Building settlement prediction based on Kalman filtering of different state models
The prediction of building deformation is of great importance to reduce the loss of human life and property.Therefore,building a model based on deformation monitoring data is an important method for deformation prediction analysis.This paper uses Kalman filtering and builds prediction algorithms based on different state models,verifies and analyzes the prediction accuracy of different models,and ob-tains the optimal prediction model to improve the prediction accuracy.It selects the deformation monito-ring data of Chang'an University Geoscience and Technology Building in various phases and combines with Kalman filtering algorithm,uses three different state estimation models of random wandering,con-stant velocity and constant acceleration to build the building settlement prediction algorithm,and verfies and compares the different algorithms by experiments.The results show that the accuracy based on the constant acceleration state model is better than based on the constant velocity state model and based on the random walk model,with an improvement of 15％and 21％respectively.
万方数据
维普
