基于集成式神经网络的扁平箱梁颤振导数预测

梅瀚雨; 王骑; 廖海黎; 张岩

doi:10.3969/j.issn.0258-2724.20200408

基于集成式神经网络的扁平箱梁颤振导数预测

doi: 10.3969/j.issn.0258-2724.20200408

梅瀚雨^1,,
王骑^{1, 2, ,},
廖海黎^{1, 2},
张岩¹

1.
西南交通大学土木工程学院，四川成都 610031
2.
西南交通大学风工程四川省重点实验室，四川成都 610031

基金项目: 国家自然科学基金（51778547，51678508）

详细信息

作者简介:
梅瀚雨（1994—），男，博士研究生，研究方向为桥梁与隧道工程，E-mail：mhanyu8866@gmail.com

通讯作者:
王骑（1980—），男，教授，研究方向为大跨度桥梁抗风，E-mail：wangchee_wind@swjtu.edu.cn

中图分类号: TP183；U441.3
计量
- 文章访问数: 230
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- PDF下载量: 15
- 被引次数: 0
出版历程
- 收稿日期: 2020-09-04
- 修回日期: 2020-11-10
- 刊出日期: 2020-11-11

Flutter Derivative Prediction of Flat Box Girder Based on Ensembled Neural Network

MEI Hanyu^1
,,
WANG Qi^{1, 2
, ,},
LIAO Haili^{1, 2},
ZHANG Yan¹

1.
School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China
2.
Wind Engineering Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu 610031, China

摘要

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.
- 强迫振动 /
- 风洞试验 /
- 集成学习 /
- 神经网络 /
- 颤振导数
Abstract:
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.
- forced vibration /
- wind tunnel tests /
- ensemble learning /
- neural network /
- flutter derivatives

HTML全文

图 1 典型断面示意

Figure 1. Typical section schematic

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图 2 扁平箱梁模型断面

Figure 2. Sectional models of flat box girders

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图 3 安装在风洞中的强迫振动装置及模型

Figure 3. Sectional model assembled on forced vibration device in wind tunnel

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图 4 弹簧悬挂自由振动风洞试验

Figure 4. Free vibration wind tunnel tests

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图 5 带风攻角的断面顶点坐标示意

Figure 5. Coordinate of flat box girder with angle of attack

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图 6 子神经网络预测颤振导数

Figure 6. Sub-network for flutter derivative prediction

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图 7 集成式深度神经网络实现流程

Figure 7. Flow chart of building the integrated deep neural network

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图 8 测试断面详图

Figure 8. Detailed geometry of testing section

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图 9 测试断面颤振导数预测结果对比

Figure 9. Comparison between predicted and tested flutter derivatives of testing section

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图 10 测试断面颤振导数误差

Figure 10. Error results of flutter derivatives of testing section

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图 11 训练集大小对误差结果的影响

Figure 11. Effects of training set size on error results

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表 1 颤振临界风速结果对比

Table 1. Comparison of critical flutter wind speeds

风攻角/（°）	试验值/（m·s⁻¹）	计算值/（m·s⁻¹）	误差/%
0	11.56	11.92	3.11
+3	10.79	10.53	−2.41
+5	8.54	8.71	1.99

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表 2 不同子神经网络结构设计参数

Table 2. Design parameters of different sub-neural networks

隐藏层层数	隐藏神经元个数	非线性激活函数	子神经网络个数/个
3, 4	12, 14, 16, 18, 20	tan h, sigmoid, ReLU	2 × 5 × 3= 30

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表 3 误差结果对比

Table 3. Comparison of different errors

模型	${M}_{{\rm{AE}}}$	${R}_{{\rm{MSE}}}$	$ {R}^{2} $
多项式回归	0.437	0.538	0.781
神经网络模型	0.364	0.468	0.807
本文集成模型	0.139	0.183	0.985

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