Minimal information data-modelling (MID) and an easily implementable low-cost SHM system for use on a short-span bridge

Connor O'Higgins, David Hester*, Patrick McGetrick, Elizabeth J. Cross, Wai Kei Ao, James Brownjohn

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Structural Health Monitoring (SHM) is a technique that involves gathering information to ensure that a structure is safe and behaving as expected. Within SHM, vibration-based monitoring is generally seen as one of the more cost-effective types of monitoring. However, vibration-based monitoring has mostly been undertaken on long-span bridges using data collected with a dense network of sensors. Historically, the logistical difficulty of collecting data on short- and medium-span bridges has meant that the usefulness of vibration-based methods on these bridges is largely unknown. Therefore, this study proposes Minimal Information Data-modelling (MID). MID is an approach that utilises low-cost, easily implementable sensors that are potentially feasible for operators to purchase and operate across a network. This approach will be investigated to determine whether MID is a feasible approach for monitoring short- and medium- span bridges. The results from MID were assessed to determine whether they could detect a suitably small shift in frequency, which is indicative of damage. It was determined that the data models could reliably detect frequency shifts as low as 0.01 Hz. This magnitude of frequency shift is similar to the level of frequency shift reported for a range of bridge damage cases found by others and validated with FE models. The accuracy achieved by the data models indicates that MID could potentially be used as a damage detection method. The cost of the equipment used to collect the data was approximately £370, demonstrating that it is feasible to use MID to monitor bridges across an entire network.

Original languageEnglish
Article number6328
Number of pages27
Issue number14
Publication statusPublished - 12 Jul 2023


  • long-term bridge monitoring
  • data modelling
  • structural health monitoring
  • low-cost
  • regression
  • environmental effects


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