Dynamic Diagnose Analyse Of The Concrete-filled Steel Tube Arch Bridge Simulation Damage

The advantage of strong leap ability and easy construction make the concrete-filled steel tube arch bridge toward large span day by day. With the increase of the span, a lot of questions are following, among them the security question is particularly outstanding. As the bridge has presented the damage, if there are measures to repair the damage in time, will jeopardize the people's personal security , cause great losses of national economy. Therefore, it has important theory meaning and practical value to go on the research that assess and diagnose the damage of step foot-path concrete-filled steel tube arch bridge.At present the question to diagnose the damage of the bridge, the domestic and international scholar carries on extensive and deep research. Study current situation indicate that there are a lot of structure dynamic methods diagnosed to damage, but all of those focus on simple structure damage mainly, diagnose to the damage of such bridge types as the cable-stay bridge , suspension bridge ,etc. that has made certain research results. However, as to step foot-path steel tube concrete arched bridge, still lack the effective methods diagnosed to damage. The development trend of method diagnosed to damage indicate neural network technology receive the development at full speed. To diagnoses the damage of step foot-path concrete-filled steel tube arch bridge, this paper have proposed three-stage hierarchical identification strategy on the basis of the natural frequency and camber mode, combining BP neural network, the main research results of the paper are as follows:1) Have summarized the research overview and the method of the dynamic diagnose method of domestic and international systematically, the research approach and thinking of damage diagnose to concrete-filled steel tube arch bridge is proposed.2) In detail discuss the principle of utilizing natural frequency, mode guarantees standard, coordinate mode guarantee standard , camber mode and gentle degrees of matrix ,etc. 5 commonly used dynamic methods to diagnose the bridge structure damage, and provide the course and step of diagnosing damage.3) The concrete-filled steel tube arch bridge is as the research object, simulation many kinds of damage state, namely single damage, many places damage, damage the position differently, damage such operating modes as the degree, etc, utilizingnatural frequency, mode guarantees standard, coordinate mode guarantee standard, camber mode and gentle degrees of matrix, etc. 5 dynamic methods to diagnose these damage. The result of calculation indicates, natural frequency and camber mode are the sensitive identification to the damaged of concrete-filled steel tube arch bridge; mode guarantees standard, coordinate mode guarantee the standard and gentle degree of matrix is not very suitable for the damage diagnose of concrete-filled steel tube arch bridge.4) Discussing the basic principle and method of the damage diagnose on the basis of artificial neural network structure. The implying layer of neural network is equivalent to the abstraction of the characteristic parameter, it is not sensitive to the parameters which has flawed characteristic or incomplete inputting, and the measurement noise and error is unavoidable while diagnosing damage, so introduce the neural network technology while diagnosing damage, it discerns the result is better, has shown the better one is suffused with the performance of melting.5) Regard the Third Jianghan Bridge as the research object, use the large-scale finite element software to set up the three-dimensional finite element model, have proposed three-stage hierarchical identification strategy on the basis of the natural frequency and camber mode, combining BP neural network: (1)damage alarming, lastly identifying the damage extent; (2)Confirm the sector of damage; (3)Assess the component damage degree. In the first stage, regard natural frequency as the introduction of the neural network, use auto-associative neural network to realize the whole bridge, because it is easy to ge...