The static performance of arch dams during construction and reservoir impoundment is assessed taking into account the effects of uncertainties presented in the model properties as well as the loading conditions.Dez ar...The static performance of arch dams during construction and reservoir impoundment is assessed taking into account the effects of uncertainties presented in the model properties as well as the loading conditions.Dez arch dam is chosen as the case study;it is modeled along with its rock foundation using the finite element method considering the stage construction.Since previous studies concentrated on simplified models and approaches,comprehensive study of the arch dam model along with efficient and state-of-the-art uncertainty methods are incorporated in this investigation.The reliability method is performed to assess the safety level and the sensitivity analyses for identifying critical input factors and their interaction effects on the response of the dam.Global sensitivity analysis based on improved Latin hypercube sampling is employed in this study to indicate the influence of each random variable and their interaction on variance of the responses.Four levels of model advancement are considered for the dam foundation system:1)Monolithic dam without any joint founded on the homogeneous rock foundation,2)monolithic dam founded on the inhomogeneous foundation including soft rock layers,3)jointed dam including the peripheral and contraction joints founded on the homogeneous foundation,and 4)jointed dam founded on the inhomogeneous foundation.For each model,proper performance indices are defined through limit-state functions.In this manner,the effects of input parameters in each performance level of the dam are investigated.The outcome of this study is defining the importance of input factors in each stage and model based on the variance of the dam response.Moreover,the results of sampling are computed in order to assess the safety level of the dam in miscellaneous loading and modeling conditions.展开更多
A new and convenient method is presented to calculate the total sensitivity indices defined by variance-based sensitivity analysis. By decomposing the output variance using error propagation equations, this method can...A new and convenient method is presented to calculate the total sensitivity indices defined by variance-based sensitivity analysis. By decomposing the output variance using error propagation equations, this method can transform the ＂double-loop＂ sampling procedure into ＂single-loop＂ one and obviously reduce the computation cost of analysis. In contrast with Sobors and Fourier amplitude sensitivity test （FAST） method, which is limited in non-correlated variables, the new approach is suitable for correlated input variables. An application in semiconductor assembling and test manufacturing （ATM） factory indicates that this approach has a good performance in additive model and simple non-additive model.展开更多
The variable importance measure(VIM)can be implemented to rank or select important variables,which can effectively reduce the variable dimension and shorten the computational time.Random forest(RF)is an ensemble learn...The variable importance measure(VIM)can be implemented to rank or select important variables,which can effectively reduce the variable dimension and shorten the computational time.Random forest(RF)is an ensemble learning method by constructing multiple decision trees.In order to improve the prediction accuracy of random forest,advanced random forest is presented by using Kriging models as the models of leaf nodes in all the decision trees.Referring to the Mean Decrease Accuracy(MDA)index based on Out-of-Bag(OOB)data,the single variable,group variables and correlated variables importance measures are proposed to establish a complete VIM system on the basis of advanced random forest.The link of MDA and variance-based sensitivity total index is explored,and then the corresponding relationship of proposed VIM indices and variance-based global sensitivity indices are constructed,which gives a novel way to solve variance-based global sensitivity.Finally,several numerical and engineering examples are given to verify the effectiveness of proposed VIM system and the validity of the established relationship.展开更多
文摘The static performance of arch dams during construction and reservoir impoundment is assessed taking into account the effects of uncertainties presented in the model properties as well as the loading conditions.Dez arch dam is chosen as the case study;it is modeled along with its rock foundation using the finite element method considering the stage construction.Since previous studies concentrated on simplified models and approaches,comprehensive study of the arch dam model along with efficient and state-of-the-art uncertainty methods are incorporated in this investigation.The reliability method is performed to assess the safety level and the sensitivity analyses for identifying critical input factors and their interaction effects on the response of the dam.Global sensitivity analysis based on improved Latin hypercube sampling is employed in this study to indicate the influence of each random variable and their interaction on variance of the responses.Four levels of model advancement are considered for the dam foundation system:1)Monolithic dam without any joint founded on the homogeneous rock foundation,2)monolithic dam founded on the inhomogeneous foundation including soft rock layers,3)jointed dam including the peripheral and contraction joints founded on the homogeneous foundation,and 4)jointed dam founded on the inhomogeneous foundation.For each model,proper performance indices are defined through limit-state functions.In this manner,the effects of input parameters in each performance level of the dam are investigated.The outcome of this study is defining the importance of input factors in each stage and model based on the variance of the dam response.Moreover,the results of sampling are computed in order to assess the safety level of the dam in miscellaneous loading and modeling conditions.
文摘A new and convenient method is presented to calculate the total sensitivity indices defined by variance-based sensitivity analysis. By decomposing the output variance using error propagation equations, this method can transform the ＂double-loop＂ sampling procedure into ＂single-loop＂ one and obviously reduce the computation cost of analysis. In contrast with Sobors and Fourier amplitude sensitivity test （FAST） method, which is limited in non-correlated variables, the new approach is suitable for correlated input variables. An application in semiconductor assembling and test manufacturing （ATM） factory indicates that this approach has a good performance in additive model and simple non-additive model.
文摘The variable importance measure(VIM)can be implemented to rank or select important variables,which can effectively reduce the variable dimension and shorten the computational time.Random forest(RF)is an ensemble learning method by constructing multiple decision trees.In order to improve the prediction accuracy of random forest,advanced random forest is presented by using Kriging models as the models of leaf nodes in all the decision trees.Referring to the Mean Decrease Accuracy(MDA)index based on Out-of-Bag(OOB)data,the single variable,group variables and correlated variables importance measures are proposed to establish a complete VIM system on the basis of advanced random forest.The link of MDA and variance-based sensitivity total index is explored,and then the corresponding relationship of proposed VIM indices and variance-based global sensitivity indices are constructed,which gives a novel way to solve variance-based global sensitivity.Finally,several numerical and engineering examples are given to verify the effectiveness of proposed VIM system and the validity of the established relationship.
