APPLICATION OF BP NEURAL NETWORK BASED ON FACTOR ANALYSIS TO PREDICTION OF ROCK MASS DEFORMATION MODULUS

Rock mass deformation modulus is the important parameter in the study of rock mass deformation characteristics. It is also of great importance to the stability analysis and optimal design of engineering rock mass. A method for predicting the rock mass deformation modulus is presented in this paper. It uses the BP neural network based on factor analysis. It is applied to the case of a hydropower station in Tibet. On the basis of laboratory tests and in-situ tests, a database of 48 data sets including density, water absorption, vertical-pace, uniaxial compressive strength, rock mass deformation modulus and poisson's ratio factors is established. Three public factors are obtained using the factor analysis method to analyze the six factors. The three public factors act as the input parameters and are used to make BP neural network predictions. Some important conclusions are drawn: The factor analysis can eliminate the defect that the excessive inputting data slows down the processing speed in BP neural network. The prediction accuracy can be improved using this method. This research idea is not only an useful attempt to predict rock mass deformation modulus, but also a great reference value to solve similar geotechnical engineering problems.