摘要
扁平箱梁因具有较优的颤振性能,已被应用于绝大多数大跨径桥梁.为便于桥梁设计者在大跨度桥梁初步设计阶段快速评估扁平箱梁的颤振性能,提出了一种基于集成学习的深度神经网络模型,用于快速预测扁平箱梁颤振导数.首先采用强迫振动风洞试验获取了15种典型扁平箱梁的颤振导数,结合自由振动风洞试验和二维颤振计算验证了颤振导数的准确性;基于风洞试验数据,构建了大小为525的颤振导数数据集,以此数据集为基础,对所提出的集成式深度神经网络开展了模型训练和性能测试.计算结果表明:所提出的集成式深度神经网络模型仅依靠扁平箱梁的气动外形特征即可准确且快速地预测不同折算风速下的8个颤振导数,且仅利用本文60%的数据集进行训练即可获取较高精度的预测结果;对比传统的多项式回归模型和单一人工神经网络模型,本文所提出的集成式深度神经网络模型预测精度更高,可直接应用到桥梁初步设计阶段的气动选型和颤振计算中.
Flat box girder has been used in most long-span bridge because of its excellent flutter performance.To facilitate bridge designers to quickly evaluate the flutter performance of flat box girders in the preliminary design stage of long span bridges,a deep neural network model based on ensemble learning was proposed for quickly predicting flutter derivatives of flat box girders.Firstly,the flutter derivatives of 15 typical flat box girders were obtained by forced vibration wind tunnel tests,and the accuracy of flutter derivatives was verified by combining the free vibration wind tunnel test and two-dimensional flutter analysis.Then,a flutter derivative dataset with the size of 525 was constructed based on wind tunnel testing data.The proposed ensemble deep neural network model was trained and tested based on the dataset.The results show that the proposed ensemble deep neural network model can accurately and quickly predict the 8 flutter derivatives at different reduced wind speeds by relying only on the box geometry properties of the flat box girder,and only using 60%of the training dataset for training can obtain acceptable prediction results with enough precision.Compared with the traditional polynomial regression model and the single artificial neural network model,the ensemble deep neural network model proposed in this paper has higher prediction accuracy and can be directly applied to the geometry selection and flutter prediction procedure in the preliminary design stage of bridges.
作者
梅瀚雨
王骑
廖海黎
张岩
MEI Hanyu;WANG Qi;LIAO Haili;ZHANG Yan(School of Civil Engineering,Southwest Jiaotong University,Chengdu 610031,China;Wind Engineering Key Laboratory of Sichuan Province,Southwest Jiaotong University,Chengdu 610031,China)
出处
《西南交通大学学报》
EI
CSCD
北大核心
2022年第4期894-902,共9页
Journal of Southwest Jiaotong University
基金
国家自然科学基金(51778547,51678508)。
关键词
强迫振动
风洞试验
集成学习
神经网络
颤振导数
forced vibration
wind tunnel tests
ensemble learning
neural network
flutter derivatives
