Based On Kalman Filtering BP Neural Network Model In Bridge Deformation Application

Continues the stable high speed development along with our country economy, the high-speed railroad construction are more and more many.As concerns the national economy development the important infrastructure, China takes the railroad construction highly, the investment large increase. The high-speed railroad must provide a hing smooth compliance and under the high stable axle for the train high speed travel the foundation, but the pile foundation took the orbital structure the foundation, must maintain under the operation condition the line track design variable in the request standard scope, this without doubt on stably set the very high request to the high-speed railroad subsidence.Therefore, the pile foundation subsidence stability as well as the subsidence forecast the high-speed railroad grade location and the construction key.This article by Beijing to Shanghai high-valence iron--take Beijing to Shanghai high-valence iron as an example, Periodically to pile settlement observation, and unifies local the geological feature and the geological condition, from Beijing to Shanghai high-valence iron subsidence aspects and so on monitoring network establishment, observation content, observation precision, observation frequency makes the quite systematic elaboration, specially to the pile foundation, the arch of bridge, the tunnel as well as the change-over portion settlement observation has done the thorough research.Has studied the Kalman filtering model and the dynamic neural network model in bridge deformation forecast application, and used in the two foundation one kind based on the Kalman filtering algorithm BP neural network model, this kind of model can synthesize the two the merit, forecast the precision had the very big enhancement. This will be the model is applied to predict the settlement of pile foundation, bridge and achieved good effect, the settlement of pile foundation for the bridge for new ideas.