传统的基于灵敏度分析的模型修正方法难以确定合适的待修正设计参数，而大多数不依赖灵敏度分析的模型修正方法往往计算量偏大，计算过程复杂．文中采用参数型模型修正方法，引入基于遗传算法优化的 BP 神经网络，以结构动力特征向量为输入量，直接得到修正后设计参数．并由该参数得到有限元模型动力特征向量误差，以此误差最小化为目标函数进行迭代求解，确定最终的修正参数．最后以一座 3 跨连续梁模型进行试验，验证了该方法的准确性与有效性．

The traditional model updating methods based on sensitivity analysis can not determine the appropriate design parameters to be updated,and most of the model updating methods independent of sensitivity analysis are complex with large computation.In this paper,a model updating method based on parameters is adopted,and a BP neural network optimized by genetic algorithm is introduced.Then,with the structural dynamic eigen vector as the input,the updated design parameters are directly obtained.The parameters are then used to obtain the structural dynamic eigen vector error of the updated finite element model,and the error minimization is taken as the objective function to implement iterative resolution,thus determining the final updated design parameters.Finally,a 3- span continuous beam model is tested.The results prove that the proposed method is accurate and effective.