市政技术2024，Vol.42Issue(6)：135-141,7.DOI:10.19922/j.1009-7767.2024.06.135
基于MEC-BP神经网络的臧湾东河特大桥施工挠度监测研究
Study on Construction Deflection Monitoring of Zangwan Donghe Bridge Based on MEC-BP Neural Network
高福忠1
作者信息
- 1. 中铁十八局集团第一工程有限公司,河北保定 072750
- 折叠
摘要
Abstract
In order to improve the accuracy of construction deflection prediction of the large bridge,the Zangwan Donghe Bridge is taken as the research object.The construction deflection of the bridge is predicted by the MEC-BP neural network model.And the predicted values are compared with the numerical simulation ones and the measured ones.The results show that the difference between the measured values and the predicted values of the MEC-BP model is smaller.The MEC-BP model shows good accuracy on the training samples;The performance of the MEC-BP model is significantly better than the traditional BP one and has higher efficiency and accuracy in the deflection prediction with the average errors of less than 5 mm.MEC algorithm helps to realize the whole optimization of the parameters of traditional BP model,which can improve the ability of predicting the mechanical behavior of bridge structure,and provide an effective solution for the structural safety problems during the construction of continuous girder bridges.关键词
连续梁桥/MEC-BP神经网络/挠度/现场实测Key words
continuous girder bridge/MEC-BP neural network/deflection/field measurement分类
交通工程引用本文复制引用
高福忠..基于MEC-BP神经网络的臧湾东河特大桥施工挠度监测研究[J].市政技术,2024,42(6):135-141,7.
