Skip to main content

The Effects of an Extended Sensitivity Analysis of Sensor Configurations for Bridge Damage Detection Using Experimental Data

  • Conference paper
  • First Online:
Dynamics of Civil Structures, Volume 2 (SEM 2023)


The damage detection capabilities of sensor setups are essential for any structural health monitoring (SHM) system. In this chapter, the performance of different subsets of sensor configurations selected from a set of 40 accelerometers is evaluated using metrics such as misclassification rate, false positive (FP), and false negative (FN) indications of damage. The subsets of sensor configurations are based on experimental data from a benchmark study that involved capturing the dynamic behavior of a full-scale steel bridge in undamaged and damaged conditions of the bridge. Several iterations with new subsets of decreasing size are generated by the elimination of random sensors. These subsets are then tested using Mahalanobis squared distance (MSD) as the novelty detection algorithm. Additionally, a manual selection of subsets is evaluated, where the sensors located farthest from the damages are eliminated. The results highlight the advantages of a dense sensor network and indicate a complex mechanism behind the damage detection capabilities of sensor networks with a clear trend of inverse proportionality between the sensor set size and FN indications of damage.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions


  1. Olofsson, I., et al.: Assessment of European railway bridges for future traffic demands and longer lives – EC project ‘Sustainable Bridges’. Struct. Infrastruct. Eng. 1(2), 93–100 (2005).

    Article  Google Scholar 

  2. Haghani, R., Al-Emrani, M., Heshmati, M.: Fatigue-prone details in steel bridges. Buildings. 2(4), 456–476 (2012).

    Article  Google Scholar 

  3. Skoglund, O.: Understanding distortion induced fatigue. KTH, Stockholm (2021)

    Google Scholar 

  4. Wenzel, H.: Health Monitoring of Bridges. Wiley, Vienna (2009)

    Book  Google Scholar 

  5. Farrar, C.R., Worden, K.: Structural Health Monitoring: A Machine Learning Perspective. John Wiley & Sons (2012)

    Book  Google Scholar 

  6. Maes, K., Van Meerbeeck, L., Reynders, E., Lombaert, G.: Validation of vibration-based structural health monitoring on retrofitted railway bridge KW51. Mech. Syst. Signal Process. 165(September 2021), 108380 (2022).

    Article  Google Scholar 

  7. Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M., Inman, D.J.: A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications. Mech. Syst. Signal Process. 147, 107077 (2021).

    Article  Google Scholar 

  8. Svendsen, B.T., Frøseth, G.T., Øiseth, O., Rønnquist, A.: A data-based structural health monitoring approach for damage detection in steel bridges using experimental data. J. Civ. Struct. Heal. Monit. 12(1), 101–115 (2022).

    Article  Google Scholar 

  9. Box, G., Jenkins, G., Reinsel, G.C.: Time Series Analysis: Forecasting and Control. John Wiley & Sons, Hoboken (2008)

    Book  MATH  Google Scholar 

  10. Svendsen, B.T., Øiseth, O., Frøseth, G.T., Rønnquist, A.: A hybrid structural health monitoring approach for damage detection in steel bridges under simulated environmental conditions using numerical and experimental data. Struct. Health Monit. 22(1), 540–561 (2023).

    Article  Google Scholar 

  11. Svendsen, B.T., Petersen, Ø.W., Frøseth, G.T., Rønnquist, A.: Improved finite element model updating of a full-scale steel bridge using sensitivity analysis. Struct. Infrastruct. Eng. 19(3), 315–331 (2022).

    Article  Google Scholar 

  12. Figueiredo, E., Figueiras, J., Park, G., Farrar, C.R., Worden, K.: Influence of the autoregressive model order on damage detection. Comput. Civ. Infrastruct. Eng. 26(3), 225–238 (2011).

    Article  Google Scholar 

  13. Azim, M.R., Gül, M.: Damage detection of steel girder railway bridges utilizing operational vibration response. Struct. Control. Health Monit. 26(11), 1–15 (2019).

    Article  Google Scholar 

  14. Worden, K., Manson, G., Fieller, N.R.J.: Damage detection using outlier analysis. J. Sound Vib. 229(3), 647–667 (2000).

    Article  Google Scholar 

Download references


The authors would like to acknowledge the Norwegian Railway Directorate for funding the project.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Gabriel A. del Pozo .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Society for Experimental Mechanics, Inc.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

del Pozo, G.A., Svendsen, B.T., Øiseth, O. (2024). The Effects of an Extended Sensitivity Analysis of Sensor Configurations for Bridge Damage Detection Using Experimental Data. In: Noh, H.Y., Whelan, M., Harvey, P.S. (eds) Dynamics of Civil Structures, Volume 2. SEM 2023. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36662-8

  • Online ISBN: 978-3-031-36663-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics