Reliability Studies of Slope Stability

Abstract

In the past few decades, the use of Bayes' theorem in geotechnical engineering has increased significantly. A good amount of work is done on implementing this theorem for improving the knowledge on soil properties, identification of soil strata, and quantifying different uncertainties involved in predicting soil properties. However, the results of using this approach on a geotechnical structure have not been mentioned in the past literature. Hence, in the current work, Bayes' approach is used to predict the soil property parameters and further these parameters are used in slope stability analysis of a basic slope. It is aimed to investigate if the uncertainties in such analysis can be quantified. For this, a simple Bayes' model is developed to predict the input parameters of the soil properties. This model is developed using the PyMC3 package in python which is specially developed to solve Bayes' problems. Along with Bayes' statistics, normal/frequentist statistics is also used to derive the soil properties from two different data sets (3000 data points and 24 data points). To conduct the reliability studies of a slope, a Finite element model developed at TU Delft is used. The results of slope stability analysis obtained using both approaches are further compared. Results clearly show that when uncertainty is involved Bayes' approach encapsulates its effect on calculated FOS whereas the frequentist approach doesn't. This difference in both approaches is observed due to consideration of all the possible values of input parameters by Bayes' approach. With the frequentist approach, a single input parameter is predicted neglecting all the other possible inputs. Hence, it is concluded that using the Bayes' approach better and more reliable results over the frequentist approach by providing more insight about the uncertainties involved in the analysis.