Spatiotemporal slope stability analytics for failure estimation (SSSAFE): linking radar data to the fundamental dynamics of granular failure

Abstract Impending catastrophic failure of granular earth slopes manifests distinct kinematic patterns in space and time. While risk assessments of slope failure hazards have routinely relied on the monitoring of ground motion, such precursory failure patterns remain poorly understood. A key challen...

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Autores principales:	Antoinette Tordesillas, Sanath Kahagalage, Lachlan Campbell, Pat Bellett, Emanuele Intrieri, Robin Batterham
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0eb7dd6d39964841a0b1fcb5d38442fb
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spelling	oai:doaj.org-article:0eb7dd6d39964841a0b1fcb5d38442fb2021-12-02T14:29:15ZSpatiotemporal slope stability analytics for failure estimation (SSSAFE): linking radar data to the fundamental dynamics of granular failure10.1038/s41598-021-88836-x2045-2322https://doaj.org/article/0eb7dd6d39964841a0b1fcb5d38442fb2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88836-xhttps://doaj.org/toc/2045-2322Abstract Impending catastrophic failure of granular earth slopes manifests distinct kinematic patterns in space and time. While risk assessments of slope failure hazards have routinely relied on the monitoring of ground motion, such precursory failure patterns remain poorly understood. A key challenge is the multiplicity of spatiotemporal scales and dynamical regimes. In particular, there exist a precursory failure regime where two mesoscale mechanisms coevolve, namely, the preferred transmission paths for force and damage. Despite extensive studies, a formulation which can address their coevolution not just in laboratory tests but also in large, uncontrolled field environments has proved elusive. Here we address this problem by developing a slope stability analytics framework which uses network flow theory and mesoscience to model this coevolution and predict emergent kinematic clusters solely from surface ground motion data. We test this framework on four data sets: one at the laboratory scale using individual grain displacement data; three at the field scale using line-of-sight displacement of a slope surface, from ground-based radar in two mines and from space-borne radar for the 2017 Xinmo landslide. The dynamics of the kinematic clusters deliver an early prediction of the geometry, location and time of failure.Antoinette TordesillasSanath KahagalageLachlan CampbellPat BellettEmanuele IntrieriRobin BatterhamNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Antoinette Tordesillas Sanath Kahagalage Lachlan Campbell Pat Bellett Emanuele Intrieri Robin Batterham Spatiotemporal slope stability analytics for failure estimation (SSSAFE): linking radar data to the fundamental dynamics of granular failure
description	Abstract Impending catastrophic failure of granular earth slopes manifests distinct kinematic patterns in space and time. While risk assessments of slope failure hazards have routinely relied on the monitoring of ground motion, such precursory failure patterns remain poorly understood. A key challenge is the multiplicity of spatiotemporal scales and dynamical regimes. In particular, there exist a precursory failure regime where two mesoscale mechanisms coevolve, namely, the preferred transmission paths for force and damage. Despite extensive studies, a formulation which can address their coevolution not just in laboratory tests but also in large, uncontrolled field environments has proved elusive. Here we address this problem by developing a slope stability analytics framework which uses network flow theory and mesoscience to model this coevolution and predict emergent kinematic clusters solely from surface ground motion data. We test this framework on four data sets: one at the laboratory scale using individual grain displacement data; three at the field scale using line-of-sight displacement of a slope surface, from ground-based radar in two mines and from space-borne radar for the 2017 Xinmo landslide. The dynamics of the kinematic clusters deliver an early prediction of the geometry, location and time of failure.
format	article
author	Antoinette Tordesillas Sanath Kahagalage Lachlan Campbell Pat Bellett Emanuele Intrieri Robin Batterham
author_facet	Antoinette Tordesillas Sanath Kahagalage Lachlan Campbell Pat Bellett Emanuele Intrieri Robin Batterham
author_sort	Antoinette Tordesillas
title	Spatiotemporal slope stability analytics for failure estimation (SSSAFE): linking radar data to the fundamental dynamics of granular failure
title_short	Spatiotemporal slope stability analytics for failure estimation (SSSAFE): linking radar data to the fundamental dynamics of granular failure
title_full	Spatiotemporal slope stability analytics for failure estimation (SSSAFE): linking radar data to the fundamental dynamics of granular failure
title_fullStr	Spatiotemporal slope stability analytics for failure estimation (SSSAFE): linking radar data to the fundamental dynamics of granular failure
title_full_unstemmed	Spatiotemporal slope stability analytics for failure estimation (SSSAFE): linking radar data to the fundamental dynamics of granular failure
title_sort	spatiotemporal slope stability analytics for failure estimation (sssafe): linking radar data to the fundamental dynamics of granular failure
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0eb7dd6d39964841a0b1fcb5d38442fb
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Spatiotemporal slope stability analytics for failure estimation (SSSAFE): linking radar data to the fundamental dynamics of granular failure

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