关键词: 桥梁工程 ；钢混组合桥梁；构件定位精度；优化果蝇算法；BP神经网络；混沌动态权重

Abstract: In order to improve the installation accuracy of steel members of bridges, an improved BP neural network based on the optimized fruit fly algorithm was established to accurately predict the spatial coordinates of steel members. In the fruit fly algorithm, Logistic mapping was introduced to optimize the initial position of fruit fly population, chaotic dynamic weight and multi-population cooperative search were added to enhance the global search and local exploration ability of the algorithm, which effectively solved the problems of long training time and low accuracy of BP neural network in dealing with complex engineering problems. The improved BP neural network could identify many variables that affected the positioning of steel components and screened out the key variables. Combined with an engineering example of a corrugated steel web-prestressed concrete composite beam low tower cable-stayed bridge, the optimized neural network was used to predict the spatial coordinates of steel web and steel anchor box in the section to be constructed. The research shows that the hole shaft deviation and cable casing deviation of the steel anchor box and corrugated steel web opening connector of the bridge have been greatly improved through the verification of the construction process, which reflects the good application prospect of the improved BP network in accurately predicting the spatial coordinates of steel members.

Key words: bridge engineering; steel and concrete composite bridge; positioning accuracy of member; optimized fruit fly algorithm; BP neural network; chaotic dynamic weight