An elasto-plastic constitutive model of moderate sandy clay based on BC-RBFNN
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.
elasto-plastic constitutive model artificial neural network BC-RBFNN (based on clustering radial basis function neural network) moderate sandy clay
PENG Xiang-hua(彭相华) WANG Zhi-chao(王智超) LUO Tao(罗涛) YU Min(余敏) LUO Ying-she(罗迎社)
Swan College of Central South University of Forestry and Technology, Changsha 410004, China College of Civil Engineering and Mechanics, Xiangtan University, Xiangtan 411105, China Institute of Rheological Mechanics and Material Engineering, Central South University of Forestry an
国际会议
长沙
英文
47-50
2008-09-28（万方平台首次上网日期，不代表论文的发表时间）