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Acoustic emission sensing of pipe-soil interaction: Development of an early warning system for buried pipe deformation

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conference contribution
posted on 05.04.2019 by Alister Smith, Ian D. Moore, Neil Dixon
This paper describes a programme of research that aims to develop a continuous, real-time acoustic emission (AE) monitoring system that can be distributed at discrete locations along buried pipelines to sense pipe/soil interaction and provide early warning of adverse behaviour to enable targeted and timely interventions. Pipe/soil interaction-generated AE propagates as guided waves along pipelines. Novel AE interpretation is allowing the evolution of the pipe/soil interaction behaviour to be characterised, and the rate and magnitude of deformation to be quantified. New understanding of AE propagation and attenuation in buried pipes is enabling source localisation methodologies to be developed. Results from normal faulting experiments performed on buried full-scale steel pipes at the buried infrastructure research facility at Queen’s University, Canada, are presented to demonstrate the potential of the AE technique for early detection of buried pipe deformation.

Funding

Alister Smith gratefully acknowledges the support of a UK Engineering and Physical Sciences Research Council Fellowship (Listening to Infrastructure, EP/P012493/1). The testing at Queen’s was supported with funds to Ian Moore through a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada and Infrastructure Operating Funds from the Canada Foundation for Innovation.

History

School

  • Architecture, Building and Civil Engineering

Published in

International Conference on Smart Infrastructure and Construction

Citation

SMITH, A., MOORE, I.D. and DIXON, N., 2019. Acoustic emission sensing of pipe-soil interaction: Development of an early warning system for buried pipe deformation. IN: DeJong, M.J., Schooling, J.M. and Viggiani, G.M.B. (eds). International Conference on Smart Infrastructure and Construction 2019 (ICSIC): Driving data-informed decision-making, Cambridge, UK, 8-10 July 2019, pp.463-468.

Publisher

ICE Publishing

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

29/03/2019

Publication date

2019

Notes

This is an Open Access article. It is published by ICE under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/.

ISBN

9780727764669;9780727764676

Language

en

Location

Cambridge, UK

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