Forecasting Cohesionless Soil Highway Slope Displacement Using Modular Neural Network

The highway slope failures are triggered by the rainfall, namely, to create the disaster. However, forecasting the failure of highway slop is difficult because of nonlinear time dependency and seasonal effects, which affect the slope displacements. Starting from the artificial neural networks (ANNs) since the mid-1990s, an effective means is suggested to judge the stability of slope by forecasting the slope displacement in the future based on the monitoring data. In order to solve the problem of forecasting the highway slope displacement, a displacement time series forecasting model of cohesionless soil highway slope is given firstly, and then modular neural network (MNN) is used to train it. With the randomness of rainfall information, the membership function based on distance measurement is constructed; after that, a fuzzy discrimination method to sample data is adopted to realize online subnets selection to improve the self-adapting ability of artificial neural networks (ANNs). The experiment on the sample data of Beijing city’s highway slope demonstrates that this model is superior to others in accuracy and adaptability