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
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
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
Accurate prediction of fatigue life under multiaxial loading conditions remains challenging due to complex stress–strain interactions.In this study,we integrate machine-learning(ML)regression with variance-based sens...Accurate prediction of fatigue life under multiaxial loading conditions remains challenging due to complex stress–strain interactions.In this study,we integrate machine-learning(ML)regression with variance-based sensitivity analysis(SA)to predict multiaxial fatigue life in CuZn37 brass and to identify the dominant mechanical factors influencing fatigue damage.Several surrogate models were evaluated,with the Gaussian Process model achieving the highest accuracy(R^(2)=0.991)while maintaining robust generalization across loading paths.Gradient Boosting,Random Forest,and Penalized Spline Regression models also demonstrated strong predictive capabilities.Importantly,the SA explicitly accounted for statistical dependencies among input parameters,revealing that normal strain–stress interactions account for over 40%of the total variance in fatigue life.In contrast,shear-related parameters exhibited secondary,compensatory effects.These results highlight the importance of capturing parameter dependencies in fatigue modeling and demonstrate that ML-based surrogates can help provide both high-fidelity predictions and physical insights under complex multiaxial loading conditions.展开更多
Predicting fatigue life with precision requires more than isolated evaluations of mechanical properties;it requires an integrated approach that captures the interdependencies between various parameters,including elast...Predicting fatigue life with precision requires more than isolated evaluations of mechanical properties;it requires an integrated approach that captures the interdependencies between various parameters,including elastic modulus,tensile strength,yield strength,and strain-hardening exponent.Neglecting these correlations in sensitivity analyses can compromise prediction accuracy and physical interpretability.In this study,we introduce a dependency-aware sensitivity analysis framework,assisted by machine learning-based surrogate models,to evaluate the contributions of these mechanical properties to fatigue life variability.Tensile strength emerged as the most influential parameter,with significant second-order interactions,particularly between tensile and yield strength,highlighting the central role of coupled effects in fatigue mechanisms.By addressing these interdependencies,the proposed approach improves the reliability of fatigue life predictions and offers a solid foundation for the optimization of metallic components subjected to cyclic stresses.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.
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Accurate prediction of fatigue life under multiaxial loading conditions remains challenging due to complex stress–strain interactions.In this study,we integrate machine-learning(ML)regression with variance-based sensitivity analysis(SA)to predict multiaxial fatigue life in CuZn37 brass and to identify the dominant mechanical factors influencing fatigue damage.Several surrogate models were evaluated,with the Gaussian Process model achieving the highest accuracy(R^(2)=0.991)while maintaining robust generalization across loading paths.Gradient Boosting,Random Forest,and Penalized Spline Regression models also demonstrated strong predictive capabilities.Importantly,the SA explicitly accounted for statistical dependencies among input parameters,revealing that normal strain–stress interactions account for over 40%of the total variance in fatigue life.In contrast,shear-related parameters exhibited secondary,compensatory effects.These results highlight the importance of capturing parameter dependencies in fatigue modeling and demonstrate that ML-based surrogates can help provide both high-fidelity predictions and physical insights under complex multiaxial loading conditions.
基金We acknowledge Ho Chi Minh City University of Technology(HCMUT),VNU-HCM for supporting this study.
文摘Predicting fatigue life with precision requires more than isolated evaluations of mechanical properties;it requires an integrated approach that captures the interdependencies between various parameters,including elastic modulus,tensile strength,yield strength,and strain-hardening exponent.Neglecting these correlations in sensitivity analyses can compromise prediction accuracy and physical interpretability.In this study,we introduce a dependency-aware sensitivity analysis framework,assisted by machine learning-based surrogate models,to evaluate the contributions of these mechanical properties to fatigue life variability.Tensile strength emerged as the most influential parameter,with significant second-order interactions,particularly between tensile and yield strength,highlighting the central role of coupled effects in fatigue mechanisms.By addressing these interdependencies,the proposed approach improves the reliability of fatigue life predictions and offers a solid foundation for the optimization of metallic components subjected to cyclic stresses.
