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dc.contributor.authorCocking, Sam
dc.description.abstractNovel sensing technologies – for instance, fibre-optic Fibre Bragg Grating (FBG) sensors – offer engineers new means to study the dynamic distributed behaviour of complex structures, such as masonry arch bridges. These bridges, which are common features of many European transport networks, are often ageing structures, with complex histories of damage and repair work. Furthermore, their material properties and non-visible geometry may be challenging, if not impossible, to ascertain, creating uncertainty in the reliability of computational analyses. Particularly in light of the financial and environmental costs of their replacement, and the contributions that many of these bridges make to our shared architectural and industrial heritage, it is increasingly urgent to improve our understanding of the structural behaviour of masonry arch bridges under their real working loads. As these bridges undergo many cycles of loading, which may directly drive their deterioration, their response in working conditions is often more important than an understanding of their collapse behaviour, which can be studied using established methods such as limit state analysis. In this thesis, Structural Health Monitoring is used to gain new insights into the behaviour of masonry arch bridges and viaducts. Part 1 includes an initial study in which the outputs of simplified finite element models are compared against previously gathered FBG monitoring data, describing the response of a damaged masonry viaduct under applied train loading. It is found that the impact of damage on the viaduct response is primarily local, while common ‘single-point’ measurements, such as the crown vertical displacements, can be well matched by the simplified models. Furthermore, relative contributions from each of the main structural components of the viaduct, towards its overall SLS performance, are quantified. The remainder of the thesis is concerned with the monitoring of a case study structure: a recently repaired, skewed, masonry arch railway bridge. FBGs are used to monitor the dynamic behaviour of this bridge in detail – in particular, the in-plane strain response and the movements across cracks, including both opening and shearing movements across a key separation crack between the arch barrel and a spandrel wall. The sensitivity of these responses to a range of external variables – namely train speed, ambient temperature, time of day, and date – is presented. Trains travelling between approximately 80 and 90 mph are observed to induce a local strain response that is up to 15% higher than that of slower trains, although this effect is only present close to the arch crown, where loads are most concentrated at the extrados. The trend with time-of-day follows anticipated passenger behaviour. Trains at peak commute times result in higher magnitude and lower statistical spread in the bridge response; hence, comparing trains recorded at these times allows for variable passenger loading to be normalised. The in-plane flow of force throughout the skewed arch is also measured and visualised, using a novel ‘FBG strain rosette’ implementation that monitors the arch principal strains. These force flow distributions, experimentally mapped in detail for the first time for a skewed masonry arch, are highly consistent across many train events, suggesting a common response to different types of train loading. Separately, videogrammetry is used to measure the out-of-plane, vertical displacements of the arch. The distribution of these movements is used to fit a simplified beam model of the transverse bending component of the arch response. This structural monitoring is part of a broader, collaborative study, trialling a large range of different monitoring technologies at the case study bridge. A practical evaluation of these various technologies is presented in this thesis; despite the larger initial costs for an FBG system, the many measurement locations and high quality of data afforded by this technology meant that it compared favourably with more established approaches. In another collaboration, existing laser scan analysis methods for masonry arch bridges have been extended to skewed arches, and employed to study the historic, distributed deformation of this bridge. It is shown that the current deformed geometry could have been caused by small support movements at the obtuse corners of the arch abutments, which are consistent with past hypotheses in the literature regarding the behaviour of skewed masonry arch bridges. Much of this thesis is concerned with the analysis and interpretation of data collected through on-site monitoring of the case study bridge, carried out over a six-month period. Since this time, the FBG system at the bridge has been adapted for autonomous, remote sensing. The necessary improvements to the system are presented, covering both the equipment installed at the bridge and the accompanying data processing strategies, which now allow for automated data categorisation, analysis, and visualisation. Following this, the long-term data gathered to date is analysed. These data reveal further trends – in particular, linking the bridge response to ambient temperature. Lower temperatures lead to a larger magnitude response – potentially due to thermal contraction of the masonry causing existing cracks throughout the bridge to open, and thus increasing the potential for movements to occur under applied loads.
dc.description.sponsorshipI would like to acknowledge additional funding from Network Rail, and in kind support from AECOM, which supported the field monitoring components of my PhD research.
dc.rightsAll Rights Reserved
dc.subjectstructural engineering
dc.subjectcivil engineering
dc.subjectmasonry arch bridge
dc.subjecthistoric masonry structures
dc.subjectskewed arch
dc.subjectrailway bridge
dc.subjectageing infrastructure
dc.subjectstructural health monitoring
dc.subjectfibre optic sensing
dc.subjectFibre Bragg Gratings
dc.subjectdynamic distributed strain monitoring
dc.subjectlaser scan analysis
dc.titleDynamic distributed monitoring of masonry railway bridges
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.publisher.institutionUniversity of Cambridge
dc.contributor.orcidCocking, Sam [0000-0003-2782-3464]
dc.type.qualificationtitlePhD in Engineering
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/I019308/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/K000314/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/L010917/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/N021614/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/P013848/1)
pubs.funder-project-idEPSRC (1730520)
cam.supervisorDeJong, Matthew
cam.supervisorTalbot, James
cam.supervisor.orcidTalbot, James [0000-0002-0036-9727]

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