|本期目录/Table of Contents|

[1]游 颖,王 建,刘学刚,等.改进BP神经网络的钢结构应力缺失数据重构[J].建筑科学与工程学报,2022,39(04):166-173.[doi:10.19815/j.jace.2021.11130]
　YOU Ying,WANG Jian,LIU Xue-gang,et al.Reconstruction of Missing Stress Data for Steel Structure Based on Improved BP Neural Network[J].Journal of Architecture and Civil Engineering,2022,39(04):166-173.[doi:10.19815/j.jace.2021.11130]
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改进BP神经网络的钢结构应力缺失数据重构(PDF)
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《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

卷:
39卷
期数:
2022年04期
页码:
166-173
栏目:
出版日期:
2022-07-12

文章信息/Info

Title:
Reconstruction of Missing Stress Data for Steel Structure Based on Improved BP Neural Network
文章编号:
1673-2049(2022)04-0166-08
作者:
游 颖1,王 建1,刘学刚2,彭 宁1,周敏峰2,邓志扬1
(1. 湖北工业大学 机械工程学院,湖北 武汉 430064; 2. 武汉中科科创工程检测有限公司,湖北 武汉 430077)
Author(s):
YOU Ying1, WANG Jian1, LIU Xue-gang2, PENG Ning1, ZHOU Min-feng2, DENG Zhi-yang1
(1. School of Mechanical Engineering, Hubei University of Technology, Wuhan 430064, Hubei, China; 2. Wuhan Zhongke Kechuang Engineering Inspection Co., Ltd., Wuhan 430077, Hubei, China)
关键词:
钢结构;  数据重构;  改进BP神经网络;  结构健康监测;  粒子群算法;  附加动量法
Keywords:
steel structure;  data reconstruction;  improved BP neural network;  structural health monitoring;  particle swarm algorithm;  additional momentum method
分类号:
TU391
DOI:
10.19815/j.jace.2021.11130
文献标志码:
A
摘要:
钢结构健康监测过程中的应力数据缺失会干扰监测各环节的运行状态,无法保障施工阶段的安全,因此解决数据缺失问题至关重要。钢结构在施工阶段应力应变监测中,由于受到外界诸多复杂因素的影响,导致监测数据不准确、缺失以及局部应力数据的重构值与真实值偏差较大等问题。采用改进BP神经网络分别对300组和30组钢结构应力应变监测数据进行重构,并对改进BP神经网络的数据重构方法进行适用性分析。结果表明:相比线性回归法,改进BP神经网络法进行离散型缺失数据的重构平均误差降低0.7%,特别是对于局部缺失数据,改进BP神经网络法的重构精度更高,平均局部误差降低2.2%; 为达到较好的重构精度,使用改进BP神经网络对缺失数据重构时,数据的缺失率不宜超过20%; 改进BP神经网络法可为钢结构应力缺失数据重构以及结构健康监测提供技术支持,具有较好的实用性。
Abstract:
The lack of stress data in the process of steel structure health monitoring will interfere with the operation status of each link of monitoring, and cannot guarantee the safety of the construction stage. Therefore, it is very important to solve the problem of data missing. In the stress and strain monitoring of the steel structure during the construction stage, due to the influence of many complex external factors, the monitoring data is inaccurate and missing, and large deviation is between the structural value and the real value. The improved BP neural network was used to reconstruct the monitoring data of the stress and strain of the steel structure of the 300 groups and 30 groups respectively. The applicability of the data reconstruction method of the improved BP neural network was analyzed. The results show that compared with the linear regression method, the improved BP neural network method reduces the average error of discrete missing data reconstruction by 0.7%. Especially for locally missing data, the reconstruction accuracy of the neural network method is higher, and the average local error is reduced by 2.2%. In order to achieve better reconstruction accuracy, when using the improved BP neural network to reconstruct missing data, the missing rate of data should not exceed 20%. The improved BP neural network method can provide technical support for the reconstruction of missing stress data of steel structures and structural health monitoring, which has good practicability.

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备注/Memo

备注/Memo:
收稿日期:2021-11-16
基金项目:国家自然科学基金项目(52105550); 湖北省自然科学基金项目(2013CFB025)
作者简介:游 颖(1969-),女,湖北赤壁人,副教授,工学博士,E-mail:774085476@qq.com。
更新日期/Last Update: 2022-07-10