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
In the present paper, we hare mtroduced the random materials. loads. geometricalshapes, force and displacement boundary condition directly. into the functionalvariational formula, by. use of a small parameter perturb...In the present paper, we hare mtroduced the random materials. loads. geometricalshapes, force and displacement boundary condition directly. into the functionalvariational formula, by. use of a small parameter perturbation method, a unifiedrandom variational principle in finite defomation of elastieity and nonlinear randomfinite element method are esiablished, and used.for reliability, analysis of structures.Numerical examples showed that the methods have the advontages of simple andconvenient program implementation and are effective for the probabilistic problems inmechanics.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In the present paper, we hare mtroduced the random materials. loads. geometricalshapes, force and displacement boundary condition directly. into the functionalvariational formula, by. use of a small parameter perturbation method, a unifiedrandom variational principle in finite defomation of elastieity and nonlinear randomfinite element method are esiablished, and used.for reliability, analysis of structures.Numerical examples showed that the methods have the advontages of simple andconvenient program implementation and are effective for the probabilistic problems inmechanics.
