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
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
Ahuja, RK, Magnanti, TL & Orlin, JB 1993, Network Flows: Theory, Algorithms, and Applications, Prentice Hall, Hoboken.
Carlà, T, Intrieri, E, Raspini, F, Bardi, F, Farina, P, Ferretti, A, Colombo, D, Novali, F & Casagli, N 2019, ‘Perspectives on the prediction of catastrophic slope failures from satellite INSAR’, Scientific Reports, vol. 9, no. 1, pp. 1–9.
Carlà, T, Intrieri, E, Di Traglia, F, Nolesini, T, Gigli, G & Casagli, N 2016, ‘Guidelines on the use of inverse velocity method as a tool for setting alarm thresholds and forecasting landslides and structure collapses’, Landslides, vol. 14, no. 2, pp. 517–534.
Cruden, D & Varnes, D 1996, ‘Landslide types and processes’, Special Report - National Research Council, Transportation Research Board, U.S. National Academy of Sciences, vol. 247, pp. 36–57.
Das, S & Tordesillas, A 2019, ‘Near real-time characterization of spatio-temporal precursory evolution of a rockslide from radar data: Integrating statistical and machine learning with dynamics of granular failure’, Remote Sensing, vol. 11, no. 23, p. 2777.
Dick, GJ, Eberhardt, E, Cabrejo-Liévano, AG, Stead, D & Rose, ND 2015, ‘Development of an early-warning time-of-failure analysis methodology for open-pit mine slopes utilizing ground-based slope stability radar monitoring data’, Canadian Geotechnical Journal, vol. 52, no. 4, pp. 515–529.
Fukuzono, T 1985, ‘A new method for predicting the failure time of a slope’, Proceedings of 4th International Conference and Field Workshop on Landslide, pp. 145–150.
Harries, N, Noon, D & Rowley, K 2006, ‘Case studies of slope stability radar used in open cut mines’, Stability of Rock Slopes in Open Pit Mining and Civil Engineering Situations, The South African Institute of Mining and Metallurgy, Johannesburg, pp. 335–342.
Intrieri, E, Carlà, T & Gigli, G 2019, ‘Forecasting the time of failure of landslides at slope-scale: A literature review’, Earth-Science Reviews, vol. 193, pp. 333–349.
Kahagalage, S 2020, A Study of Optimised Network Flows for Prediction of Force Transmission and Crack Propagation in Bonded Granular Media, PhD thesis, The University of Melbourne, Melbourne.
Kahagalage, S, Tordesillas, A, Nitka, M & Tejchman, J 2017, ‘Of cuts and cracks: data analytics on constrained graphs for early prediction of failure in cementitious materials’, EPJ Web of Conferences, vol. 140, p. 08012.
Leśniewska, D, Nitka, M, Tejchman, J & Pietrzak M 2020, ‘Contact force network evolution in active earth pressure state of granular materials: photo-elastic tests and DEM’, Granular Matter, vol. 22, no. 2, pp. 1–31.
Lin, Q & Tordesillas, A 2014, ‘Towards an optimization theory for deforming dense granular materials: minimum cost maximum flow solutions’, Journal of Industrial & Management Optimization, vol. 10, no. 1, pp. 337–362.
Rousseeuw, PJ 1987, ‘Silhouettes: a graphical aid to the interpretation and validation of cluster analysis’, Journal of Computational and Applied Mathematics, vol. 20, pp. 53–65.
Saunders, P, Kabuya, J, Torres, A & Simon, R 2020, ‘Post-blast slope stability monitoring with slope stability radar’, in PM Dight (ed.), Proceedings of the 2020 International Symposium on Slope Stability in Open Pit Mining and Civil Engineering, Australian Centre for Geomechanics, Perth, pp. 507–522.
Sharon, R, & Eberhardt, E 2020, Guidelines for Slope Performance Monitoring, CSIRO Publishing, Clayton,
Sullivan, TD 2007, ‘Hydromechanical coupling and pit slope movements’, Y Potvin (ed.), Proceedings of the 2007 International Symposium on Rock Slope Stability in Open Pit Mining and Civil Engineering, Australian Centre for Geomechanics, Perth,
Singh, K & Tordesillas, A 2020, ‘Spatiotemporal evolution of a landslide: A transition to explosive percolation’, Entropy, vol. 22, no. 1, p. 67.
Tordesillas, A 2018, Directing Transmission Patterns in Granular Structures from the Grain Scale, US Air Force Office of Scientific Research Final Report AOARD TA101161 / GL080099.
Tordesillas, A, Cramer, A & Walker, DM 2013a, ‘Minimum cut and shear bands’, Powders and Grains 2013: Proceedings of the 7th International Conference on Micromechanics of Granular Media, AIP Publishing, Melville, pp. 507–510.
Tordesillas, A, Walker, DM, Andò, E & Viggiani, G 2013b, ‘Revisiting localized deformation in sand with complex systems’, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 469, no. 2152, p. 20120606.
Tordesillas, A, Kahagalage, S, Ras, C, Nitka, M & Tejchman, J 2020a, ‘Early prediction of macrocrack location in concrete, rocks and other granular composite materials’, Scientific Reports, vol. 10, no. 1, pp. 1–16.
Tordesillas, A, Kahagalage, S, Ras, C, Nitka, M & Tejchman, J 2020b, ‘Coupled evolution of preferential paths for force and damage in the pre-failure regime in disordered and heterogeneous, quasi-brittle granular materials’, Frontiers in Materials, vol. 7,
Tordesillas, A, Pucilowski, S, Lin, Q, Peters, JF & Behringer, RP 2016, ‘Granular vortices: identification, characterization and conditions for the localization of deformation’, Journal of the Mechanics and Physics of Solids, vol. 90, pp. 215–241.
Tordesillas, A, Pucilowski, S, Sibille, L, Nicot, F & Darve, F 2012, ‘Multiscale characterisation of diffuse granular failure’, Philosophical Magazine, vol. 92, no. 36, pp. 4547—4587.
Tordesillas, A, Tobin, S, Cil, M, Alshibli, K & Behringer, RP 2015b, ‘Network flow model of force transmission in unbonded and bonded granular media’, Physical Review E, vol. 91, no. 6, p. 062204.
Tordesillas, A, Kahagalage, S, Campbell, L, Bellett, P, Intrieri, E & Batterham, R 2021a, ‘Spatiotemporal slope stability analytics for failure estimation (SSSAFE): linking radar data to the fundamental dynamics of granular failure’, Scientific Reports, vol. 11, no. 1, pp. 1–18.
Tordesillas, A, Zhou, S, Kahagalage, S, Intrieri, E, Nolesini, T & Di Traglia, F 2021b, ‘Connecting force chains with fracture paths to forecast crater-rim collapse at active 2 volcanoes: Stromboli August 2014 collapses’, in preparation.
Tordesillas, A, Pucilowski, S, Tobin, S, Kuhn, MR, Andò, E, Viggiani, G, Druckrey, A & Alshibli, K 2015a, ‘Shear bands as bottlenecks in force transmission’, Europhysics Letters, vol. 110, no. 5, p. 58005.
Vinh, NX, Epps, J & Bailey, J 2010, ‘Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance’, The Journal of Machine Learning Research, vol. 11, pp. 2837–2854.
Wang, H, Qian, G & Tordesillas, A 2020, ‘Modeling big spatio-temporal geo-hazards data for forecasting by error-correction cointegration and dimension-reduction’, Spatial Statistics, vol. 32, p. 100432.
Zhou, S, Bondell, H, Tordesillas, A, Rubinstein, BIP & Bailey, J 2020, ‘Early identification of an impending rockslide location via a spatially-aided gaussian mixture model’, Annals of Applied Statistics, vol. 14, no. 2, pp. 977–992.
Zhou, S, Tordesillas, A, Intrieri, E, Di Traglia, F & Catani, F 2021, ‘Pinpointing early signs of impending slope failures from space’,
Journal of Geophysical Research: Solid Earth, in review.