科研之友
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摘要
Application research of neural networks to geotechnical engineering has become a hotspot nowadays. General model may not reach the predicting precision in practical application due to different characteristics in different fields. In allusion to this, an elasto-plastic constitutive model based on clustering radial basis function neural network(BC-RBFNN) was proposed for moderate sandy clay according to its properties. Firstly, knowledge base was established on triaxial compression testing data; then the model was trained, learned and emulated using knowledge base; finally, predicting results of the BC-RBFNN model were compared and analyzed with those of other intelligent model. The results show that the BC-RBFNN model can alter the training and learning velocity and improve the predicting precision, which provides possibility for engineering practice on demanding high precision.
