Damage Identification Research For Large-scale Bridge Structures

The health of bridge structure plays a very crucial role of people’s lives, the national economy since bridge structure is an important component of traffic. In recent years, many bridge structures have been broken because of the rising traffic volume. The carrying capacity of bridge is reduced and the safety, applicability and durability of bridge structure do not meet safety standard. As a result, it has great practical significance to monitor the health of bridge realtimely, find out the damage of bridge structure, assess its safety and develop appropriate maintenance a repair strategies to improve the operational efficiency of the bridge structure.Main research works in this paper are as follows:1. Modeling a plane truss structure, do analogy computation to the damage of simply supported by ANSYS12.0,Get the required modal parameters(Strain mode; Difference of modal curvature; Normalized change ratio of frequency),Analysis the relationship between structure damage and these parameters, Determine the location of structural damage to the relationship. Then, Considering the impact of the structure’s different degrees damage on the modal parameters, Largely determine the extent of structural damage from the value.2. To the same truss structure, Identify the degree of structural damage by RBF neural networks method. Use the modal parameters (modal strain)of varying degrees of damage as input, use the power of your imagination neural network’s robustness, fault tolerance and generalization ability, Eventually it can effectively identify the extent of damage. And the damage degree of the truss structure is identified when environmental noise is considered.3、Analysis of the damage identification to a bridge of Changjiang River. The method to identify the damage location of the bridge is to select the curvature modal difference as recognition parameters, through the risk analysis of the bridge, the establishment of finite element model and vibration analysis. And the method is as same as the damage identification of truss structure. In the identification of damage degree, the BP neural network which is more suitable is selected. Finally, environmental noise is considered, and the results are acceptable when the noise is less than5%.