## 1 Introduction  
Liquefaction-induced hazards (for instance, settlements, sand boils, lateral spreading, and ground cracks) cause substantial infrastructure damages to buildings, bridges, and lifelines during earthquakes [1; 2; 3; 4; 5]. Thus, it is important to assess the occurrence of soil liquefaction potential and its effects in a seismically active region for earthquake-induced hazard mitigation. Several empirical and semi-empirical methods of liquefaction quantification are presented in the literature [6]. Many of these existing methods are based on correlations between in-situ test measurements and field observation data, and are extensions of the'simplified procedure' developed by [PERSON] and [PERSON] [7]. The in-situ cone penetration test (CPT) has been used by numerous researchers to evaluate seismic soil liquefaction potential (e.g., [PERSON] et al. [8], [PERSON] et al. [6]). The principal advantage of the CPT is that it offers a continuous record of penetration resistance in soil profiles, which enables more detailed subsurface exploration than other in-situ tests. Artificial intelligence (AI) techniques--adaptive neuro fuzzy inference system (ANFIS) [9], artificial neural network (ANN) method [10], support vector machine (SVM) [11; 12], patient ruled-induction method (PRIM) [13], and stochastic gradient boosting (SGB) [14]--have been successfully applied to seismic soil liquefaction assessment based on an in-situ test database. Nevertheless, most of the introduced AI techniques have the following limitations [14; 15; 16]: (1) difficulty in concluding the assessment results due to limited use of prior knowledge; (2) trouble in integrating various sources of information into a coherent system; (3) not proficient in assessing uncertainty; and (4) black box nature. In this paper, Bayesian belief network (BBN) and C4.5 decision tree (DT) approaches are employed to assess seismic soil liquefaction potential based on the updated and relatively large cone penetration test (CPT) database, which includes 251 case histories. BBN is a graphical model that permits a probabilistic association between a set of variables based on the Bayes theorem [17]. There are extended uses of the Bayesian belief network (BBN) in the fields of civil engineering; for example, in seismic hazard assessment [18; 19], risk assessment for bridges [19] and buildings [20; 21], and diagnosis of embankment dam distress [22; 23]. Nevertheless, BBN model applications in seismic soil liquefaction study are still limited [24; 25; 26]. There is no comprehensive BBN model based on machine learning algorithm (ML) and domain knowledge (DK) data fusion methodology to evaluate soil liquefaction potential based on the CPT database. Decision tree (DT) algorithms, such as interactive dichotomizer version 3 (ID3) [27] and commercial version 4.5 (C4.5) [28], are quite transparent. One of the advantages of DT analysis is that the relationship between the binary dependent variable and the related independent variables is clearly illustrated using a tree structure. Hence, decision trees are well-liked and powerful tools in data mining [29]. Limited research work has been reported in the literature on predicating seismic soil liquefaction. [PERSON] et al. [30] used three different DT approaches i.e., chi-squared automatic interaction detection (CHAID), exhaustive CHAID, and classification and regression tree (CART) algorithms for assessment of soil liquefaction. [PERSON] and [PERSON] [16] used the C4.5 DT model on 109 CPT-based case histories; they considered seven significant factors of seismic soil liquefaction--cone tip resistance (\(q_{c}\)), total vertical stress (\(\sigma_{v}\)), vertical effective stress (\(\sigma_{v}\)), mean grain size (\(D_{50}\)), peak ground acceleration (\(a_{max}\)), cyclic stress ratio (\(\tau\)/\(\sigma_{v}\)\({}^{\prime}\)), and earthquake magnitude (\(M\))--without paying attention to sampling bias in the training and testing data sets.