Failure Analysis of Soil Slopes with Advanced Bayesian Networks

  • Longxue He

    Leibniz University of Hannover

  • António Topa Gomes

    University of Porto

  • Matteo Broggi

    Leibniz University Hannover

  • Michael Beer

    Leibniz University Hannover, University of Liverpool and Tongji University


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.

Keywords: failure probability, slope stability, water table, drainage, advanced Bayesian Networks
Published online
How to Cite
He, L., Gomes, A. T., Broggi, M., Beer, M. “Failure Analysis of Soil Slopes with Advanced Bayesian Networks”, Periodica Polytechnica Civil Engineering, 63(3), pp. 763-774, 2019.
Research Article