Introducing a data-driven framework for spatiotemporal slope stability analytics for failure estimation

Authors: Tordesillas, A; Kahagalage, S; Campbell, L; Bellett, P; Batterham, R Download Paper

DOI https://doi.org/10.36487/ACG_repo/2135_14

Cite As:
Tordesillas, A, Kahagalage, S, Campbell, L, Bellett, P & Batterham, R 2021, 'Introducing a data-driven framework for spatiotemporal slope stability analytics for failure estimation', in PM Dight (ed.), SSIM 2021: Second International Slope Stability in Mining, Australian Centre for Geomechanics, Perth, pp. 235-246, https://doi.org/10.36487/ACG_repo/2135_14



Abstract:
In this paper, we present spatiotemporal slope stability analytics for failure estimation (SSSAFE), a deterministic, data-driven model of force transmission in a rock slope. Its input solely comprises the spatiotemporal surface deformation of the slope, here gathered from slope stability radar (SSR). The model combines recent advances from data analytics, granular media physics and mechanics, and slope stability monitoring. SSSAFE is unique in its explicit connections to the underlying physics of strength and failure in the precursory failure regime (PFR) of granular systems. Distinct from the single pixel selection for time of failure methods, this model exploits all the kinematic information available on the entire monitoring domain to quantitatively track the coupled evolution of the preferred transmission pathways for force and energy (socalled force chains) and the preferential crack paths. This coupled evolution gives rise to a force bottleneck, which comprises vulnerable and congested sites closest to breaking point (fracture). The force bottleneck is an emergent structure that is not static. Prior studies have shown that the spatiotemporal dynamics of this bottleneck holds clues to the ultimate location and timing of failure. Initially, in the early stages of PFR, the bottleneck continually shifts in location in the rock body. This process is due to the inherent redundancies in the force pathways in the rock mass. Such redundant paths enable stresses to be redistributed and diverted away from the pre-existing bottleneck to another location where a new bottleneck may then form. However, as damage spreads, and the time of failure draws near, a tipping point is reached when all the redundant paths have been exhausted and no further stress reroutes are possible. At this point, a recurring bottleneck, invariant in space and time, emerges along which previously disconnected cracks begin to coalesce. Simultaneously, this process leads to a persistent kinematic clustering pattern, as the active region begins to detach from the rest of the slope and accelerate. That is, the closer it is to the time of failure, the more the kinematic clusters (the two groups of monitoring points on either side of the bottleneck) move such that intra-cluster motions become increasingly similar while inter-cluster motions become increasingly different. Here we demonstrate how to extract, quantify, and exploit this particular form of spatiotemporal dynamics from SSR data for two distinct open pit mine slopes, for the purposes of early prediction of failure of the geometry, location, and time of collapse.

Keywords: slope stability analytics, force bottlenecks, spatiotemporal dynamics, force chains

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