The stability and deformation problems of soil have been a research topic of greatconcern since the past decades. The potential catastrophic events are induced by various complex factors, such as uncertain geotechnical conditions, external environment, and anthropogenic influence, etc. To prevent the occurrence of disasters in geotechnical engineering, the main purpose of this study is to enhance the Bayesian networks (BNs) model for quantifying the uncertainty and predicting the risk level in solving the geotechnical problems. The advanced BNs model is effective for analyzing the geotechnical problems in the poor data environment. The advanced BNs approach proposed in this study is applied to solve the stability of soil slopes problem associated with the specific-site data. When probabilistic models for soil properties are adopted, enhanced BNs approach was adopted to cope with continuous input parameters. On the other hand, Credal networks (CNs), developed on the basis of BNs, are specially used for incomplete input information. In addition, the probabilities of slope failure are also investigated for different evidences. A discretization approach for the enhanced BNs is applied in the case of evidence entering into the continuous nodes. Two examples implemented are to demonstrate the feasibility and predictive effectiveness of the BNs model. The results indicate the enhanced BNs show a precisely low risk for the slope studied. Unlike the BNs, the results of CNs are presented with bounds. The comparisonof three different input information reveals the more imprecision in input, the more uncertainty in output. Both of them can provide the useful disaster-induced informationfor decision-makers. According to the information updating in the models, the positionof the water table shows a significant role in the slope failure, which is controlled bythe drainage states. Also, it discusses how the different types of BNs contribute toassessing the reliability and risk of real slopes, and how new information could beintroduced in the analysis. The proposed models in this study illustrate the advancedBN model is a good diagnosis tool for estimating the risk level of the slope failure.In a follow-up study, the BNs model is developed based on its potential capabilityfor the information updating and importance measure. To reduce the influence ofuncertainty, with the proposed BN model, the soil parameters are updated accuratelyduring the excavation process, and besides, the contribution of epistemic uncertainty from geotechnical parameters to the potential disaster can be characterized based on the developed BN model. The results of this study indicate the BNs model is aneffective and flexible tool for risk analysis and decision making support in geotechnical engineering.