Das, Roneet (2016-05). Probabilistic Slope Stability Assessment of Submarine and Slides by the Use of Bayesian Inference. Master's Thesis. Thesis uri icon

abstract

  • Estimates of probability of slope failure based on Monte-Carlo methods depend upon the state of evidence on the slope stability model parameters. The Bayesian framework illustrated in this paper to estimate the probability of failure against submarine landslide incorporates experimental data (undrained shear strength) with the initial state of evidence on the model parameters to achieve more certain and accurate model predictions and estimates of probability of failure. The objective of this research was to determine the probability of failure of a submarine slope due to static loading conditions for a given state of evidence (e.g. soil data, slope stability model and expert beliefs). A physics-based forward model (infinite slope) was adopted to evaluate the probability of failure against sliding. The geotechnical and geometric parameters (unit weight of the slice, thickness, pseudo-static seismic coefficient and slope angle) of the proposed model were regarded as random variables. The initial state of evidence and level of uncertainty associated with the proposed model parameters were presented as prior probability distribution functions (log-normal distribution). The Bayesian framework was adopted to calibrate the proposed model with synthetically generated experimental observations representing different in-situ undrained shear strength conditions. Model predictions on the mobilized shear strength when sampled from posterior distributions of the model parameters showed greater certainty and accuracy with respect to the Monte-Carlo forward model simulations based on the prior distributions. Results showed significant changes in the landslide probability with the increase in amount of data for two scenarios used for model calibration, while indicating the correlation structure changes among the model parameters. This allowed to estimating the sampling scenarios and their corresponding confidence gains prior to a field investigation.
  • Estimates of probability of slope failure based on Monte-Carlo methods depend upon the state of evidence on the slope stability model parameters. The Bayesian framework illustrated in this paper to estimate the probability of failure against submarine landslide incorporates experimental data (undrained shear strength) with the initial state of evidence on the model parameters to achieve more certain and accurate model predictions and estimates of probability of failure.

    The objective of this research was to determine the probability of failure of a submarine slope due to static loading conditions for a given state of evidence (e.g. soil data, slope stability model and expert beliefs). A physics-based forward model (infinite slope) was adopted to evaluate the probability of failure against sliding. The geotechnical and geometric parameters (unit weight of the slice, thickness, pseudo-static seismic coefficient and slope angle) of the proposed model were regarded as random variables. The initial state of evidence and level of uncertainty associated with the proposed model parameters were presented as prior probability distribution functions (log-normal distribution). The Bayesian framework was adopted to calibrate the proposed model with synthetically generated experimental observations representing different in-situ undrained shear strength conditions.

    Model predictions on the mobilized shear strength when sampled from posterior distributions of the model parameters showed greater certainty and accuracy with respect to the Monte-Carlo forward model simulations based on the prior distributions. Results showed significant changes in the landslide probability with the increase in amount of data for two scenarios used for model calibration, while indicating the correlation structure changes among the model parameters. This allowed to estimating the sampling scenarios and their corresponding confidence gains prior to a field investigation.

publication date

  • May 2016