Communications - Scientific Letters of the University of Zilina 2019, 21(2):75-80 | DOI: 10.26552/com.C.2019.2.75-80

Computer Simulation Models for Consideration of Seasonal Trends Influence on the Structural Dynamics of Bridges

Taisiya V. Shepitko1, Elena S. Shepitko1, Vladimir S. Afanasev1
1 Institute of the Railway Track, Construction and Structure, Russian University of Transport (MIIT), Moscow, Russia

Elimination of seasonality temperature trends in a structural health monitoring of bridges is considered in the article. On example of a beam bridge, eigenfrequencies are plotted against seasonal fluctuations of environmental temperature. Next, analysis of a time series, formed from a data of frequency changes in the bridge, was made. To describe the time series, two different methods were implemented: ARIMA model based on a statistical relationship between the data and LSTM model of a recurrent neural network.

Keywords: computer simulation; seasonality elimination; structural health monitoring; time series; forecast; SHM; ARIMA; recurrent neural network

Received: January 24, 2019; Accepted: February 20, 2019; Published: May 24, 2019  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Shepitko, T.V., Shepitko, E.S., & Afanasev, V.S. (2019). Computer Simulation Models for Consideration of Seasonal Trends Influence on the Structural Dynamics of Bridges. Communications - Scientific Letters of the University of Zilina, 21(2), 75-80. doi: 10.26552/com.C.2019.2.75-80
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. KO, J. M., et al. Formulation of an uncertainty model relating modal parameters and environmental factors by using long-term monitoring data. SPIE 5057, Smart Structures and Materials 2003: Smart Systems and Nondestructive Evaluation for Civil Infrastructures : proceedings [online]. Vol. 5057. 2003, p. 298-307. Available from: https://doi.org/10.1117/12.482380 Go to original source...
    2. RAMOS, L. F. Damage identification on masonry structures based on vibration signatures. PhD Thesis. Braga: University of Minho, 2007.
    3. MAECK, J., PEETERS, B., DE ROECK, G. Damage identification on the Z24-bridge using vibration monitoring. Smart Materials and Structures [online]. 2001, 10(3), p. 512-517. ISSN 0964-1726/eISSN 1361-665X. Available from: https://doi.org/10.1088/0964-1726/10/3/313 Go to original source...
    4. WONG, K. Y., MAN, K. L., CHAN, W. Y., Monitoring Hong Kong's bridges real-time kinematic spans the gap. GPS World. 2001, 12(7), p. 10-18.
    5. International Federation for Structural Concrete. Task group 2.8. Safety and performance concepts: Reliability assessment of concrete structures: Guide to good practice. In: Bulletin / Feedeeration internationale du beeton. NO. 86. Lausanne, 2018. ISBN 978-2-88394-126-7.
    6. Recommendations for calculating the temperature and shrinkage effects on span structures of bridges. CNIIS, 2009.
    7. BOX, G. E. P., et al. Time series analysis: Forecasting and control. 5. ed. Hoboken, New Jersey: John Wiley & Sons, 2015. ISBN: 978-1-118-67502-1.
    8. CRONE, S. F., HIBON, M., NIKOLOPOULOS, K. Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction. International Journal of Forecasting [online]. 2011, 27(3), p. 635-660. ISSN 0169-2070. Available from: https://doi.org/10.1016/j.ijforecast.2011.04.001 Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content