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"Structural health monitoring of bridges: a Bayesian network approach" presented at IALCCE2018

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Between February 2016 and April 2017 seven bridges around the world collapsed suddenly, by causing fatalities and significant economic losses. Structural Health Monitoring (SHM) techniques can assess the health state of an infrastructure by analysing its behaviour, and point out degraded elements that require to be maintained. As a consequence, SHM methods can improve the safety of the transportation network, and reduce the life cycle costs of the infrastructure, by optimizing the maintenance schedule. In this paper, a Bayesian Belief Network (BBN) approach is proposed to assess the health state of a beam-and-slab bridge, by relying on the analysis of its vertical acceleration. A procedure to robustly define the Conditional Probabilities Tables (CPTs) of the BBN is proposed, by merging expert judgement with information about the bridge behaviour. The BBN allows updating the health state of the bridge when a new measurement of the bridge acceleration is available, by taking account of the health state of each element of the bridge. In this way, the degradation level of the whole bridge and of its elements is monitored over time, and unexpected behaviour of the bridge are diagnosed.

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"Structural health monitoring of bridges: a Bayesian network approach" presented at IALCCE2018

  1. 1. Reducing Uncertainty in Structural Safety Special Session SS6 Ghent, Belgium 28-31 October 2018
  2. 2. Structural health monitoring of bridges: a Bayesian network approach Matteo Vagnoli, Rasa Remenyte-Prescott, John Andrews
  3. 3. Outline • Where we are today • Bridge condition monitoring and damage detection using Bayesian Belief Network • A case study
  4. 4. 17 February 2016, Gudbrandsdalslågen (Norway) Bridge failures 1 year old!!!
  5. 5. Bridge failures 2 August 2016, Leicestershire (UK)
  6. 6. Bridge failures 19 August 2016, Pitrufquén (Chile)
  7. 7. Bridge failures 7 September 2016, Dimbokro (Ivory Coast)
  8. 8. Bridge failures 28 October 2016, Milan (Italy) 9 March 2017, Ancona (Italy) 18 April 2017, Fossano, Cuneo (Italy)
  9. 9. Bridge failures 6 February 2018, Brasilia (Brazil)
  10. 10. Bridge failures 14 August 2018, Genoa (Italy)
  11. 11. Why does this keep happening?
  12. 12. 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.
  13. 13. 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
  14. 14. To continuously monitor and the condition of the bridge and diagnose its unexpected behaviour by assessing the health state of each bridge element and the whole bridge simultaneously, without requiring complex and time-consuming process of analysis. Measurements processing Bridge element 1 Bridge element 2 Bridge element 3 Bridge element 4 Bridge health state Measurements processing Measurements processing Measurements processing Bayesian Belief Network approach
  15. 15. Bayesian Belief Network definition and use BBN allows to merge expert knowledge of the engineers, with the analysis of the measurement of the bridge behaviour Conditional Probability Tables investigate the relationship between different bridge elements
  16. 16. A new method for defining Conditional Probability Tables (CPTs) The CPTs of the BBN are defined by merging the expert knowledge with the analysis of the bridge behaviour during different health states of the bridge Probability of a that a bridge element is degraded Probability assessed by analyzing a database of bridge behavior Probability assessed by interviewing bridge experts using what-if scenarios
  17. 17. A new method for defining Conditional Probability Tables (CPTs) Probability of a that a bridge element is degraded Probability assessed by analyzing a database of bridge behavior Probability assessed by interviewing bridge experts using what-if scenarios Alpha is a weighting factor defined through a sensitivity analysis
  18. 18. The Conditional Probability Tables (CPTs) Expert knowledge elicitation process Bridge engineers and professors of structural engineering are interviewed by the means of a online surveys, in order to assess the influence of a bridge element on the health state of the other bridge elements.
  19. 19. The Conditional Probability Tables (CPTs) Expert knowledge elicitation process Bridge engineers and professors of structural engineering are interviewed by the means of a online surveys, in order to assess the influence of a bridge element on the health state of the other bridge elements. Fuzzy Analytic Hierarchy process CPTs value Expert judgments
  20. 20. The Conditional Probability Tables (CPTs) Historical bridge behaviour analysis The influences between different elements of the bridge are assessed by reconstructing the probability density function of a Damage Index (DI), that assesses the health state of the bridge elements when the degraded element of the bridge is known.
  21. 21. The Conditional Probability Tables (CPTs) Historical bridge behaviour analysis The influences between different elements of the bridge are assessed by reconstructing the probability density function of a Damage Index (DI), that assesses the health state of the bridge elements when the degraded element of the bridge is known. Historical/simulated behaviour of the bridge Vibration analysis of the bridge behaviour Influence between different bridge elements Reconstruction of pdf using Finite Mixture models
  22. 22. Bayesian Belief Network approach When the BBN structure is developed and the CPTs are defined, the BBN is used to monitor in real time the bridge health state
  23. 23. A case study • A FEM of a simply supported beam-and-slab bridge is considered. The bridge is 20 m long and 10 m wide structure • The passage of a truck over the bridge at constant speed is considered • The speed of the truck is unknown • 9 accelerometers are used to monitor the bridge behaviour • 16 unknown damage scenarios of the bridge are evaluated
  24. 24. BBN model Each beam of the bridge (10 longitudinal beams) is considered as major element (20 m long). Each longitudinal beam is divided into 5 smaller elements 4 m long (e.g. beam 1 and 2, and their smaller elements). The BBN allows to assess interdependencies between different elements. The health state of the whole bridge is assessed by taking account of the health state of each bridge element
  25. 25. CPT definition - Historical bridge behaviour analysis A Damage Index (DI) is introduced to evaluate the bridge acceleration The larger the loss of stiffness of the bridge, the higher the value of the damage index Number of acceleration crossings per unit time Bridge vibration energy
  26. 26. CPT definition - Historical bridge behaviour analysis Finite Mixture Models (FMM) are used to reconstruct the pdf of the DI FMM Pdf of the influence of degradation of lane 2 on the health state of lane 1 The reconstructed pdf is made of two Gaussian distributions: • one with mean 1.91 (partial degradation of lane1) • another with mean 6.99 (severe degradation of lane 1) 1.91 6.99
  27. 27. CPT definition – Merging expert and data analysis The proposed method allows to update the CPTs based on the actual health state of the bridge elements Expert based CPT Pdf based CPTs CPTs based on the proposed method, merging expert and data analysis
  28. 28. Damage detection and diagnostic
  29. 29. Damage detection and diagnostic Accuracy of the proposed BBN method is 75% 12 out of 16 degraded scenarios are correctly detected and diagnosed
  30. 30. • There is a need for real-time remote structural health monitoring and fault detection by overcoming the limitations of traditional manual bridge SHM. • The influence of each single bridge element should be considered on the assessment of the bridge health state. We propose a BBN-based SHM method to monitor the evolution of a bridge health state Pros: • Each bridge element influences the health state of the whole bridge. • The health state of the bridge and its elements is updated each time when new evidence of the bridge behaviour is available • The expert knowledge is merged with the analysis of the bridge behavior. • The definition of the CPTs can be time-consuming, by requiring data or interviews. • The accuracy of the BBN in diagnosing unexpected behaviour can depend on the number of nodes of the BBN. • The measurement of the bridge behaviour needs to be pre-processed when an in-field bridge is analysed. Cons:
  31. 31. 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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