DIAPHRAGM WALL’S DEFORMATION FORECASTING BASED ON BP-RBF NEURAL NETWORKS

A artificial neural network is adopted to forecast diaphragm wall’s deformations. Five parameters, the soil’s cohesion C, the soil’s internal friction angle , the wall’s height H, the excavation depth H1 and the survey point’s depth h, governing diaphragm wall’s deformation are abstracted and taken as inputs of the artificial neural network model. A new hybrid neural network model, BP-RBF Neural Network Model is established by combining the traditional BP and RBF neural network. This new neural network model shows great superiority in higher efficiency and a simpler network structure compared with the traditional pure BP neural network model, at the same time the forecasting accuracy is ensured.