In the method of response surface Monte-Carlo based on artificial neural network

the uniform design

a kind of design of computer experiments

is introduced to arrange training samplings set of RBF neural network that combines the advantages of uniform design and RBF neural network organically. On the one hand

it may decrease the number of training samples as fully as possible

and on the other

it fills training samples into the parameter space uniformly

and enhances the prediction accuracy of RBF neural network which has strongly local approximate capacity. In the probability analysis of retaining wall stability against sliding

for example

the results of the proposed method are very consistent with those of the direct Monte-Carlo simulations. And in addition this probability analysis shows that friction coefficient of the wall base f

the inner friction angle f

unit weight γ of filled soils

and the friction angle of the soils against wall back δ have influences on the retaining wall stability against sliding from high to low in turn.