Structural Health Monitoring of a Vertical Lift Bridge Using Vibration Data

An objective decision-making criterion is developed for condition assessment of the new Memorial Bridge connecting Portsmouth, New Hampshire to Kittery, Main, United States. The analysis is based on the normalized energy of acceleration signals obtained from a series of accelerometers permanently deployed along the bridge. In the present paper, a Wavelet Packet Transform (WPT) is used as a means to decompose the measured signals with an arbitrary time-frequency resolution. A unique aspect of this approach is the coupling of various techniques in an effort to enhance the discrimination between vibration-based data recorded from different states of the structure. Firstly, the wavelet packet component that represents the most dominant patterns of variation of the signal properties is determined through wavelet analysis. Secondly, the wavelet packet component normalized coefficient is computed for each sensor. Finally, the mean coefficient obtained from the entire set of sensors in each day is calculated for further investigations through a control chart analysis. A statistical framework is developed to train a baseline model in the early age of the bridge when the condition is undamaged. The principal theory behind the methodology relies on assumption that the variations of the extracted features between the estimated control limits correspond mainly to normal operating conditions of the bridge. Therefore, the exceedance of future indicators from the enclosure signifies the presence of unusual sources of variability. The proposed approach is analytically verified through a Finite Element (FE) model of the bridge subjected to structural damage in one of its diagonals. Results indicate a significant distinction between undamaged and damaged responses of the bridge.

DOI: https://doi.org/10.32548/RS.2018.027

References

 

  • Hu, W.C, C. Moutinho, E. Caetano, F. Magalhães, and Á. Cunha, 2012, "Continuous Dynamic Monitoring of a Lively Footbridge for Serviceability Assessment and Damage Detection," Mechanical Systems and Signal Processing, 33: pp 38-55.
  • Shahsavari, V., J. Bastien, L. Chouinard, and A. Clément, 2017, "Likelihood-Based Testing of Wavelet Coefficients for Damage Detection in Beam Structures," Journal of Civil Structural Health Monitoring, 7(1): pp 79-98.
  • García Palencia, A Javier, and E SantiniBell, 2013, "A TwoStep Model Updating Algorithm for Parameter Identification of Linear Elastic Damped Structures," ComputerAided Civil and Infrastructure Engineering, 28(7): pp 509-521.
  • Daubechies, I., 1992, Ten Lectures on Wavelets. Vol. 61. Society for Industrial and Applied Mathematics, Library of Congress Cataloging in-Publication-Data, PA
  • Sohn, H., J.A. Czarnecki, and C.R. Farrar, (2000), "Structural Health Monitoring Using Statistical Process Control," Journal of structural engineering, 126(11): PP 1356-1363.
  • Liew, K. M., and Q. Wang, 1998, "Application of Wavelet Theory for Crack Identification in Structures," Journal of Engineering Mechanics, 124 (2): pp 152-157.
  • Shahsavari, V., J. Bastien, L. Chouinard, and A. Clément, 2015, "A Novel Response-Based Approach to Localize Low Intensity Damage of Beam-Like Structures," 5th International Conference on Smart Materials and Nanotechnology in Engineering, Vancouver, BC, Canada.
  • Jaiswal, N. G., and D. W. Pande, 2015, "Sensitizing the Mode Shapes of Beam towards Damage Detection Using Curvature and Wavelet Transform," International Journal of Scientific & Technology Research, 4(4): pp 266-272.
  • Vafaei, M., S.C. Alih, A.B. Rahman, and A.Z. Adnan, 2015, "A Wavelet-Based Technique for Damage Quantification via Mode Shape Decomposition," Structure and Infrastructure Engineering, 11(7): pp 869-883. Yen, G.G., and K.C. Lin, 2000, "Wavelet Packet Feature Extraction for Vibration Monitoring," IEEE Transactions on Industrial Electronics, 47 (3): pp 650-667.
  • Sun, Z., and C.C. Chang, 2002. "Structural Damage Assessment Based on Wavelet Packet Transform," Journal of Structural Engineering, 128(10): pp 1354-1361.
  • Han, J.G., W.X. Ren, and Z.S. Sun, 2005, "Wavelet Packet Based Damage Identification of Beam Structures," International Journal of Solids and Structures, 42(26): pp 6610-6627.
  • Shahsavari, V., L. Chouinard, and J. Bastien, 2017, "Wavelet-Based Analysis of Mode Shapes for Statistical Detection and Localization of Damage in Beams Using Likelihood Ratio Test." Engineering Structures, 132: pp 494-507.
  • Montgomery, D.C., 2009, Introduction to statistical quality control. John Wiley & Sons, New York. Portsmouth Memorial Bridge, http://livingbridge.unh.edu.
  • Nash, T.P., 2016, "An Objective Protocol for Movable Bridge Operation in High-Wind Events Based on Hybrid Analyses by European and American Design Code," Master’s Thesis, University of New Hampshire. Yan, A-M., G. Kerschen, P.D. Boe, and J.C. Golinval, 2005, "Structural Damage Diagnosis under Varying Environmental Conditions—Part I: A Linear Analysis." Mechanical Systems and Signal Processing, 19(4): pp 847-864.
  • Mehrkash, M., and E. S-Bell, 2018, “Modeling and Characterization of Complicated Connections in Structural and Mechanical Systems as Applied to a Gusset-less Truss Connection,” 97th Annual Meeting of Transportation Research Board, Washington D.C., USA.
Metrics
Usage Shares
Total Views
24 Page Views
Total Shares
0 Tweets
24
0 PDF Downloads
0
0 Facebook Shares
Total Usage
24