Deterioration model and condition monitoring of aged railway embankment using non-invasive geophysics

Gunn, D.A.; Chambers, J.E.; Dashwood, B.E.; Lacinska, A.; Dijkstra, T.; Uhlemann, S.; Swift, R.; Kirkham, M.; Milodowski, A.; Wragg, J.; Donohue, S.. 2018 Deterioration model and condition monitoring of aged railway embankment using non-invasive geophysics. Construction and Building Materials, 170. 668-678. 10.1016/j.conbuildmat.2018.03.066

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Official URL: http://dx.doi.org/10.1016/j.conbuildmat.2018.03.06...

Abstract/Summary

Effective management of railway infrastructure is becoming increasingly reliant upon remote condition monitoring of geotechnical asset condition. Current monitoring approaches focus on confirmation of the morphological effects caused by subsurface processes driving deterioration. However, geophysical imaging offers new opportunities for ‘predict and prevent’ practices, providing access to monitoring internal property change patterns preceding these morphological responses. Geophysical methods utilize disturbances that propagate through and holistically sample earthworks and are especially suited to imaging the unique heterogeneity of aged embankments. In this case study, surface wave seismic surveys are interpreted to construct a stiffness ground model consistent with a heterogeneous embankment comprising local borrow materials. Time-lapse electrical resistivity imaging was also used to investigate and visualise ground water ingress and movement within this ground model. Ground water movement was shown to be highly dynamic, responding very quickly to local storm events with infiltration into the embankment within hours. Subsequent wetting and drying cycles throughout the embankment’s lifespan have caused the dissolution, mobilisation and re-precipitation of soluble minerals within the fill materials. This process has driven the deterioration of the fill fabric, which is evidenced in thin sections by voids and localised rupture about in situ mineral growths. Finally, we provide a framework showing how geophysical methods could support more risk-based asset management practices of the future.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1016/j.conbuildmat.2018.03.066
ISSN:	09500618
Date made live:	08 Oct 2018 12:02 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/521133

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1016/j.conbuildmat.2018.03.066

ISSN:

09500618

Date made live:

08 Oct 2018 12:02 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/521133