Combined forecasting model based on wavelet neural network for prediction of high fill subgrade settlement
Liang Li · Journal of Changsha University of Science & Technology · 2010
Using the nature of good time-frequency localization of wavelet transform and the self-learning function of neural networks,a combined freeway high filling subgrade settlement prediction model with S-Growth Model is established,based on wavelet neural network.The model is based on Real-time monitoring data,avoiding the calculation of various human factors.Through Ru-Chen freeway K59+375～K59+445 embankment settlement on-site monitoring data of high learning,prediction and testing,and with S-Growth Model and BP neural network prediction,results show that combined model of wavelet neural network prediction are accurate and consistent with the actual situation.