Abstract:

When the soil slope stability is evaluated by neural network model,varieties of training samples always make the evaluation result unsatisfactory.In order to solve the problem,we introduce the C4.5 decision tree algorithm,build an evaluation model of soil slope stability based on decision tree classifier,and prune the tree structure established.Furthermore,we adopt measured data in several soil slope projects and select classification attributes according to gain ratio of information in this model.Compared with BP neural network and LVQ(Learning Vector Quantization) neural network,the result shows that decision tree algorithm has the highest accuracy for classification,up to 90%,and the computation time of this model is 2.24 seconds.Finally,it is feasible to introduce decision tree algorithm for stability evaluation in soil slope.

Key words: soil slope, stability prediction, decision tree, BP neural network, LVQ neural network