This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube s...This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.展开更多
Materials data deep-excavation is very important in materials genome exploration.In order to carry out materials data deep-excavation in hot die steels and obtain the relationships among alloying elements,heat treatme...Materials data deep-excavation is very important in materials genome exploration.In order to carry out materials data deep-excavation in hot die steels and obtain the relationships among alloying elements,heat treatment parameters and materials properties,a 11×12×12×4 back-propagation（BP）artificial neural network（ANN）was set up.Alloying element contents,quenching and tempering temperatures were selected as input;hardness,tensile and yield strength were set as output parameters.The ANN shows a high fitting precision.The effects of alloying elements and heat treatment parameters on the properties of hot die steel were studied using this model.The results indicate that high temperature hardness increases with increasing alloying element content of C,Si,Mo,W,Ni,V and Cr to a maximum value and decreases with further increase in alloying element content.The ANN also predicts that the high temperature hardness will decrease with increasing quenching temperature,and possess an optimal value with increasing tempering temperature.This model provides a new tool for novel hot die steel design.展开更多
Computational models provide additional tools for studying the brain,however,many techniques are currently disconnected from each other.There is a need for new computational approaches that span the range of physics o...Computational models provide additional tools for studying the brain,however,many techniques are currently disconnected from each other.There is a need for new computational approaches that span the range of physics operating in the brain.In this review paper,we offer some new perspectives on how the embedded element method can fill this gap and has the potential to connect a myriad of modeling genre.The embedded element method is a mesh superposition technique used within finite element analysis.This method allows for the incorporation of axonal fiber tracts to be explicitly represented.Here,we explore the use of the approach beyond its original goal of predicting axonal strain in brain injury.We explore the potential application of the embedded element method in areas of electrophysiology,neurodegeneration,neuropharmacology and mechanobiology.We conclude that this method has the potential to provide us with an integrated computational framework that can assist in developing improved diagnostic tools and regeneration technologies.展开更多
基金financially supported by the National Natural Science Foundation of China(Grant No.51278217)
文摘This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.
文摘Materials data deep-excavation is very important in materials genome exploration.In order to carry out materials data deep-excavation in hot die steels and obtain the relationships among alloying elements,heat treatment parameters and materials properties,a 11×12×12×4 back-propagation（BP）artificial neural network（ANN）was set up.Alloying element contents,quenching and tempering temperatures were selected as input;hardness,tensile and yield strength were set as output parameters.The ANN shows a high fitting precision.The effects of alloying elements and heat treatment parameters on the properties of hot die steel were studied using this model.The results indicate that high temperature hardness increases with increasing alloying element content of C,Si,Mo,W,Ni,V and Cr to a maximum value and decreases with further increase in alloying element content.The ANN also predicts that the high temperature hardness will decrease with increasing quenching temperature,and possess an optimal value with increasing tempering temperature.This model provides a new tool for novel hot die steel design.
基金support provided by Computational Fluid Dynamics Research Corporation(CFDRC)under a sub-contract funded by the Department of Defense,Department of Health Program through contract W81XWH-14-C-0045
文摘Computational models provide additional tools for studying the brain,however,many techniques are currently disconnected from each other.There is a need for new computational approaches that span the range of physics operating in the brain.In this review paper,we offer some new perspectives on how the embedded element method can fill this gap and has the potential to connect a myriad of modeling genre.The embedded element method is a mesh superposition technique used within finite element analysis.This method allows for the incorporation of axonal fiber tracts to be explicitly represented.Here,we explore the use of the approach beyond its original goal of predicting axonal strain in brain injury.We explore the potential application of the embedded element method in areas of electrophysiology,neurodegeneration,neuropharmacology and mechanobiology.We conclude that this method has the potential to provide us with an integrated computational framework that can assist in developing improved diagnostic tools and regeneration technologies.
