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"A machine learning classifier for condition monitoring and damage detection of bridge infrastructure" presented at CERI2018 by Matteo Vagnoli

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Structural Health Monitoring (SHM) techniques are able to monitor the behaviour of critical infrastructure over time, by improving the safety and reliability of the asset. A large amount of data is generated by SHM methods continuously. Therefore, machine learning methods can be developed in order to transform the available data into valuable information for decision makers, by pointing out vulnerabilities of the critical infrastructure. In this paper, a machine learning classifier for condition monitoring and damage detection of bridges is proposed by adopting a Neuro-Fuzzy algorithm. The method allows to assess the health state of the infrastructure automatically, accurately and rapidly, every time when a new measurement of the bridge behaviour is available. The method is validated and tested by monitoring the behaviour of an in-field steel truss bridge, which is subjected to a progressive damage process.

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"A machine learning classifier for condition monitoring and damage detection of bridge infrastructure" presented at CERI2018 by Matteo Vagnoli

  1. 1. Workshop CERI, UCD, Dublin Wednesday 29th August 2018
  2. 2. Matteo Vagnoli, Rasa Remenyte-Prescott, John Andrews A machine learning classifier for condition monitoring and damage detection of bridge infrastructure
  3. 3. Outline • Where we are today • Bridge condition monitoring and damage detection using machine learning • A case study
  4. 4. Bridge failures 17 February 2016, Gudbrandsdalslågen (Norway) Bridge failures
  5. 5. Bridge failures 17 February 2016, Gudbrandsdalslågen (Norway) 1 year old!!! Bridge failures
  6. 6. Bridge failuresBridge failures 2 August 2016, Leicestershire (UK)
  7. 7. Bridge failuresBridge failures 19 August 2016, Pitrufquén (Chile)
  8. 8. Bridge failuresBridge failures 7 September 2016, Dimbokro (Ivory Coast)
  9. 9. Bridge failuresBridge failures 28 October 2016, Milan (Italy)
  10. 10. Bridge failuresBridge failures 28 October 2016, Milan (Italy) 9 March 2017, Ancona (Italy)
  11. 11. Bridge failuresBridge failures 28 October 2016, Milan (Italy) 9 March 2017, Ancona (Italy) 18 April 2017, Fossano, Cuneo (Italy)
  12. 12. Bridge failuresBridge failures 6 February 2018, Brasilia (Brazil)
  13. 13. Bridge failuresBridge failures 14 August 2018, Genoa (Italy)
  14. 14. Bridge failures Why does this keep happening?
  15. 15. Bridge condition monitoring today Bridge condition assessment and damage detection strategies are usually carried out by subjective visual inspection, at intervals of one to six years.
  16. 16. Bridge condition monitoring today Bridge condition assessment and damage detection strategies are usually carried out by subjective visual inspection, at intervals of one to six years. More than 35% of the over 1 million bridges across Europe are over 100 years old. European Commission, EU transport in figures, statistical pocketbook, 2012.
  17. 17. Bridge condition monitoring today Bridge condition assessment and damage detection strategies are usually carried out by subjective visual inspection, at intervals of one to six years. Deterioration processes may lead to a lower safety level and, potentially, to catastrophic events. More than 35% of the over 1 million bridges across Europe are over 100 years old. European Commission, EU transport in figures, statistical pocketbook, 2012.
  18. 18. Bridge condition monitoring today Bridge condition assessment and damage detection strategies are usually carried out by subjective visual inspection, at intervals of one to six years. Deterioration processes may lead to a lower safety level and, potentially, to catastrophic events. How can we change the way we approach this problem? More than 35% of the over 1 million bridges across Europe are over 100 years old. European Commission, EU transport in figures, statistical pocketbook, 2012.
  19. 19. Bridge condition monitoring tomorrow Real-time measurement system
  20. 20. Bridge condition monitoring tomorrow Real-time measurement system ✓Remote structural health monitoring and damage detection ✓Overcoming of the visual inspection limitations ✓Assessment of the health state of the whole bridge by analysing the bridge behaviour ✓Maintenance can be scheduled based on the real health state of the bridge
  21. 21. Bridge condition monitoring tomorrow Real-time measurement system ✓Remote structural health monitoring and damage detection ✓Overcoming of the visual inspection limitations ✓Assessment of the health state of the whole bridge by analysing the bridge behaviour ✓Maintenance can be scheduled based on the real health state of the bridge
  22. 22. A Method for bridge condition monitoring Identification of the bridge free-vibration Assessment of 22 features over time Assessment of feature trend (using EMD) Identification of the bridge health state using a Neuro-Fuzzy classifier Raw data from sensors
  23. 23. A Method for bridge condition monitoring Identification of the bridge free-vibration Assessment of 22 features over time Assessment of feature trend (using EMD) Identification of the bridge health state using a Neuro-Fuzzy classifier Raw data from sensors
  24. 24. A Method for bridge condition monitoring Identification of the bridge free-vibration Assessment of 22 features over time Assessment of feature trend (using EMD) Identification of the bridge health state using a Neuro-Fuzzy classifier Raw data from sensors
  25. 25. A case study: steel truss bridge 8@7400= 59200 mm P1 P2 A1 A2 A3 A4 A5 A6 A7 A8 DMG1 DMG2 DMG3 Passing direction Ai: Accelerometer No. i (Vert.) DMGi: damage scenario i Pi: Pier No.i
  26. 26. Raw acceleration of the bridge Identification of the bridge free-vibration Assessment of 22 features over time Assessment of feature trend (using EMD) Identification of the bridge health state using a Neuro-Fuzzy classifier Raw data from sensors
  27. 27. Bridge free-vibration identification Identification of the bridge free-vibration Assessment of 22 features over time Assessment of feature trend (using EMD) Identification of the bridge health state using a Neuro-Fuzzy classifier Raw data from sensors Free vibration
  28. 28. Features assessment Identification of the bridge free-vibration Assessment of 22 features over time Assessment of feature trend (using EMD) Identification of the bridge health state using a Neuro-Fuzzy classifier Raw data from sensors
  29. 29. Trend of the feature using the EMD method Identification of the bridge free-vibration Assessment of 22 features over time Assessment of feature trend (using EMD) Identification of the bridge health state using a Neuro-Fuzzy classifier Raw data from sensors
  30. 30. Automatic classification of the bridge health state Identification of the bridge free-vibration Assessment of 22 features over time Assessment of feature trend (using EMD) Identification of the bridge health state using a Neuro-Fuzzy classifier Raw data from sensors
  31. 31. Automatic classification of the bridge health state Identification of the bridge free-vibration Assessment of 22 features over time Assessment of feature trend (using EMD) Identification of the bridge health state using a Neuro-Fuzzy classifier Raw data from sensors The health state of the bridge has been correctly identified for 6 scenarios out of 6. The nature of the damage for 3 scenarios out of 4. The proposed method allows to automatically monitor and assess the health state of the bridge
  32. 32. Automatic classification of the bridge health state Identification of the bridge free-vibration Assessment of 22 features over time Assessment of feature trend (using EMD) Identification of the bridge health state using a Neuro-Fuzzy classifier Raw data from sensors The health state of the bridge has been correctly identified for 6 scenarios out of 6. The nature of the damage for 3 scenarios out of 4. The proposed method allows to automatically monitor and assess the health state of the bridge The proposed method has been verified on more challenging in-field bridges and very good results have been obtained
  33. 33. Automatic classification of the bridge health state Identification of the bridge free-vibration Assessment of 22 features over time Assessment of feature trend (using EMD) Identification of the bridge health state using a Neuro-Fuzzy classifier Raw data from sensors The health state of the bridge has been correctly identified for 6 scenarios out of 6. The nature of the damage for 3 scenarios out of 4. The proposed method allows to automatically monitor and assess the health state of the bridge The Neuro-Fuzzy requires a database of bridge behaviour for the training process The proposed method has been verified on more challenging in-field bridges and very good results have been obtained
  34. 34. Conclusion Improve safety, reliability and performance of the transportation network Improve the maintenance and renewals activities of the assets by optimizing their budget How can we achieve these objectives? Real-time automatic condition monitoring?
  35. 35. 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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