The prediction of frost heave in artificial freezing soil based on fractal and neural network

Based on granularity distribution of soil having fractal character, the fractal dimension of soil is studied by theory analysis and calculation. The structure character of soil is quantized by using fractal dimension, which build up a foundation for neural network considering soil structure in the process of prediction of frost heave. Topology structure of BP neural network is built, and L-M arithmetic is used to find a solution. It has favorable coherence and curvature tolerance between prediction result and test result. This method remedies the defect of theory model and numerical analysis, which is unable to consider interior structure of soil. The research shows it is feasible to use this method into forecasting frost heave. Fractal dimension reflects the different of interior structure in soil. On the process of prediction by using neural network, prediction result considered fractal dimension fit the actual condition most. This method can make up the defect of ignoring soil structure in theory model and numerical analysis to some extent. Because of complexity of moisture immigration, prediction model aims at closed freezing system without considering the influence of outside moisture. Because the quantity of collected data far less than test data, it is primary cause of error that training of network is not sufficient.