Techno Press
You logged in as Techno Press

Structural Monitoring and Maintenance
  Volume 7, Number 4, December 2020, pages 283-294

Multi-variate Empirical Mode Decomposition (MEMD) for ambient modal identification of RC road bridge
Swarup Mahato, Budhaditya Hazra and Arunasis Chakraborty

    In this paper, an adaptive MEMD based modal identification technique for linear time-invariant systems is proposed employing multiple vibration measurements. Traditional empirical mode decomposition (EMD) suffers from mode-mixing during sifting operations to identify intrinsic mode functions (IMF). MEMD performs better in this context as it considers multi-channel data and projects them into a n-dimensional hypercube to evaluate the IMFs. Using this technique, modal parameters of the structural system are identified. It is observed that MEMD has superior performance compared to its traditional counterpart. However, it still suffers from mild mode-mixing in higher modes where the energy contents are low. To avoid this problem, an adaptive filtering scheme is proposed to decompose the interfering modes. The Proposed modified scheme is then applied to vibrations of a reinforced concrete road bridge. Results presented in this study show that the proposed MEMD based approach coupled with the filtering technique can effectively identify the parameters of the dominant modes present in the structural response with a significant level of accuracy.
Key Words
    hilbert transform; Multi-variate Empirical Mode Decomposition; intrinsic mode function; operational modal analysis; modal parameters of bridge
(1) Swarup Mahato:
Department of Components and System, Gustave Eiffel University, Bouguenais - 44340, France;
(2) Budhaditya Hazra, Arunasis Chakraborty:
Department of Civil Engineering, Indian Institute of Technology Guwahati, Assam - 781039, India.

Techno-Press: Publishers of international journals and conference proceedings.       Copyright © 2021 Techno Press
P.O. Box 33, Yuseong, Daejeon 305-600 Korea, Tel: +82-42-828-7996, Fax : +82-42-828-7997, Email: