Determining the joint probability distribution of correlated non-normal geotechnical parameters based on incomplete statistical data is a challenging problem.This paper proposes a Gaussian copula-based method for mode...Determining the joint probability distribution of correlated non-normal geotechnical parameters based on incomplete statistical data is a challenging problem.This paper proposes a Gaussian copula-based method for modelling the joint probability distribution of bivariate uncertain data.First,the concepts of Pearson and Kendall correlation coefficients are presented,and the copula theory is briefly introduced.Thereafter,a Pearson method and a Kendall method are developed to determine the copula parameter underlying Gaussian copula.Second,these two methods are compared in computational efficiency,applicability,and capability of fitting data.Finally,four load-test datasets of load-displacement curves of piles are used to illustrate the proposed method.The results indicate that the proposed Gaussian copula-based method can not only characterize the correlation between geotechnical parameters,but also construct the joint probability distribution function of correlated non-normal geotechnical parameters in a more general way.It can serve as a general tool to construct the joint probability distribution of correlated geotechnical parameters based on incomplete data.The Gaussian copula using the Kendall method is superior to that using the Pearson method,which should be recommended for modelling and simulating the joint probability distribution of correlated geotechnical parameters.There exists a strong negative correlation between the two parameters underlying load-displacement curves.Neglecting such correlation will not capture the scatter in the measured load-displacement curves.These results substantially extend the application of the copula theory to multivariate simulation in geotechnical engineering.展开更多
Numerical methods are helpful for understanding the behaviors of geotechnical installations.However,the computational cost sometimes may become prohibitive when structural reliability analysis is performed,due to repe...Numerical methods are helpful for understanding the behaviors of geotechnical installations.However,the computational cost sometimes may become prohibitive when structural reliability analysis is performed,due to repetitive calls to the deterministic solver.In this paper,we show how accurate and efficient reliability analyses of geotechnical installations can be performed by directly coupling geotechnical software with a reliability solver.An earth dam is used as the study object under different operating conditions.The limit equilibrium method of Morgenstern-Price is used to calculate factors of safety and find the critical slip surface.The conmercial software packages Seep/W and Slope/W are coupled with StRAnD structural reliability software.Reliability indices of critical probabilistic surfaces are evaluated by the first-and second-order structural reliability methods(FORM and SORM),as well as by importance sampling Monte Carlo(ISMC)simulation.By means of sensitivity analysis,the effective friction angle(φ′)is found to be the most relevant uncertain geotechnical parameter for dam equilibrium.The correlations between different geotechnical properties are shown to be relevant in terms of equilibrium reliability indices.Finally,it is shown herein that a critical slip surface,identified in terms of the minimum factor of safety(FS),is not the critical surface in terms of the reliability index.展开更多
Geotechnical parameters derived from an intrusive cone penetration test(CPT)are used to asses mechanical properties to inform the design phase of infrastructure projects.However,local,in situ 1D measurements can fail ...Geotechnical parameters derived from an intrusive cone penetration test(CPT)are used to asses mechanical properties to inform the design phase of infrastructure projects.However,local,in situ 1D measurements can fail to capture 3D subsurface variations,which could mean less than optimal design decisions for foundation engineering.By coupling the localised measurements from CPTs with more global 3D measurements derived from geophysical methods,a higher fidelity 3D overview of the subsurface can be obtained.Machine Learning(ML)may offer an effective means to capture all types of geophysical information associated with CPT data at a site scale to build a 2D or 3D ground model.In this paper,we present an ML approach to build a 3D ground model of cone resistance and sleeve friction by combining several CPT measurements with Multichannel Analysis of Surface Waves(MASW)and Electrical Resistivity Tomography(ERT)data on a land site characterisation project in the United Arab Emirates(UAE).To avoid a potential overfitting problem inherent to the use of machine learning and a lack of data at certain locations,we explore the possibility of using a prior Geo-Statistical(GS)approach that attempts to constrain the overfitting process by“artificially”increasing the amount of input data.A sensitivity study is also performed on input features used to train the ML algorithm to better define the optimal combination of input features for the prediction.Our results showed that ERT data were not useful in capturing 3D variations of geotechnical properties compared to Vs due to the geographical location of the site(200 m east from the Oman Gulf)and the possible effect of saline water intrusion.Additionally,we demonstrate that the use of a prior GS phase could be a promising and interesting means to make the prediction of ground properties more robust,especially for this specific case study described in this paper.Looking ahead,better representation of the subsurface can lead to a number of benefits for stakeholders involved in developing assets.Better ground/geotechnical models mean better site calibration of design methods and fewer design assumptions for reliability-based design,creating an opportunity for value engineering in the form of lighter construction without compromising safety,shorter construction timelines,and reduced resource requirements.展开更多
基金supported by the National Basic Research Program of China ("973" Program) (Grant No. 2011CB013506)the National Natural Science Foundation of China (Grant Nos. 51028901 and 50839004)
文摘Determining the joint probability distribution of correlated non-normal geotechnical parameters based on incomplete statistical data is a challenging problem.This paper proposes a Gaussian copula-based method for modelling the joint probability distribution of bivariate uncertain data.First,the concepts of Pearson and Kendall correlation coefficients are presented,and the copula theory is briefly introduced.Thereafter,a Pearson method and a Kendall method are developed to determine the copula parameter underlying Gaussian copula.Second,these two methods are compared in computational efficiency,applicability,and capability of fitting data.Finally,four load-test datasets of load-displacement curves of piles are used to illustrate the proposed method.The results indicate that the proposed Gaussian copula-based method can not only characterize the correlation between geotechnical parameters,but also construct the joint probability distribution function of correlated non-normal geotechnical parameters in a more general way.It can serve as a general tool to construct the joint probability distribution of correlated geotechnical parameters based on incomplete data.The Gaussian copula using the Kendall method is superior to that using the Pearson method,which should be recommended for modelling and simulating the joint probability distribution of correlated geotechnical parameters.There exists a strong negative correlation between the two parameters underlying load-displacement curves.Neglecting such correlation will not capture the scatter in the measured load-displacement curves.These results substantially extend the application of the copula theory to multivariate simulation in geotechnical engineering.
基金financial support by the Coordination for the Improvement of Higher Education Personnel(CAPES)for research funding(Grant No.88882.145758/2017-01)the Brazilian National Council of Scientific and Technological Development(CNPq)。
文摘Numerical methods are helpful for understanding the behaviors of geotechnical installations.However,the computational cost sometimes may become prohibitive when structural reliability analysis is performed,due to repetitive calls to the deterministic solver.In this paper,we show how accurate and efficient reliability analyses of geotechnical installations can be performed by directly coupling geotechnical software with a reliability solver.An earth dam is used as the study object under different operating conditions.The limit equilibrium method of Morgenstern-Price is used to calculate factors of safety and find the critical slip surface.The conmercial software packages Seep/W and Slope/W are coupled with StRAnD structural reliability software.Reliability indices of critical probabilistic surfaces are evaluated by the first-and second-order structural reliability methods(FORM and SORM),as well as by importance sampling Monte Carlo(ISMC)simulation.By means of sensitivity analysis,the effective friction angle(φ′)is found to be the most relevant uncertain geotechnical parameter for dam equilibrium.The correlations between different geotechnical properties are shown to be relevant in terms of equilibrium reliability indices.Finally,it is shown herein that a critical slip surface,identified in terms of the minimum factor of safety(FS),is not the critical surface in terms of the reliability index.
文摘Geotechnical parameters derived from an intrusive cone penetration test(CPT)are used to asses mechanical properties to inform the design phase of infrastructure projects.However,local,in situ 1D measurements can fail to capture 3D subsurface variations,which could mean less than optimal design decisions for foundation engineering.By coupling the localised measurements from CPTs with more global 3D measurements derived from geophysical methods,a higher fidelity 3D overview of the subsurface can be obtained.Machine Learning(ML)may offer an effective means to capture all types of geophysical information associated with CPT data at a site scale to build a 2D or 3D ground model.In this paper,we present an ML approach to build a 3D ground model of cone resistance and sleeve friction by combining several CPT measurements with Multichannel Analysis of Surface Waves(MASW)and Electrical Resistivity Tomography(ERT)data on a land site characterisation project in the United Arab Emirates(UAE).To avoid a potential overfitting problem inherent to the use of machine learning and a lack of data at certain locations,we explore the possibility of using a prior Geo-Statistical(GS)approach that attempts to constrain the overfitting process by“artificially”increasing the amount of input data.A sensitivity study is also performed on input features used to train the ML algorithm to better define the optimal combination of input features for the prediction.Our results showed that ERT data were not useful in capturing 3D variations of geotechnical properties compared to Vs due to the geographical location of the site(200 m east from the Oman Gulf)and the possible effect of saline water intrusion.Additionally,we demonstrate that the use of a prior GS phase could be a promising and interesting means to make the prediction of ground properties more robust,especially for this specific case study described in this paper.Looking ahead,better representation of the subsurface can lead to a number of benefits for stakeholders involved in developing assets.Better ground/geotechnical models mean better site calibration of design methods and fewer design assumptions for reliability-based design,creating an opportunity for value engineering in the form of lighter construction without compromising safety,shorter construction timelines,and reduced resource requirements.
