Toward improved prediction of the bedrock depth underneath hillslopes: Bayesian inference of the bottom‐up control hypothesis using high‐resolution topographic data

Gomes, Guilherme José Cunha; Vrugt, Jasper A.; Vargas, Edson

doi:10.1002/2015wr018147

Cited by 20 publications

(27 citation statements)

References 105 publications

(216 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Instead, we generated many different plausible relizations of the bedrock surface using the calibrated depth-tobedrock (DTB) model of [33]. The approach we describe below accounts explicitly for DTB parameters and model uncertainty, and delivers on the promise of [33] that (page 3085) "The posterior prediction uncertainty of the DTB model can be propagated forward through hydromechanical models to derive probabilistic estimates of factors of safety." Indeed, one can treat depth to bedrock as a probabilistic variable and use Monte Carlo simulation with many different bedrock topographies to quantify uncertainty in the estimates of slope stability.…”

Section: Appendix B Characterization Of Bedrock Topographic Uncertaintymentioning

confidence: 99%

“…In a recent paper, Gomes et al [33] developed a geomorphologic model to predict the spatial distribution of depth to bedrock (DTB) from high-resolution topographic data, numerical modeling and Bayesian analysis. We used the DTB model of [33] and refer interested readers to this publication for further details.…”

Section: Uncertainty Of Bedrock Topographymentioning

confidence: 99%

“…We used the DTB model of [33] and refer interested readers to this publication for further details. Bayesian analysis was used to reconcile the DTB model with distributed bedrock depth measurements.…”

Section: Uncertainty Of Bedrock Topographymentioning

confidence: 99%

“…Resulting topographic maps often do not do justice to complex field measurements of the bedrock depth, which often demonstrate significant spatial variability [3,33] with a geometry that is difficult to characterize adequately with some closed-form mathematical expression, while hydraulic and strength parameters can vary abruptly at the soil-bedrock interface. Whereas some authors have used high-resolution bedrock depth maps to assess hillslope stability [28,32,34,35], existing studies in the literature do not properly recognize the effect of bedrock depth uncertainty on slope stability assessments.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, [3] advocated monitoring variations in pore water pressures (both negative and positive) at specific sites during the year in order to assess their impact on slope stability. We use the calibrated geomorhologic model of [33] to generate plausible maps of the bedrock depth. The model was calibrated against a rich data set of distributed bedrock depth measurements using Bayesian inference with the DREAM algorithm [36,37].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

The role of uncertainty in bedrock depth and hydraulic properties on the stability of a variably-saturated slope

Gomes

Vrugt

Vargas

et al. 2017

Computers and Geotechnics

Self Cite

View full text Add to dashboard Cite

a b s t r a c tWe investigate the uncertainty in bedrock depth and soil hydraulic parameters on the stability of a variably-saturated slope in Rio de Janeiro, Brazil. We couple Monte Carlo simulation of a threedimensional flow model with numerical limit analysis to calculate confidence intervals of the safety factor using a 22-day rainfall record. We evaluate the marginal and joint impact of bedrock depth and soil hydraulic uncertainty. The mean safety factor and its 95% confidence interval evolve rapidly in response to the storm events. Explicit recognition of uncertainty in the hydraulic properties and depth to bedrock increases significantly the probability of failure.

show abstract

Section: Appendix B Characterization Of Bedrock Topographic Uncertaintymentioning

confidence: 99%

Section: Uncertainty Of Bedrock Topographymentioning

confidence: 99%

Section: Uncertainty Of Bedrock Topographymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The role of uncertainty in bedrock depth and hydraulic properties on the stability of a variably-saturated slope

Gomes

Vrugt

Vargas

et al. 2017

Computers and Geotechnics

Self Cite

View full text Add to dashboard Cite

show abstract

Machine learning‐based prediction of the seismic response of fault‐crossing natural gas pipelines

Zhang¹,

Ayello²,

Honegger

et al. 2023

Earthq Engng Struct Dyn

View full text Add to dashboard Cite

Herein, we utilized machine‐learning (ML) and data‐driven (regression) techniques to tackle a critical infrastructure engineering problem—namely, predicting the seismic response of natural gas pipelines crossing earthquake faults. Such a 3D nonlinear problem can take up to 10 h to solve by performing finite element analysis (FEA), considering the length of the pipeline and a large number of pipe and soil elements. However, the ML and data‐driven techniques can learn the projection rule of input‐output and predict the pipeline response instantaneously given a set of input features. In addition, the well‐trained ML model can be implemented for regional‐scale risk and rapid post‐event damage assessments. In this study, the input for ML comprised approximately 217K nonlinear FEAs, which covered a wide range of combinations of soil, structural and fault properties and yielded critical pipe strain responses under fault‐rupture displacements. We adopted various regression models and physics‐constrained neural networks, which can accurately and rapidly predict the tensile and compressive strains for a broad range of probable fault‐rupture displacements. Performances of various ML and conventional statistical models were systematically examined. Not surprisingly, neural networks exhibited the best performance for this multi‐output regression problem, in which R2 > 0.95 was achieved for a wide range of fault displacement (FD) levels. Further, we used the trained neural network with 14.5 million Monte‐Carlo‐generated input samples to predict the maximum tensile and compressive strain curves of pipelines. This new dataset aimed at filling the missing input‐output points from the 217K FEAs, and improved the accuracy of the prediction of probability of failure for natural gas pipelines under FD hazards.

show abstract

Bayesian geomorphology

Korup

2020

Earth Surf Processes Landf

View full text Add to dashboard Cite

Summary The rapidly growing amount and diversity of data are confronting us more than ever with the need to make informed predictions under uncertainty. The adverse impacts of climate change and natural hazards also motivate our search for reliable predictions. The range of statistical techniques that geomorphologists use to tackle this challenge has been growing, but rarely involves Bayesian methods. Instead, many geomorphic models rely on estimated averages that largely miss out on the variability of form and process. Yet seemingly fixed estimates of channel heads, sediment rating curves or glacier equilibrium lines, for example, are all prone to uncertainties. Neighbouring scientific disciplines such as physics, hydrology or ecology have readily embraced Bayesian methods to fully capture and better explain such uncertainties, as the necessary computational tools have advanced greatly. The aim of this article is to introduce the Bayesian toolkit to scientists concerned with Earth surface processes and landforms, and to show how geomorphic models might benefit from probabilistic concepts. I briefly review the use of Bayesian reasoning in geomorphology, and outline the corresponding variants of regression and classification in several worked examples. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd

show abstract

Toward improved prediction of the bedrock depth underneath hillslopes: Bayesian inference of the bottom‐up control hypothesis using high‐resolution topographic data

Cited by 20 publications

References 105 publications

The role of uncertainty in bedrock depth and hydraulic properties on the stability of a variably-saturated slope

The role of uncertainty in bedrock depth and hydraulic properties on the stability of a variably-saturated slope

Machine learning‐based prediction of the seismic response of fault‐crossing natural gas pipelines

Bayesian geomorphology

Contact Info

Product

Resources

About