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

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Acikgoz, S 
Alexakis, H 
Franza, A 
DeJong, M 

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.

Description

Keywords

4005 Civil Engineering, 3403 Macromolecular and Materials Chemistry, 40 Engineering, 34 Chemical Sciences

Journal Title

Proceedings of ARCH 2019

Conference Name

9th International Conference on Arch Bridges (ARCH 2019)

Journal ISSN

2522-560X
2522-5618

Volume Title

11

Publisher

Springer International Publishing
Sponsorship
Technology Strategy Board (920035)
Engineering and Physical Sciences Research Council (EP/N021614/1)
Engineering and Physical Sciences Research Council (EP/K000314/1)
Engineering and Physical Sciences Research Council (EP/L010917/1)
Engineering and Physical Sciences Research Council (EP/I019308/1)
Engineering and Physical Sciences Research Council (EP/P013848/1)
This work is being funded by the Lloyd’s Register Foundation, EPSRC and Innovate UK through the Data-Centric Engineering programme of the Alan Turing Institute and through the Cambridge Centre for Smart Infrastructure and Construction. Funding for the monitoring installation was provided by EPSRC under the Ref. EP/N021614/1 grant and by Innovate UK under the Ref. 920035 grant.