Abstract: Application of theory on strength reduction FEM was analyzed based on improved BP neural networks. BP neural networks can simulate human brain and deal with complicated nonlinear relationship in different slope parameters under the condition of unknown relation between data distribution form and variables. Just taking the advantage the authors forecast the accuracy of every strength criterion in evaluating the slop stability. Take known data to the system to train the networks. According to different strength criterions, the different networks were trained. After that, the networks with new data were checked up. In the same way, the authors also analyze different slope damages considering character of dilatancy. The results show that using DP4 and DP5 criterions may obtain a favorable effect;meanwhile, the error is the largest in DP1 criterion. The conclusions would offer useful information for further application of strength reduction FEM.

Key words: improved BP neural networks, strength reduction FEM, strength criterion, character of dilatancy