Damage Identification Of Arch Bridge Based On Artificial Neural Network

With the expeditious development of our civil traffic projects, plenty of oversize bridges have constantly rushed. The number of new and old bridges has been increased. In order to insure safety and security of the people's health and wealth, it is the focus of current bridge project to detect fleetly and effectively structural damaged position and extent likely occurring and to command the health status of bridge in commonly using state in time. The special mechanics behavior and the superiority in configuration make the Arch Bridges have been a widely used in long-span bridges. However, there are no valid methods to evaluate the safety of the Arch Bridge until now. How to evaluate the safety of the Arch Bridges under the permissibility of economic and technique condition is an important and urgent task.Through analyzing the research findings of structural damage identification based on artificial neural networks and comparing the capabilities of BP and RBF neural networks, this thesis presents that Arch Bridge damage detection can be researched based on RBF neural networks and the process is given.On the basis of collection and analysis of the data about structural damage identification and artificial neural networks, combined with the prospect of bridge damage identification and artificial neural networks, vibration modal analysis theory is integrated with RBF neural networks so as to detect Arch Bridge damage with the help of ANSYS and MATLAB, at the same time, it has been successful in detecting damaged position and extent likely occurring in this thesis. Take the Qiuxihe Bridge as the background, damage-detection-oriented finite element model of the bridge is established and the free vibration analysis is then carried out. The focus of the research is placed on three instances, including: single element damaged; two elements and three elements damaged. Modal frequencies, mode shapes, curvature mode are used as RBF neural networks input vector respectively; sample data of each damaged state are collected; some RBF neural networks models are established for researching Arch Bridge damage detection. The result indicates that RBF neural networks can detect not only the damage position but also the damage degree.