| Among many methods that deal with structural damage assessment, most of them focus on simple structures and don抰 consider any errors, so there is a certain distance before they can be used to assess the damage of real structures. This dissertation carries on research of real engineering structures. The main contents include:1 Take high pile and beam bridge for example, this paper discusses the merits and defects of the damage assessment method based on sensitivity analysis. In order to overcome the defect that this method can抰 identify serious damage, this paper presents iteration method to solve this problem. When damaged elements increase at the same time and the number of element is much more than that of measured modes, the sensitivity analysis method can抰 give desired results. Therefore, a method which can identify damage though two steps, Location and Extent, is put forward. After probable damage location through MSECR, the method based on the sensitivity of orthogonality conditions gives good predictions of damage. This method overcomes the natural defect of the concept first order approximation of the ordinary sensitivity analysis method. But this method needs complete measured modes.2.The influence of model error on damage assessment results is analyzed. We get the sensitivity matrix using the model that contains model error in the following two cases. If the identification data are got using the model without model error before and after the damage, the error resulted from model error can be overlooked. If the data are got using model with model error before the damage and using the model without model error after the damage, error results from model error is first order and model error can bring more assessment errors. All above is proved by equations and examples.3..The effect of measurement error is considered as Gauss white noise. Through theoretical analysis and numerical simulation, damage assessment results are also subject to Gauss distribution. Furthermore, results indicate that the mean keeps intact, but the standard deviation is linear to error level.4.Using processed frequencies and first order modal DOFs in a few points as network inputs, trained neural network gives excellent assessment results of a steel truss bridge. A method is put forward which selects modal DOFs in a few points according to element strain energy coefficient. Therefore, the selected modal DOFs in a few points usually have large amplitude, which is conducive to be measured and the network training. The effect of model error on assessment results is also reflected in the ANN method.5.This paper uses ANN to realize the damage monitoring of a large structure, a concrete-filled steel tubular bridge of Beichuan River. For the damage of the main arch-ring elements, this paper uses the relative change of first order strain mode before and after thedamage. A combined network which consists of four subnets is put forward. Every subnet is trained using the data of one portion of arch-ring elements, and the outputs from all subnets give the whole damage information of the arch-ring. The influence of hanger damage on the archring strain mode is also considered and get the conclusion that there is no much change in strain mode when the damaged hangers is small. Therefore, network can still gives good results when some hangers are damaged lightly.6..For the damage of the hanger elements, the change of first order mode of deck nodes before and after the damage is used. The Kohonen network was put forward to realize the location of the damaged hangers. When the hangers are damaged as seriously as 10%. 90%, the network still gives good predictions. At last, comparisons indicate that the damage of arch-ring elements with different location have different influence on the location of the damaged hangers.
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