## 6 Discussion and Conclusions  
In a BBN, the input of expert knowledge can enable the realistic integration of domain knowledge with statistical data. In addition, all the parameters in a BBN have an understandable semantic interpretation. Furthermore, a BBN model can always be updated as new data become available. The authors in this study selected 11 direct significant variables or factors of seismic soil liquefaction, while other researchers (e.g., [PERSON] and [PERSON] [16]) used indirect parameters i.e., cyclic stress ratio (CSR). In the proposed BBN models, soil, seismic, and site condition factors are encoded directly from a CPT dataset, instead of just cyclic resistance ratio (CRR) and CSR; this can be effectively utilized by geotechnical professionals to evaluate seismic soil liquefaction potential. No calibration or normalization of cone tip resistance, \(q_{c}\) (MPa), was done in previous studies (e.g., [PERSON] and [PERSON] [16], [PERSON] and [PERSON] [49], [PERSON] and [PERSON] [5]). However, in this work, \(q_{c1 Ncs}\), the equivalent clean sand penetration resistance, was used to decrease uncertainty. Additionally, [PERSON] et al. [50] concluded that normalized cone tip penetration value (\(q_{c1N}\)) is an important factor, and has the highest effect on seismic soil liquefaction triggering. The resulting BBN model showed a relatively better performance for training and testing data than other models for the metrics of overall accuracy (OA), MCC, precision, recall and F-measure, and AUC of ROC. The limitations of the proposed robust BBN are: too much reliance on bulk data and not being suitable for missing values, owing to the learning demands of the K2 ML algorithm. Discerning earthquake-induced soil liquefaction is a complicated and nonlinear procedure, affected by diversified factors of uncertainties. In this paper, two effective approaches--BBN and C4.5 DT--are presented to examine the seismic soil liquefaction potential assessment. They are then compared with a simplified procedure and an evolutionary-based approach. The major findings in this study are presented as follows: \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Node** & **Mutual Info** & **Percent** & **Variance of Beliefs** \\ \hline Liquefaction potential & 0.980740 & 100.0000 & 0.243354 \\ Equivalent clean sand penetration resistance & 0.022310 & 2.270000 & 0.007442 \\ Soil behavior type index & 0.010800 & 1.100000 & 0.003674 \\ Peak ground acceleration & 0.004560 & 0.465000 & 0.001552 \\ Vertical effective stress & 0.003450 & 0.351000 & 0.001171 \\ Fines content & 0.002980 & 0.304000 & 0.001015 \\ Earthquake magnitude & 0.001150 & 0.118000 & 0.000394 \\ Groundwater table & 0.000490 & 0.050100 & 0.000167 \\ Total vertical stress & 0.000230 & 0.023800 & 0.000079 \\ Depth of soil deposit & 0.000200 & 0.020400 & 0.000068 \\ Closest distance to rupture surface & 0.000190 & 0.019500 & 0.000065 \\ Thickness of soil layer & 0.000000 & 0.000000 & 0.000000 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Sensitivity analysis of “liquefaction potential”. 1. The BBN model has relatively better results, as compared to the C4.5 DT, CPT-YD, and CPT-RA models. Considering its overall predictive accuracy, MCC, precision, recall, F-measure for liquefaction and non-liquefaction instances, AUC of ROC, simplicity in practice, data-driven characteristics, and the ability to map interactions between variables, the use of the BBN model in evaluating seismic soil liquefaction by multiple complex factors is quite promising. 2. The proposed robust BBN model can not only quantitatively predict seismic soil liquefaction potential probability under certain influence factors (seismic, soil, and site conditions), but also identify the main diagnostic reasons and fault-finding states' combinations presumed to support decisions on seismic soil liquefaction mitigation measures for sustainable development. 3.