Abstract
To prevent catastrophic consequences of slope failure, it can be effective to have in advance a good understanding of the effect of both, internal and external triggering-factors on the slope stability. Herein we present an application of advanced Bayesian networks for solving geotechnical problems. A model of soil slopes is constructed to predict the probability of slope failure and analyze the influence of the induced-factors on the results. The paper explains the theoretical background of enhanced Bayesian networks, able to cope with continuous input parameters, and Credal networks, specially used for incomplete input information. Two geotechnical examples are implemented to demonstrate the feasibility and predictive effectiveness of advanced Bayesian networks. The ability of BNs to deal with the prediction of slope failure is discussed as well. The paper also evaluates the influence of several geotechnical parameters. Besides, it discusses how the different types of BNs contribute for assessing the stability of real slopes, and how new information could be introduced and updated in the analysis.
Highlights
Slope failure is a potential catastrophic threat by leading to casualties and economic loss in many areas around the world
In the case of poor information, the input uncertainty affects the precision of output so that the results are denoted with the probability bounds
When the input nodes Cohesion, Friction Angle, Unsaturated Unit Weight, and Saturated Unit Weight only can be defined as interval variables with the limited information, the probability bound of slope failure is between 0 and 1
Summary
Slope failure is a potential catastrophic threat by leading to casualties and economic loss in many areas around the world. The slope stability problem, as a classical research topic, has attracted much attention in geotechnical engineering [1]. Water plays a significant role in the process, affecting the slope stability. Soil properties and the presence or absence of vegetation can potentially affect the slope stability [3, 4]. It is pivotal for decision-makers to achieve the information which the key failure-inducing factors are more sensitive to destabilizing the slope in order to avoid the highly economical and life loss
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
