Spatial variability of soil properties imposes a challenge for practical analysis and design in geotechnical engineering.The latter is particularly true for slope stability assessment,where the effects of uncertainty ...Spatial variability of soil properties imposes a challenge for practical analysis and design in geotechnical engineering.The latter is particularly true for slope stability assessment,where the effects of uncertainty are synthesized in the so-called probability of failure.This probability quantifies the reliability of a slope and its numerical calculation is usually quite involved from a numerical viewpoint.In view of this issue,this paper proposes an approach for failure probability assessment based on Latinized partially stratified sampling and maximum entropy distribution with fractional moments.The spatial variability of geotechnical properties is represented by means of random fields and the Karhunen-Loève expansion.Then,failure probabilities are estimated employing maximum entropy distribution with fractional moments.The application of the proposed approach is examined with two examples:a case study of an undrained slope and a case study of a slope with cross-correlated random fields of strength parameters under a drained slope.The results show that the proposed approach has excellent accuracy and high efficiency,and it can be applied straightforwardly to similar geotechnical engineering problems.展开更多
In structural reliability analysis,simulation methods are widely used.The statistical characteristics of failure probability estimate of these methods have been well investigated.In this study,the sensitivities of the...In structural reliability analysis,simulation methods are widely used.The statistical characteristics of failure probability estimate of these methods have been well investigated.In this study,the sensitivities of the failure probability estimate and its statistical characteristics with regard to sample,called‘contribution indexes’,are proposed to measure the contribution of sample.The contribution indexes in four widely simulation methods,i.e.,Monte Carlo simulation(MCS),importance sampling(IS),line sampling(LS)and subset simulation(SS)are derived and analyzed.The proposed contribution indexes of sample can provide valuable information understanding the methods deeply,and enlighten potential improvement of methods.It is found that the main differences between these investigated methods lie in the contribution indexes of the safety samples,which are the main factors to the efficiency of the methods.Moreover,numerical examples are used to validate these findings.展开更多
The shear behavior of large-scale weak intercalation shear zones(WISZs)often governs the stability of foundations,rock slopes,and underground structures.However,due to their wide distribution,undulating morphology,com...The shear behavior of large-scale weak intercalation shear zones(WISZs)often governs the stability of foundations,rock slopes,and underground structures.However,due to their wide distribution,undulating morphology,complex fabrics,and varying degrees of contact states,characterizing the shear behavior of natural and complex large-scale WISZs precisely is challenging.This study proposes an analytical method to address this issue,based on geological fieldwork and relevant experimental results.The analytical method utilizes the random field theory and Kriging interpolation technique to simplify the spatial uncertainties of the structural and fabric features for WISZs into the spatial correlation and variability of their mechanical parameters.The Kriging conditional random field of the friction angle of WISZs is embedded in the discrete element software 3DEC,enabling activation analysis of WISZ C2 in the underground caverns of the Baihetan hydropower station.The results indicate that the activation scope of WISZ C2 induced by the excavation of underground caverns is approximately 0.5e1 times the main powerhouse span,showing local activation.Furthermore,the overall safety factor of WISZ C2 follows a normal distribution with an average value of 3.697.展开更多
This study proposes a framework to evaluate the performance of borehole arrangements for the design of rectangular shallow foundation systems under spatially variable soil conditions. Performance measures are introduc...This study proposes a framework to evaluate the performance of borehole arrangements for the design of rectangular shallow foundation systems under spatially variable soil conditions. Performance measures are introduced to quantify, for a fixed foundation layout and given soil sounding locations, the variability level of the foundation system bearing capacities in terms of their mean values and standard deviations. To estimate these measures, the recently proposed random failure mechanism method (RFMM) has been adopted and extended to consider any arrangement of rectangular foundations and boreholes. Hence, three-dimensional bearing capacity estimation under spatially variable soil can be efficiently performed. Several numerical examples are presented to illustrate the applicability of the proposed framework, including diverse foundation arrangements and different soil correlation structures. Overall, the proposed framework represents a potentially useful tool to support the design of geotechnical site investigation programs, especially in situations where very limited prior knowledge about the soil properties is available.展开更多
The application of reliability analysis and reliability sensitivity analysis methods to complicated structures faces two main challenges:small failure probability(typical less than 10-5)and time-demanding mechanical m...The application of reliability analysis and reliability sensitivity analysis methods to complicated structures faces two main challenges:small failure probability(typical less than 10-5)and time-demanding mechanical models.This paper proposes an improved active learning surrogate model method,which combines the advantages of the classical Active Kriging–Monte Carlo Simulation(AK-MCS)procedure and the Adaptive Linked Importance Sampling(ALIS)procedure.The proposed procedure can,on the one hand,adaptively produce a series of intermediate sampling density approaching the quasi-optimal Importance Sampling(IS)density,on the other hand,adaptively generate a set of intermediate surrogate models approaching the true failure surface of the rare failure event.Then,the small failure probability and the corresponding reliability sensitivity indices are efficiently estimated by their IS estimators based on the quasi-optimal IS density and the surrogate models.Compared with the classical AK-MCS and Active Kriging–Importance Sampling(AK-IS)procedure,the proposed method neither need to build very large sample pool even when the failure probability is extremely small,nor need to estimate the Most Probable Points(MPPs),thus it is computationally more efficient and more applicable especially for problems with multiple MPPs.The effectiveness and engineering applicability of the proposed method are demonstrated by one numerical test example and two engineering applications.展开更多
A probabilistic model is proposed that uses observation data to estimate failure probabilities during excavations.The model integrates a Bayesian network and distanced-based Bayesian model updating.In the network,the ...A probabilistic model is proposed that uses observation data to estimate failure probabilities during excavations.The model integrates a Bayesian network and distanced-based Bayesian model updating.In the network,the movement of a retaining wall is selected as the indicator of failure,and the observed ground surface settlement is used to update the soil parameters.The responses of wall deflection and ground surface settlement are accurately predicted using finite element analysis.An artificial neural network is employed to construct the response surface relationship using the aforementioned input factors.The proposed model effectively estimates the uncertainty of influential factors.A case study of a braced excavation is presented to demonstrate the feasibility of the proposed approach.The update results facilitate accurate estimates according to the target value,from which the corresponding probabilities of failure are obtained.The proposed model enables failure probabilities to be determined with real-time result updating.展开更多
基金funding support from the China Scholarship Council(CSC).
文摘Spatial variability of soil properties imposes a challenge for practical analysis and design in geotechnical engineering.The latter is particularly true for slope stability assessment,where the effects of uncertainty are synthesized in the so-called probability of failure.This probability quantifies the reliability of a slope and its numerical calculation is usually quite involved from a numerical viewpoint.In view of this issue,this paper proposes an approach for failure probability assessment based on Latinized partially stratified sampling and maximum entropy distribution with fractional moments.The spatial variability of geotechnical properties is represented by means of random fields and the Karhunen-Loève expansion.Then,failure probabilities are estimated employing maximum entropy distribution with fractional moments.The application of the proposed approach is examined with two examples:a case study of an undrained slope and a case study of a slope with cross-correlated random fields of strength parameters under a drained slope.The results show that the proposed approach has excellent accuracy and high efficiency,and it can be applied straightforwardly to similar geotechnical engineering problems.
基金NSAF(Grant No.U1530122)the Aeronautical Science Foundation of China(Grant No.ASFC-20170968002)the Fundamental Research Funds for the Central Universities of China(XMU,20720180072).
文摘In structural reliability analysis,simulation methods are widely used.The statistical characteristics of failure probability estimate of these methods have been well investigated.In this study,the sensitivities of the failure probability estimate and its statistical characteristics with regard to sample,called‘contribution indexes’,are proposed to measure the contribution of sample.The contribution indexes in four widely simulation methods,i.e.,Monte Carlo simulation(MCS),importance sampling(IS),line sampling(LS)and subset simulation(SS)are derived and analyzed.The proposed contribution indexes of sample can provide valuable information understanding the methods deeply,and enlighten potential improvement of methods.It is found that the main differences between these investigated methods lie in the contribution indexes of the safety samples,which are the main factors to the efficiency of the methods.Moreover,numerical examples are used to validate these findings.
基金support from the Key Projects of the Yalong River Joint Fund of the National Natural Science Foundation of China(Grant No.U1865203)the Innovation Team of Changjiang River Scientific Research Institute(Grant Nos.CKSF2021715/YT and CKSF2023305/YT)。
文摘The shear behavior of large-scale weak intercalation shear zones(WISZs)often governs the stability of foundations,rock slopes,and underground structures.However,due to their wide distribution,undulating morphology,complex fabrics,and varying degrees of contact states,characterizing the shear behavior of natural and complex large-scale WISZs precisely is challenging.This study proposes an analytical method to address this issue,based on geological fieldwork and relevant experimental results.The analytical method utilizes the random field theory and Kriging interpolation technique to simplify the spatial uncertainties of the structural and fabric features for WISZs into the spatial correlation and variability of their mechanical parameters.The Kriging conditional random field of the friction angle of WISZs is embedded in the discrete element software 3DEC,enabling activation analysis of WISZ C2 in the underground caverns of the Baihetan hydropower station.The results indicate that the activation scope of WISZ C2 induced by the excavation of underground caverns is approximately 0.5e1 times the main powerhouse span,showing local activation.Furthermore,the overall safety factor of WISZ C2 follows a normal distribution with an average value of 3.697.
基金support of the Polish National Agency for Academic Exchange under the Bekker NAWA Programme(Grant No.BPN/BEK/2021/1/00068)which founded the postdoctoral fellowship at the Institute of Risk and Reliability at Leibniz University Hannover.The first author would also like to thank to Prof.Wengang Zhang and Chongzhi Wu(School of Civil Engineering,Chongqing University)for inspiring discussions initi-ated by High-end Foreign Expert Introduction program(Grant No.DL2021165001L)by the Ministry of Science and Technology(MOST),ChinaThe second author would like to thank the support from ANID(National Agency for Research and Development,Chile)and DAAD(German Academic Exchange Service,Germany)under CONICYT-PFCHA/Doctorado Acuerdo Bilateral DAAD Becas Chile/2018-62180007.The third author gratefully acknowledges the support by ANID under its program FONDECYT(Grant No.1200087).
文摘This study proposes a framework to evaluate the performance of borehole arrangements for the design of rectangular shallow foundation systems under spatially variable soil conditions. Performance measures are introduced to quantify, for a fixed foundation layout and given soil sounding locations, the variability level of the foundation system bearing capacities in terms of their mean values and standard deviations. To estimate these measures, the recently proposed random failure mechanism method (RFMM) has been adopted and extended to consider any arrangement of rectangular foundations and boreholes. Hence, three-dimensional bearing capacity estimation under spatially variable soil can be efficiently performed. Several numerical examples are presented to illustrate the applicability of the proposed framework, including diverse foundation arrangements and different soil correlation structures. Overall, the proposed framework represents a potentially useful tool to support the design of geotechnical site investigation programs, especially in situations where very limited prior knowledge about the soil properties is available.
基金supported by National Natural Science Foundation of China(Nos.51905430,51608446)the Fundamental Research Fund for Central Universities(No.3102018zy011)+1 种基金the supports of Alexander von Humboldt Foundation of Germanythe Top International University Visiting Program for Outstanding Young scholars of Northwestern Polytechnical University。
文摘The application of reliability analysis and reliability sensitivity analysis methods to complicated structures faces two main challenges:small failure probability(typical less than 10-5)and time-demanding mechanical models.This paper proposes an improved active learning surrogate model method,which combines the advantages of the classical Active Kriging–Monte Carlo Simulation(AK-MCS)procedure and the Adaptive Linked Importance Sampling(ALIS)procedure.The proposed procedure can,on the one hand,adaptively produce a series of intermediate sampling density approaching the quasi-optimal Importance Sampling(IS)density,on the other hand,adaptively generate a set of intermediate surrogate models approaching the true failure surface of the rare failure event.Then,the small failure probability and the corresponding reliability sensitivity indices are efficiently estimated by their IS estimators based on the quasi-optimal IS density and the surrogate models.Compared with the classical AK-MCS and Active Kriging–Importance Sampling(AK-IS)procedure,the proposed method neither need to build very large sample pool even when the failure probability is extremely small,nor need to estimate the Most Probable Points(MPPs),thus it is computationally more efficient and more applicable especially for problems with multiple MPPs.The effectiveness and engineering applicability of the proposed method are demonstrated by one numerical test example and two engineering applications.
基金supported by the National Natural Science Foundation of China(Grant No.11572233)the National Defense Pre-Research Foundation of China(Grant No.61400020106)as well as the Fundamental Research Funds for the Central Universities.
基金This work is supported by the Chinese Scholarship Council.
文摘A probabilistic model is proposed that uses observation data to estimate failure probabilities during excavations.The model integrates a Bayesian network and distanced-based Bayesian model updating.In the network,the movement of a retaining wall is selected as the indicator of failure,and the observed ground surface settlement is used to update the soil parameters.The responses of wall deflection and ground surface settlement are accurately predicted using finite element analysis.An artificial neural network is employed to construct the response surface relationship using the aforementioned input factors.The proposed model effectively estimates the uncertainty of influential factors.A case study of a braced excavation is presented to demonstrate the feasibility of the proposed approach.The update results facilitate accurate estimates according to the target value,from which the corresponding probabilities of failure are obtained.The proposed model enables failure probabilities to be determined with real-time result updating.