当前位置: X-MOL 学术Struct. Health Monit. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Investigation on the data augmentation using machine learning algorithms in structural health monitoring information
Structural Health Monitoring ( IF 6.6 ) Pub Date : 2021-03-11 , DOI: 10.1177/1475921721996238
Xuyan Tan 1, 2 , Xuanxuan Sun 3 , Weizhong Chen 1, 2 , Bowen Du 3 , Junchen Ye 3 , Leilei Sun 3
Affiliation  

Structural health monitoring system plays a vital role in smart management of civil engineering. A lot of efforts have been motivated to improve data quality through mean, median values, or simple interpolation methods, which are low-precision and not fully reflected field conditions due to the neglect of strong spatio-temporal correlations borne by monitoring datasets and the thoughtless for various forms of abnormal conditions. Along this line, this article proposed an integrated framework for data augmentation in structural health monitoring system using machine learning algorithms. As a case study, the monitoring data obtained from structural health monitoring system in the Nanjing Yangtze River Tunnel are selected to make experience. First, the original data are reconstructed based on an improved non-negative matrix factorization model to detect abnormal conditions occurred in different cases. Subsequently, multiple supervised learning methods are introduced to process the abnormal conditions detected by non-negative matrix factorization. The effectiveness of multiple supervised learning methods at different missing ratios is discussed to improve its university. The experimental results indicate that non-negative matrix factorization can recognize different abnormal situations simultaneously. The supervised learning algorithms expressed good effects to impute datasets under different missing rates. Therefore, the presented framework is applied to this case for data augmentation, which is crucial for further analysis and provides an important reference for similar projects.



中文翻译:

在结构健康监测信息中使用机器学习算法进行数据扩充的研究

结构健康监测系统在土木工程的智能管理中起着至关重要的作用。由于平均值,中值或简单的插值方法,由于忽略了监视数据集所带来的强烈的时空相关性而忽略了很强的时空相关性,因此已经采取了许多努力来提高数据质量,这些方法的精度较低且无法完全反映现场条件用于各种形式的异常情况。沿着这一思路,本文提出了使用机器学习算法在结构健康监测系统中进行数据增强的集成框架。以南京长江隧道结构健康监测系统为例,从监测数据中总结经验。第一的,基于改进的非负矩阵分解模型重建原始数据,以检测在不同情况下发生的异常情况。随后,引入了多种监督学习方法来处理通过非负矩阵分解检测到的异常情况。讨论了在不同遗漏率下多种监督学习方法的有效性,以提高其大学水平。实验结果表明,非负矩阵分解可以同时识别不同的异常情况。监督学习算法在不同丢失率下对数据集的插补表现出良好的效果。因此,提出的框架适用于这种情况下的数据扩充,这对于进一步分析至关重要,并且为类似项目提供了重要参考。

更新日期:2021-03-11
down
wechat
bug