Computational Technology Resources - CCC

R. Tarnus, T. Bardainne, L. Michel, N. Deladerrière, C. Cai, "A case study for railway underground imaging using trains as seismic signal for sinkhole and subsidence phenomena prevention", in J. Pombo, (Editor), "Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 1, Paper 10.13, 2022, doi:10.4203/ccc.1.10.13

Keywords: sinkhole, seismic, imaging, masw.

Abstract

Railway tracks are particularly exposed to the danger represented by sinkholes, a type of naturally forming underground cavity. Preventing the potentially severe outcomes through early detection represents a challenge for underground imaging techniques. We present here a method derived from seismic interferometry combined with a Multichannel Analysis of Surface Waves (MASW) approach. Technical aspects include using the energy from regular train traffic as a powerful and broadband signal source, recorded using a linear network of autonomous sensor nodes. In addition to lightweight deployment, this allows for more reliable and stable results due to the high energy transmitted into the ground by passing trains. Final results are provided in the form of surface waves velocity cross-sections of the ground, where decompressed terrains associated with cavities are identified as low-velocity anomalies. To illustrate the potential of this technique, a case study is presented here from a survey conducted on a railway section having experienced a severe collapsing phenomenon, due to a sinkhole cavity.

download the full-text of this paper (PDF, 6 pages, 403 Kb)

go to the previous paper
go to the next paper
return to the table of contents
return to the volume description

	Computational & Technology Resources an online resource for computational, engineering & technology publications
	not logged in - login
Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 1 PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 10.13 A case study for railway underground imaging using trains as seismic signal for sinkhole and subsidence phenomena prevention R. Tarnus, T. Bardainne, L. Michel, N. Deladerrière and C. Cai Sercel SAS, Carquefou, France doi:10.4203/ccc.1.10.13 Full Bibliographic Reference for this paper R. Tarnus, T. Bardainne, L. Michel, N. Deladerrière, C. Cai, "A case study for railway underground imaging using trains as seismic signal for sinkhole and subsidence phenomena prevention", in J. Pombo, (Editor), "Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 1, Paper 10.13, 2022, doi:10.4203/ccc.1.10.13 Keywords: sinkhole, seismic, imaging, masw. Abstract Railway tracks are particularly exposed to the danger represented by sinkholes, a type of naturally forming underground cavity. Preventing the potentially severe outcomes through early detection represents a challenge for underground imaging techniques. We present here a method derived from seismic interferometry combined with a Multichannel Analysis of Surface Waves (MASW) approach. Technical aspects include using the energy from regular train traffic as a powerful and broadband signal source, recorded using a linear network of autonomous sensor nodes. In addition to lightweight deployment, this allows for more reliable and stable results due to the high energy transmitted into the ground by passing trains. Final results are provided in the form of surface waves velocity cross-sections of the ground, where decompressed terrains associated with cavities are identified as low-velocity anomalies. To illustrate the potential of this technique, a case study is presented here from a survey conducted on a railway section having experienced a severe collapsing phenomenon, due to a sinkhole cavity. download the full-text of this paper (PDF, 6 pages, 403 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
Back to top	©Civil-Comp Limited 2023 - terms & conditions