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"Vibration-based, output-only damage identification of bridge under vehicle induced excitation" presented at CERI2018 by JJ Moughty

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Many traditional methods of damage identification in bridge structures implement numerical models and/or modal parameters as a means of condition assessment. While such techniques can often be effective, they may also succumb to their own intrinsic constraints, such as shortcoming in numerical model calibration to dynamic behaviour and environmental sensitivity of modal parameters. Furthermore, the degree of vibration signal non-stationarity that may be induced due to vehicle excitation can limit the applicability of some common signal processing techniques, such as Fourier transforms. The current study investigates vibration-based approaches to damage identification that circumvent some of these issues. Vibration data obtained from a real bridge structure subjected to a progressive damage test under vehicle induced excitation is used as a test subject. Novel vibration parameters obtained from the raw signals are assessed for their damage detection, localisation and quantification capabilities. Additionally, advanced Empirical Mode Decomposition (EMD) and the Hilbert-Huang Transformation (HHT) is applied to the non-stationary signals for the purpose of damage identification. The investigation shows that damage detection, localisation and quantification is achievable from the vehicle induced vibration signals using the proposed empirical techniques.

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"Vibration-based, output-only damage identification of bridge under vehicle induced excitation" presented at CERI2018 by JJ Moughty

  1. 1. Workshop CERI, UCD, Dublin Wednesday 29th August 2018
  2. 2. John Moughty & Joan Ramon Casas Vibration-Based, Output-Only Damage Identification of Bridge under Vehicle Induced Excitation
  3. 3. Introduction Research motivation Test data Novel damage features Application and results Conclusions
  4. 4. Motivation Traditional damage detection methods using modal parameters have drawbacks when applied to short, non- stationary vibration signals: 1. FFT based methods require stationary input data 2. Vehicle-induced vibration signal variation 3. Separation of vehicle dynamics from structure dynamics
  5. 5. Steel Truss Bridge: Progressive Damage Test Steel truss bridge subjected to 4 damage scenarios to central vertical members A 21kN double-axle vehicle with a velocity of 40km/hr was used for structural excitation Vertical acceleration response of vehicle passage was recorded from 8 locations
  6. 6. Novel Damage Features 1. Cumulative Absolute Displacement (CAD) 2. Vibration Envelope Area (VEA) 3. Exponential Decay of Hilbert Envelope 4. Instantaneous Vibration Intensity (IVI)
  7. 7. Novel Damage Features 1. Cumulative Absolute Displacement (CAD) 2. Vibration Envelope Area (VEA) 3. Exponential Decay of Hilbert Envelope 4. Instantaneous Vibration Intensity (IVI)
  8. 8. Novel Damage Features 1. Cumulative Absolute Displacement (CAD) 2. Vibration Envelope Area (VEA) 3. Exponential Decay of Hilbert Envelope 4. Instantaneous Vibration Intensity (IVI)
  9. 9. Novel Damage Features 1. Cumulative Absolute Displacement (CAD) 2. Vibration Envelope Area (VEA) 3. Exponential Decay of Hilbert Envelope Intercept & Decay Constant 4. Instantaneous Vibration Intensity (IVI) from Hilbert-Huang Transform
  10. 10. Vehicle Induced Vibration Response Vehicle Passage Duration Vibration Decay
  11. 11. Vehicle-induced signal variation
  12. 12. Vehicle-induced signal variation – Solution Cumulative Errors
  13. 13. Vehicle-induced signal variation – Solution Cumulative Errors with Threshold Damage Plot
  14. 14. Results of CAD and VEA Damage Identification using CAD Damage Identification using VEA
  15. 15. Exponential Decay of Hilbert Envelope Hilbert Envelope Intercept Hilbert Envelope Decay Constant Hilbert Envelope
  16. 16. Instantaneous Vibration Intensity Non-Stationary Data HHT Instantaneous Frequencies Instantaneous Amplitudes Instantaneous Vibration Intensity Hilbert Spectrum
  17. 17. IVI Damage IdentificationAssessment Sensor 3 Sensor 4 Sensor 5
  18. 18. IVI Damage IdentificationAssessment Sensor 3 Sensor 4 Sensor 5
  19. 19. Conclusions • Output-only, vibration-based parameters assessed attain a significant degree of damage sensitivity and scope of application for non- stationary data, with Hilbert envelope decay parameters and IVI performing best • Vehicle induced signal variation must be accounted for via statistical measurement to obtain a true damage threshold
  20. 20. The TRUSS ITN project (http://trussitn.eu) has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 642453 Thanks for your attention

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