**3. Machine Learning Model for Punching Shear Resistance Prediction**

The flow of establishment of an ML model is shown in Figure 4, which can be generalized as the following steps [40]: (1) Divide the compiled database as a training set (containing 500 data) and test set (containing 110 data) based on the ratio of 80% and 20%; (2) obtain the optimal hyperparameters by model training; (3) examine the generalization ability of the candidate model by the test set; (4) output the final prediction model. The four ML models selected in this paper are all established following this procedure, and the related introductions for models are displayed in Section 3.1.

**Figure 4.** Flowchart of ML modelling.

#### *3.1. Overview of Machine Learning Models*

As the basic ML algorithms, ANN and DT have been widely studied and thus become the beginning of two types of artificial intelligence algorithms: deep learning and ensemble learning [41]. Among ensemble learning algorithms, RF and XGBoost are two representative algorithms constructed by different ensemble tactics such as bagging and boosting [40]. Due to the four typical ML models possessing different fitting techniques, the comparison of them enhances the credibility of the final prediction model.
