Scoops3D: software to analyze 3D slope stability throughout a digital landscape

Reid, Mark E.; Christian, Sarah B.; Brien, Dianne L.; Henderson, S. T.

doi:10.3133/tm14a1

Cited by 100 publications

(117 citation statements)

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“…Scoops3D uses a three‐dimensional “method of columns” limit‐equilibrium analysis to compute the stability of potential slope failures (landslides) with a spherical potential (trial) slip surface. In these simulations, we apply the Bishop's simplified method of limit‐equilibrium analysis (Bishop, ) to calculate stability, which uses the following equation to determine factor of safety of each potential landslide:

F = \frac{\sum R_{i, j} ([], c_{i, j} A_{normalh, j} + ((), W_{i, j} - S_{{normale}_{i, j}} u_{i, j} A_{h, j}) tan φ_{i, j}) / m_{α_{i, j}}}{\sum W_{i, j} ([], R_{i, j} sin α_{i, j} + k_{eq} e_{i, j})}

where R i,j is the distance (m) from a rotational axis above the ground surface to the center of the trial slip area for the i , j column in a failure mass, c i,j is the cohesion (in kPa) on the trial slip surface for the i , j column in a potential failure mass, A h,j is the horizontal area (m 2 ) of the slip surface, W i,j is the weight of the column (kg m/s 2 ), S ei,j is the effective degree of saturation of the column (dimensionless), φ i,j is the angle of internal friction on trial slip surface for the i , j column (°), u i,j is the pore‐fluid pressure (Pa), m αi,j is part of the computation of normal force on the slip surface (dimensionless), and α i,j is the apparent dip of the column base (°); k eq and e i,j are used to incorporate the effects of earthquake loading and are not included in our analyses (Reid et al, ). Version 1.1 of Scoops3D allows for direct use of pore pressures instead of pressure heads, making it possible to transfer data from the groundwater flow model with less post‐processing; for this variably saturated case, Scoops3D assumes S e i,j = 1 for saturated materials and thereby computes strength using the standard Coulomb‐Terzaghi rule (Terzaghi, ).…”

Section: Modeling Methods and Input Parametersmentioning

confidence: 99%

“…where R i,j is the distance (m) from a rotational axis above the ground surface to the center of the trial slip area for the i, j column in a failure mass, c i,j is the cohesion (in kPa) on the trial slip surface for the i, j column in a potential failure mass, A h,j is the horizontal area (m 2 ) of the slip surface, W i,j is the weight of the column (kg m/s 2 ), S ei,j is the effective degree of saturation of the column (dimensionless), φ i,j is the angle of internal friction on trial slip surface for the i, j column (°), u i,j is the pore-fluid pressure (Pa), m αi,j is part of the computation of normal force on the slip surface (dimensionless), and α i,j is the apparent dip of the column base (°); k eq and e i,j are used to incorporate the effects of earthquake loading and are not included in our analyses (Reid et al, 2015). Version 1.1 of Scoops3D allows for direct use of pore pressures instead of pressure heads, making it possible to transfer data from the groundwater flow model with less post-processing; for this variably Figure 3.…”

Section: Slope-stability Analysismentioning

confidence: 99%

See 1 more Smart Citation

Combining Multiphase Groundwater Flow and Slope Stability Models to Assess Stratovolcano Flank Collapse in the Cascade Range

et al. 2018

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Hydrothermal alteration can create low‐permeability zones, potentially resulting in elevated pore‐fluid pressures, within a volcanic edifice. Strength reduction by rock alteration and high pore‐fluid pressures have been suggested as a mechanism for edifice flank instability. Here we combine numerical models of multiphase heat transport and groundwater flow with a slope‐stability code that incorporates three‐dimensional distributions of strength and pore‐water pressure to address the following questions: (1) What permeability distributions and contrasts produce elevated pore‐fluid pressures in a stratovolcano? (2) What are the effects of these elevated pressures on flank stability? (3) Finally, what are the effects of magma intrusion on potential flank failure in an edifice? Simulation results show that under a range of plausible parameters, water tables in a stratovolcano can be elevated or perched. These elevated water tables result in universally lower stability (lower factor of safety) compared with equivalent dry edifices, indicating a higher likelihood of flank collapse. Low‐permeability (<1 × 10−17 m2) layers such as altered pyroclastic deposits or breccias can result in locally saturated regions (perched water) and lower factors of safety near the ground surface but may actually reduce liquid water saturation and pore pressures in the core of the edifice and thus may favor small, shallow collapses over larger, deeper collapses. Magma intrusion into the base of the edifice increases pore‐fluid pressures and decreases the factor of safety. However, the shear strength of edifice rocks also exerts a significant control on stability, so both mechanical properties and pore‐fluid pressures are important for stability assessments.

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F = \frac{\sum R_{i, j} ([], c_{i, j} A_{normalh, j} + ((), W_{i, j} - S_{{normale}_{i, j}} u_{i, j} A_{h, j}) tan φ_{i, j}) / m_{α_{i, j}}}{\sum W_{i, j} ([], R_{i, j} sin α_{i, j} + k_{eq} e_{i, j})}

Section: Modeling Methods and Input Parametersmentioning

confidence: 99%

Section: Slope-stability Analysismentioning

confidence: 99%

Combining Multiphase Groundwater Flow and Slope Stability Models to Assess Stratovolcano Flank Collapse in the Cascade Range

et al. 2018

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“…In the literature, approaches to predict the temporal occurrence of landslides are equally if not more diversified. Depending on the scope, the temporal coverage, the return time of the predictions, and the extent of the study area, methods include (i) the use of empirical rainfall thresholds for the possible occurrence of landslides (Glade et al, 2000;Dai and Lee, 2001;Crosta and Frattini, 2000;Aleotti, 2004;Guzzetti et al, 2007Guzzetti et al, , 2008Saito et al, 2010;Ko and Lo, 2018;Segoni et al, 2018;Guzzetti et al, 2019), (ii) physically-based, coupled, distributed rainfall and infiltration slope stability models (Montgomery and Dietrich, 1994;Van Asch et al, 1996;Baum et al, 2008;Lanni et al, 2013;Formetta et al, 2014;Reid et al, 2015;Formetta et al, 2016;Alvioli and Baum, 2016;Bout et al, 2018), and (iii) the analysis of time series of historical landslides and landslide events (Crovelli and Coe, 2009;Rossi et al, 2010b;Witt et al, 2010). Only physically-based models (inherently) consider the spatio-temporal interactions that condition landslide occurrence, which are not considered by the threshold models or by the analysis of the historical records.…”

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confidence: 99%

Space-Time Landslide Predictive Modelling

Lombardo¹,

Opitz²,

Ardizzone³

et al. 2020

Preprint

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Landslides are nearly ubiquitous phenomena and pose severe threats to people, properties, and the environment in many areas. Investigators have for long attempted to estimate landslide hazard in an effort to determine where, when (or how frequently), and how large (or how destructive) landslides are expected to be in an area. This information may prove useful to design landslide mitigation strategies, and to reduce landslide risk and societal and economic losses. In the geomorphology literature, most of the attempts at predicting the occurrence of populations of landslides by adopting statistical approaches are based on the empirical observation that landslides occur as a result of multiple, interacting, conditioning and triggering factors. Based on this observation, and under the assumption that at the spatial and temporal scales of our investigation individual landslides are discrete "point" events in the landscape, we propose a novel Bayesian modelling framework for the prediction of the spatio-temporal occurrence of landslides of the slide type caused by weather triggers. We build our modelling effort on a Log-Gaussian Cox Process (LGCP) by assuming that individual landslides in an area are the result of a point process described by an unknown intensity function. The modelling framework has two stochastic components: (i) a Poisson component, which models the observed (random) landslide count in each terrain subdivision for a given landslide "intensity", i.e., the expected number of landslides per terrain subdivision (which may be transformed into a corresponding landslide "susceptibility"); and (ii) a Gaussian component, used to account for the spatial distribution of the local environmental conditions that influence landslide occurrence, and for the spatio-temporal distribution of "unobserved" latent environmental controls on landslide occurrence. We tested our prediction framework in the Collazzone area, Umbria, Central Italy, for which a detailed multi-temporal landslide inventory covering the period from before 1941 to 2014 is available together with lithological and bedding data. We subdivided the 79 km 2 area into 889 slope units (SUs). In each SU, we computed the percentage of 16 morphometric covariates derived from a 10 m × 10 m digital elevation model, and 13 lithological and bedding attitude covariates obtained from a 1:10,000 scale geological map. We further counted how many of the 3,379 landslides in the multi-temporal inventory affect each SU and grouped them into six periods. We used this complex space-time information to prepare five models of increasing complexity. Our "baseline" model (Mod1) carries the spatial information only through the covariates mentioned above. It does not include any additional information about the spatial and temporal structure of the data, and it is therefore equivalent to a "traditional" landslide susceptibility model. The second model (Mod2) is analogous, but it allows for time-interval-specific regression constants. Our next two models are more complex. In partic...

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“…También encontramos algunos sistemas propietarios como el Scroops3D (Reid, Christian, Brien & Henderson, 2015), que tiene buenas prestaciones pero, por supuesto, son herramientas costosas.…”

Section: Estado Del Arteunclassified

Visualizador 3D de la geografía de Costa Rica

Hernández-Castro¹,

Monge-Fallas²

2016

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Scoops3D: software to analyze 3D slope stability throughout a digital landscape

Cited by 100 publications

References 0 publications

Combining Multiphase Groundwater Flow and Slope Stability Models to Assess Stratovolcano Flank Collapse in the Cascade Range

Combining Multiphase Groundwater Flow and Slope Stability Models to Assess Stratovolcano Flank Collapse in the Cascade Range

Space-Time Landslide Predictive Modelling

Visualizador 3D de la geografía de Costa Rica

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