results show that the proposed method can automatically identify the RSSs of slope with only one evaluation of the conventional deterministic slope stability model.The RSSs are invariant with the statistics of soil properties,which allows parametric studies that are often required in slope reliability analysis to be efficiently achieved with ease.It is also found that the proposed method provides comparable values of factor of safety(FS)and probability of failure(Pf)of slopes with those obtained from direct analysis and lite rature.展开更多
Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters,making the slope reliability assessment closer to the engineering reality.However,multi-source information(includi...Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters,making the slope reliability assessment closer to the engineering reality.However,multi-source information(including test data,monitored data,field observation and slope survival records)is rarely used in current probabilistic back-analysis.Conducting the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating multi-source information is a challenging task since thousands of random variables and high-dimensional likelihood function are usually involved.In this paper,a framework by integrating a modified Bayesian Updating with Subset simulation(mBUS)method with adaptive Conditional Sampling(aCS)algorithm is established for the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction.Within this framework,the high-dimensional probabilistic back-analysis problem can be easily tackled,and the multi-source information(e.g.monitored pressure heads and slope survival records)can be fully used in the back-analysis.A real Taoyuan landslide case in Taiwan,China is investigated to illustrate the effectiveness and performance of the established framework.The findings show that the posterior knowledge of soil parameters obtained from the established framework is in good agreement with the field observations.Furthermore,the updated knowledge of soil parameters can be utilized to reliably predict the occurrence probability of a landslide caused by the heavy rainfall event on September 12,2004 or forecast the potential landslides under future rainfalls in the Fuhsing District of Taoyuan City,Taiwan,China.展开更多
Intense rainfall infiltration is one of the primary triggers for landslides.Developing a robust model for rainfall infiltration analysis is crucial for mitigating landslide disasters.Although the numerical solution of...Intense rainfall infiltration is one of the primary triggers for landslides.Developing a robust model for rainfall infiltration analysis is crucial for mitigating landslide disasters.Although the numerical solution of Richard's equation provides high computational accuracy,it often encounters convergence issues.In contrast,the Green-Ampt(GA)model,which is more computationally efficient,lacks accuracy in dealing with the non-uniform distribution of the initial volumetric water content(VWC)and the pore-water redistribution process.Therefore,this study proposes a novel model for analyzing the slope rainfall infiltration process based on the GA model.The proposed model discretizes both the geological layers of the slope and the rainfall event in spatial and temporal scales,respectively,improving accuracy by adjusting step sizes of discretization adaptively.The proposed model is applied to analyze the permeability,stability and reliability of heterogeneous infinite slopes considering uncertainties in multiple parameters.Comparative studies with the numerical solution of Richard's equation and other models demonstrate that the proposed model can provide high computational accuracy and superior analysis convergence in rainfall infiltration modeling.It also indicates that neglecting the pore-water redistribution underestimates the probability of slope failure,overestimates the factor of safety(FOS)of the slope,and inaccurately estimates the depth of the critical slip surface.Moreover,the uncertainties in shear strength parameters may overshadow the influence of initial VWC uncertainties on the slope reliability.This study provides a theoretical basis for the analysis of rainfall infiltration on heterogeneous slopes and the formulation of strategies for landslide disaster prevention.展开更多
文摘A new method was proposed to cope with the earth slope reliability problem under seismic loadings. The algorithm integrates the concepts of artificial neural network, the first order second moment reliability method and the deterministic stability analysis method of earth slope. The performance function and its derivatives in slope stability analysis under seismic loadings were approximated by a trained multi-layer feed-forward neural network with differentiable transfer functions. The statistical moments calculated from the performance function values and the corresponding gradients using neural network were then used in the first order second moment method for the calculation of the reliability index in slope safety analysis. Two earth slope examples were presented for illustrating the applicability of the proposed approach. The new method is effective in slope reliability analysis. And it has potential application to other reliability problems of complicated engineering structure with a considerably large number of random variables.
基金The work described in this paper was nancially supported by the Natural Science Foundation of China(Grant Nos.51709258,51979270 and 41902291),the CAS Pioneer Hundred Talents Pro-gram and the Research Foundation of Key Laboratory of Deep Geodrilling Technology,Ministry of Land and Resources,China(Grant No.F201801).
文摘Slope reliability analysis considering inherent spatial variability(ISV)of soil properties is timeconsuming when response surface method(RSM)is used,because of the"curse of dimensionality".This paper proposes an effective method for identification of representative slip surfaces(RSSs)of slopes with spatially varied soils within the framework of limit equilibrium method(LEM),which utilizes an adaptive K-means clustering approach.Then,an improved slope reliability analysis based on the RSSs and RSM considering soil spatial variability,in perspective of computation efficiency,is established.The detailed implementation procedure of the proposed method is well documented,and the ability of the method in identifying RSSs and estimating reliability is investigated via three slope examples.Results show that the proposed method can automatically identify the RSSs of slope with only one evaluation of the conventional deterministic slope stability model.The RSSs are invariant with the statistics of soil properties,which allows parametric studies that are often required in slope reliability analysis to be efficiently achieved with ease.It is also found that the proposed method provides comparable values of factor of safety(FS)and probability of failure(Pf)of slopes with those obtained from direct analysis and lite rature.
文摘Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters,making the slope reliability assessment closer to the engineering reality.However,multi-source information(including test data,monitored data,field observation and slope survival records)is rarely used in current probabilistic back-analysis.Conducting the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating multi-source information is a challenging task since thousands of random variables and high-dimensional likelihood function are usually involved.In this paper,a framework by integrating a modified Bayesian Updating with Subset simulation(mBUS)method with adaptive Conditional Sampling(aCS)algorithm is established for the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction.Within this framework,the high-dimensional probabilistic back-analysis problem can be easily tackled,and the multi-source information(e.g.monitored pressure heads and slope survival records)can be fully used in the back-analysis.A real Taoyuan landslide case in Taiwan,China is investigated to illustrate the effectiveness and performance of the established framework.The findings show that the posterior knowledge of soil parameters obtained from the established framework is in good agreement with the field observations.Furthermore,the updated knowledge of soil parameters can be utilized to reliably predict the occurrence probability of a landslide caused by the heavy rainfall event on September 12,2004 or forecast the potential landslides under future rainfalls in the Fuhsing District of Taoyuan City,Taiwan,China.
基金supported by the National Natural Science Foundation of China(Grant Nos.52179103 and 52222905)Jiangxi Provincial Natural Science Foundation(Grant No.20242BAB24001).
文摘Intense rainfall infiltration is one of the primary triggers for landslides.Developing a robust model for rainfall infiltration analysis is crucial for mitigating landslide disasters.Although the numerical solution of Richard's equation provides high computational accuracy,it often encounters convergence issues.In contrast,the Green-Ampt(GA)model,which is more computationally efficient,lacks accuracy in dealing with the non-uniform distribution of the initial volumetric water content(VWC)and the pore-water redistribution process.Therefore,this study proposes a novel model for analyzing the slope rainfall infiltration process based on the GA model.The proposed model discretizes both the geological layers of the slope and the rainfall event in spatial and temporal scales,respectively,improving accuracy by adjusting step sizes of discretization adaptively.The proposed model is applied to analyze the permeability,stability and reliability of heterogeneous infinite slopes considering uncertainties in multiple parameters.Comparative studies with the numerical solution of Richard's equation and other models demonstrate that the proposed model can provide high computational accuracy and superior analysis convergence in rainfall infiltration modeling.It also indicates that neglecting the pore-water redistribution underestimates the probability of slope failure,overestimates the factor of safety(FOS)of the slope,and inaccurately estimates the depth of the critical slip surface.Moreover,the uncertainties in shear strength parameters may overshadow the influence of initial VWC uncertainties on the slope reliability.This study provides a theoretical basis for the analysis of rainfall infiltration on heterogeneous slopes and the formulation of strategies for landslide disaster prevention.
