Study On Damage Detection Of Shengli Yellow River Bridge

With the expeditious development of our civil traffic projects, plenty of oversize and novel 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 dynamic characteristic of structures has a direct relationship with structural parameters, and structural damage can cause dynamic characteristics shifts correspondently. Therefore, if the mapping relationship between structural damage and dynamic characteristic shifts can be established, the damage can be diagnosed by using dynamic measurements of the structures. According to the measured data and identification principle, the identification methods can be divided into the method based on frequency, mode shape, modal flexibility and neural network and so on. Whereas, only lower modes and incomplete DOF measurement information can be obtained in the field test for the civil infrastructures. It is a challenging task to identify the structural damage by using of the lower frequencies, which is available in practical test.The steel cable-stayed bridge, Shengli Yellow River Bridge is the first one in Mainland China, was studied in the dissertation. Surrounding the subject of structural damage identification, the methods of structural damage identification based on modal flexibility and neural network are respectively introduced, and the numerical simulation for the damage identification of the cable-stayed bridge is carried out.The flexibility of the structure would be changed if the structure damaged. So the location and the degree of the damaged structure can be identified due to the variations of flexibility matrix. In this paper we set up the methods of damage detection based on the flexibility difference and the flexibility curvature. Numerical simulation results of steel beam demonstrate that this method is capable of locating and quantifying structural damage. Besides, the identification of multiple damaged locations in structures is also studied.With the merits of its strong nonlinear mapping ability, anti-interference capability and rapid computation, artificial neural network, which is used in the identification of structural damage based on vibration, becoming more popularly. However, for large scale complicated structures, the network structure is usually complicated and identification efficiency is low. A method of substructural damage identification is established using the probabilistic neuralnetwork in this thesis. The result shows that this method can reduce the complexity of the network and improve learning efficiency effectively.The results demonstrate that the proposed methods can localize the structural damages for cable-stayed bridge, which can be applied to the safety assessment.