Abstract：The neural network structure adopted is that, the node number of input layer of 4, the node number of hidden layer of 29, and the node number of output layer of 1 in the neural network structure are adopted. During the training of RBF neural network, the clustering method of K-means is used. The training speed of 0.15, the seed number of weighting of 2, the sigma parameter of 0.1, the weighting of 0.2, the maximum of iteration time of 16 000, and the average value of controlled of error 0.01 are adopted. It is found that the relative error of fitting value of compressibility coefficient compared with the observed value for 30 groups of independent variables in training RBF neural network model is between -2.540 0％～2.600 0％, and there are 25 groups of data whose relative error is 0, the absolute value of the relative error is 0.185 14％: In general,the error(<25％) is feasible in geotechnical engineering, so the prediction model of coefficient of compressibility with RBF neural network is feasible.
2011, Vol. 32 
