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The Marsh Lane Railway Viaduct: 2 Years of Monitoring with Combined Sensing and Surveying Technologies

  • Sinan AcikgozEmail author
  • Haris Alexakis
  • Cong Ye
  • Andrea Franza
  • Matthew DeJong
Conference paper
Part of the Structural Integrity book series (STIN, volume 11)

Abstract

Marsh Lane viaduct is a typical example of a 19th century brick masonry railway arch in the UK. It frequently carries passenger trains to and from Leeds Station. This paper broadly discusses the sensing techniques and associated analysis procedures used to (i) identify the reasons for existing damage, (ii) quantify their impact on the dynamic response of the structure and (iii) measure degradation of the response over a period of one year. To identify existing damage, distortions in geometry of the structure are examined with new point cloud processing techniques. With the aid of limit analyses, these distortions are interpreted, and past support movements which may have caused the distortions are identified. Then, to measure the dynamic response of the bridge, quasi-distributed fibre optic strain sensing and digital image correlation displacement measurement techniques are used. These highlight the increased dynamic response around locations of existing damage, and point out to the global mechanisms of response that could propagate damage. Continuous fibre optic strain measurements between November 2017 and 2018 are then discussed to investigate the ongoing deterioration.

Keywords

Masonry arch bridges Point clouds Primitive fitting Fibre optic strain sensing Digital image correlation 

Notes

Acknowledgements

This work is being funded by the Lloyd’s Register Foundation, EPSRC (Ref. EP/N021614/1) and Innovate UK (Ref. 920035) The authors are grateful to Network Rail for providing power, technical support and site access for this research.

References

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sinan Acikgoz
    • 1
    Email author
  • Haris Alexakis
    • 2
  • Cong Ye
    • 2
  • Andrea Franza
    • 3
  • Matthew DeJong
    • 4
  1. 1.University of OxfordOxfordUK
  2. 2.University of CambridgeCambridgeUK
  3. 3.Universidad Politécnica de MadridMadridSpain
  4. 4.University of California at BerkeleyBerkeleyUSA

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