In order to analyze the stability of slopes

by using coordinated particle swarm optimization(CPSO) and BP neural network

a CPSO-BP model is built to predict slope stability. BP neural network can describe the complex nonlinear relationship between the slope stability and its influential factors; six main influential factors

i.e. internal friction angle

slope angle

unit weight of rock

slope height

cohesion and pore pressure ratio

as a network input

the network output is the slope stability factor. BP neural network is so easy to fall into local optimum that take advantage of coordinated particle swarm optimization algorithm the ability of global to determine BP neural network connection weights and thresholds

let the advantages of BP neural network can fully play. Frinally we will approach to the aim that improving the accuracy of model predictions. Case study shows that the CPSO-BP model has better prediction accuracy and it is feasible to predict the slope stability.