# Study on Classification Method of Soil Liquefaction Potential Based on Decision Tree
###### Abstract  
A new method for evaluating the liquefaction probability of saturated soils is presented in this paper. The existing methods are mainly expressed in equation form, and the evaluation process is always complex. Therefore, to address these shortcomings, a new method based on a tabular form is constructed using a hybrid approach combining the logistic equation and decision trees (classification and regression tree and chi-squared automatic interaction detector) based on the shear wave velocity test databases. The effectiveness of the new method was verified by comparing the predicted liquefaction results with actual liquefaction results. The new method improved the accuracy of the liquefaction evaluation method based on shear wave velocity; the classification was correct for more than 75% of the samples. In addition, the importance of the liquefaction factors was considered during the method construction, which ultimately simplified the evaluation process while ensuring accuracy.  
shear wave velocity; liquefaction probability; decision tree; liquefaction potential grades; order of importance of liquefaction parameters +
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probabilistic reasoning of the robust BBN model investigating the performance of the Bayesian belief network (BBN) and C4.5 decision tree (DT) models to evaluate seismic soil liquefaction potential based on a cone penetration test (CPT) dataset [9]. [PERSON] proposed a machine learning-based liquefaction disaster assessment method based on a convolutional neural network (CNN) model and a Light Gradient Boosting Machine (Light GBM) model [10]. [PERSON] proposed to optimize the deep neural network (DNN) and decided to train the prediction model by a multi-layer fully connected network (ML-FCN) based on the \(V_{s}\) and SPT dataset [11]. However, the above methods only carry out operations checking calculations for the machine learning model, without providing specific evaluation criteria as a conclusion. Although there has been great progress in the study of liquefaction probability evaluation methods [12], the complicated equation fitting process leads to a complicated form of the liquefaction probability equation, which is not convenient for practical engineering use [13]. To address this issue, in this study, a new modeling method was adopted to construct a convenient liquefaction probability evaluation table. This study was divided into two parts.  
Part 1: First, a logistic regression (LR) liquefaction probability evaluation equation was constructed, and then the liquefaction probability of each site in a database was calculated. Finally, the rationality of the liquefaction probability values was evaluated.  
Part 2: All the sites in the dataset were divided into three grades based on the liquefaction probabilities. The liquefaction grade and factors were substituted into a decision tree to obtain interpretable classification results of the liquefaction grades of various sites. A new liquefaction probability scale was derived from the decision tree results. Finally, the rationality of the liquefaction evaluation scale was evaluated.  
The significance of this study is not only that tabular grading criteria were proposed for the liquefaction potential but also that a decision tree was applied in the study of the liquefaction potential. In the future, this concept can be used to construct liquefaction potential assessment methods based on other in situ test results.