Notice of RetractionDeformation Predication and Management of Large Bridge Based on Radial Basis Function Neural Network

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

In the long-term process of use, due to the influence of various factors as material aging, load effect and environmental change, a certain degree of deformation will occur to the foundation and superstructure of the brige, the long-term monitoring for the deformation of the bridge as well as its influencing factors and the establishment of mathematical models for predicting deformation will contribute to the maintenance and management of large bridge. According to the complex characteristics of factors influencing, the improved algorithm of radial basis function neural network and network parameters was studied, and a software based on MATLAB was developed for bridge deformation predication. Combined with the monitoring data of Nanjing second Yangtze river bridge, a RBF network deformation forecasting model that based on 9 # measure point affected by many factors was established, and the deformation forecast and analysis were carried out. Research methods and the calculation and analysis results show that improved radial basis function neural network has higher prediction accuracy and can provide an important reference for the management of bridge.