Study On The Safety Evaluation For CFST Arch Bridge Based On Fuzzy-Neural Network

Long-span bridges are the hinges of traffic system, playing an important role in national economic and society. Recently, bridge accidents occur from time to time, which bring more and more attention to safety of existing bridges all over the world. Now the safety evaluation of bridge is becoming the hot problem in the research of bridge engineering.With the development of bride analysis theory, structure material and building technique, the span of the CFST Arch Bridges is getting longer and longer. The special mechanics behavior and the superiority in construction make the CFST Arch Bridges have a widely use in long-span bridges. But, with the growth of the span, the safety is becoming more and more important. However, there are no valid methods to evaluate the safety of the CFST Arch Bridge until now. The related researches focus on the extreme load limitation of steel tube. How to evaluate the safety of the CFST Arch Bridges under the permissibility of economic and technique condition is an important and urgent task. To this problem, this dissertation gives a deep study on the safety evaluation of the CFST Arch Bridges based on the existing safety evaluation methods. The main achievement of this dissertation are briefly mentioned as follows:1. Expatiate the existing methods of the safety evaluation of bridges, and probe into its connotation and developing direction about the safety evaluation of bridges, in the end predict that study on the intellectualized system to take the place of the present evaluation methods is the inevitable current of the bridge-evaluation theory.2. Give a complete evaluation model to evaluate the safety of the CFST Arch Bridges based on AHP (the Analytic Hierarchy process). First give a hierarchy combination from the load bearing, construct damage and bridge condition and make a three layers safety evaluation model of the CFST Arch Bridges. Further more, give it's complete index system according to the practice of the CFST Arch Bridges.3. According to different influential factors, select different evaluation indexes rationally. The structural adjusting coefficient is selected as an evaluation indexes based on the load experiments of the bridge according to the load bearing. The crack-splitting degree and steel-rusting degree are selected asevaluation indexes according to the structure damage. As to the bridge condition, we chose standard attribute degree as evaluation index based on Fuzzy-theory.4. Fuzzy theory and Artificial Neural Network technique are systemically studied, especially how to combine with these two theories are throughout studied. Give a three layers onwards-feedback Fuzzy-Neural Network model according to the practice of the CFST Arch Bridges. The evaluation model includes three parts: fuzzy-disposing import information, three layers BP Neural Network, counter fuzzy-disposing export information. Sigmoid function is adopted as activated function of Neural Network, as to fuzzy-disposing import information, normal distribution function is adopted. Evaluation samples are disposed by fuzzy theory, and then are imported into BP Neural Network. When the training of the Neural Network is finished, we can get useful safety evaluation model of the CFST Arch Bridges.5. The method of safety evaluation of the CFST Arch Bridges based on Fuzzy-Neural Network is applied on the QingChuan Bridge of Wuhan city. Select 40 team examples, which are based on inspection of QingChuan Bridge, to training the Neural Network, and the result shows that the evaluation result is unanimous to the result given by bridge engineering experts. This method is proved that it can reappear the empirical knowledge and instinctive thinking of bridge engineering experts, reduce the contrived influence and assure the objectivity of the evaluating result better.The safety evaluation of the CFST Arch Bridges is a studying field which is a more synthesis subject and correlates with many subjects. The researches are extremely difficult, but those applied prospect in engineering are vas...