Study On Substructural Stiffness Update Of Bridge Health Diagnosis

With the rapid development of modern transportation, plenty of super-large bridges have been emerging and the number of old and new bridges is constantly increasing. In order to satisfy the safety and utility requirements of bridges, it is of urgent demand to detect damage of bridges and their components effectively and to obtain in time the health state of the bridge under operation condition. In the light of the research on bridge structural damage, the paper explains the connotation of bridge health diagnosis definitely, which includes not only analysis and estimation of structural damage but also evaluation and decision of the damage. Various methods and the current status of structural damage identification are summarized and analyzed, which are the key components of structural health diagnosis. A prospect is made of the development trend of this domain based on an analysis of the main problems that presently exist. A simply-supported beam is demonstrated to verify one of the structural damage identification methods—sub-structural stiffness updating method. As in most practical cases, the number of coordinates defining the FE model exceeds by far the number of measured coordinates, two methods including modal expansion and modal reduction are used to solve this problem. The thesis also touches upon the noise problem of the actual measurement. Modal expansion methods rely on the precision of the stiffness and mass of the analyzed structure; therefore the data will contain inevitable errors when got by modal expansion method thus leading to failure result. However, accurate identification of damage location and damage level can be gained when the freedom of FEM is matched with test freedom by means of modal reduction method. But this needs increase of test points and use of iterative algorithm at the same time. When certainty identification method is used to identify damage in a single sample, measurement noise influence is great and there is no way to eliminate it. Since the bridge monitoring system can provide continuous and stable test data, this thesis uses the statistic method to eliminate the noise jamming.