Civil Engineering Structure Damage Identification Based On Neutral Network

Engineering structures are inevitably ageing with increased serviceability time while frequent occurrences of natural disasters are also causing damage in structures, such as Kobe earthquake in Japan (in 1995) and Taiwan earthquake (in 1999), which cause great damage in numerous houses and even total collapse, taking a heavy toll on people's lives and their properties. The detection and repair of structural damage play an important role in greatly reducing the loss of lives and properties. Meanwhile, maintenance and repair costs can be lowered substantially if early recognition of structural damage is done. Therefore, real time health diagnosis and safety appraisal are most crucial for engineering structures.Dynamic characters usually may change due to structural damage. Recently, diagnosis and monitoring techniques based on characteristic parameters of structural dynamics have stood in the spotlight of research fields by academic and engineering society owing to their various advantages and convenience for real time diagnosis. In the meantime, artificial neural network in structural damage recognition has been widely heeded and researched. With the support of the fund for science and technology innovation of Wuhan university-Research on Safety Monitoring and Safety Condition Evaluation of the Building. We systemically study the method in damage identification based on modal parameters and artificial neutral network .The main contents are as follows.1. Based on the basic conception of damage mechanics, reinforced concrete -three level damage models are summarized. Representational damage models are also introduced, the mechanism of damage occurring and developing is discussed.2. Prevalent testing method based on dynamic parameters are analyzed and summarized systematically, whereas forming parameters needed often meet complicated reverse calculation problem .So the method is hard been used in practical application. With the strong non-linear mapping ability, artificial neural networks can make inverse problem into obverse one, so we combine modal analysis method with neural networks technique, taking modal parameters to construct identification parameters diagnostic vector as input vector for nondestructive testing.3. Generally, Structural Damage Identification include existence , location anddegree of damage-three level problem. Through two factual examples, we try to solve damage identification problem step by step. The method is effective with satisfactory result4. Dynamic characteristics of the structure are directly related to physical parameters, the relationship between frequency -. mode -. mode variance and damage location together with damage degree is discussed.5. During course of practical identification, actual errors inevitably impact the result. Study of the relationship between errors and reliability has shown that the influence of low-level measured errors is limited. As for model errors, a useful proposal is given.6. Nowadays, large numbers of research results are focusing on simple component such as beam ^ board and pillar. Benchmark problem in structural health monitoring was studied in this paper, and the results shows that identification by stages can be useful for simplification and solution to a question.Finally, some results are summarized in this project with concerning prospect for the further research.