Research On Continuous Rigid Frame Bridge Grider Design Parameter Optimization Based On Dynamic Response

As a kind of bridge,The prestressed reinforced concrete continuous rigid frame bridge,Which has the advantage of nice wholeness,the larger span,bearing property, moderate costing,construction convenience.smooth driving and beautiful appearance. It is widely used in high pier and large span structure. According to the studies:When selecting bridge that main span in the range of 150m to 250m, Continuous rigid frame bridge is vast majority. But at present, The seismic design of continuous rigid frame bridge is mainly based on experience and calculation. But such design is usually too conservative and has genetic nature of design defects, So it is necessary to optimize the design parameters of continuous rigid frame bridge.This paper mainly choosing Ratio of side to middle span> The beam bottom effect curve-. The 0 beam height and the middle beam height as variables on the background of a prestressed reinforced concrete continuous rigid frame bridge in KunMing, Establishing 25continuous rigid frame models with different parameters. Garrying on static elastic plastic analysis(Pushover) is based on ANSYS10.1 software and combine orthogonal test, neural network with genetic algorithm, Studing seismic performance optimization of continuous rigid frame bridge. The main work is summarized as follows:(1)This paper introduces the development and direction of the continuous rigid frame bridge, Discussing the necessity of the research on the seismic optimization of the continuous rigid frame bridge and the present situation and deficiency a,It is welly introduces the structure optimization design and seismic response analysis method, especially pushover analysis method, The APDL language modeling are introduced in this paper.(2)Paper sorted out the parameters of continuous rigid frame bridges which have been built, Obtaines corresponding application range and make target function according to the seismic fortification level. After the preliminary optimization of the orthogonal test, Results is follow:Ratio of side to middle span is 0.6, The beam bottom effect curve is 1.6, The 0 beam height is 11.21m, The middle beam height is 4.75m. The influence degree of each factor on the experimental results was obtained through the range analysis of the optimization results.(3)Paper introduces the working principle of genetic algorithm and neural network and analysis function and application of MATLAB tool, Combined the orthogonal table,neural network with genetic algorithm to optimize, Results is follow:Ratio of side to middle span is 0.57, The beam bottom effect curve is 1.6, The 0 beam height is 11.56m, The middle beam height is 4.75m.(4) Comparing the Optimization results of chapter five and chapter four, Finding multi objective function minimum decreased from the fourth chapter of 0.033 to 0.0254, decreased by 23%,Suggesting that the use of more accurate optimization with orthogonal table, neural network and genetic algorithm.