Data-based structural health monitoring using small training data sets-Reference-Cited by-同舟云学术

Data-based structural health monitoring using small training data sets

Published:2015-03-19 Issue:10 Volume:22 Page:1240-1264
ISSN:1545-2255
Container-title:Structural Control and Health Monitoring
language:en
Short-container-title:Struct. Control Health Monit.

Author:

Balsamo Luciana¹,Betti Raimondo¹

Affiliation:

1. Department of Civil Engineering and Engineering Mechanics; Columbia University; New York NY 10027 U.S.A.

Funder

Center for Advanced Infrastructure and Transportation (CAIT)

Publisher

Wiley

Subject

Mechanics of Materials,Building and Construction,Civil and Structural Engineering

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/stc.1744/fullpdf

Reference34 articles.

1. Identifying damage sensitive features using nonlinear time-series and bispectral analysis;George;Proceedings of the International Modal Analysis Conference-IMAC,2000

2. Structural health monitoring using statistical pattern recognition techniques;Sohn;Transactions of the ASME,2001

3. Lei Y Kiremidjian AS Nair KK Lynch JP La KH Kenny TW Carryer E Kottapalli A Statistical damage detection using time series analysis on a structural health monitoring benchmark problem Proceedings of the 9th International Conference on Applications of Statistics and Probability in Civil Engineering 2003

Cited by 35 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Short-term damage alarming with limited vibration data in bridge structures: A fully non-parametric machine learning technique;Measurement;2024-08

2. Time-History Identification of Physical Properties of a Single-Storey Steel Frame Subjected to a Seismic Excitation;Lecture Notes in Civil Engineering;2024

3. Fishbone model-based inversion to estimate physical parameters of elastic structures under earthquake excitations;Frontiers in Built Environment;2023-07-20

4. Investigation of Frequency-Domain Dimension Reduction for A2M-Based Bridge Damage Detection Using Accelerations of Moving Vehicles;Materials;2023-02-24

5. A locally unsupervised hybrid learning method for removing environmental effects under different measurement periods;Measurement;2023-02