## 4 Results and Analysis  
### Evaluation and Validation  
After we performed grid search optimization (as detailed in Section 3.2) on the machine learning models, the models with the highest grid cross-validation accuracy were selected to evaluate their performance. Table 4 shows the final hyperparameters and performance evaluation metrics (macro-averages) for the selected machine learning models. The suffixes 1, 2, and 3 represent the models trained for structural, transportation/civil, and non-structural sections, respectively. Figure 4 compares the performance evaluation metrics of different machine learning models for structural, civil, and non-structural sections, showing that the effectiveness of machine learning modeling significantly depends upon the selection of a suitable algorithm. When evaluated using the four macro-average evaluation metrics, the LGBM classification algorithm demonstrated higher generalization ability compared to the other classifiers, which yielded prediction accuracies of 0.904, 0.944, and 0.762 for structural, transportation/civil, and non-structural sections, respectively. The prediction of the service state for the transportation/civil sections was the easiest, while prediction proved relatively more difficult for non-structural sections. This is due to the complexity of the various element types included in non-structural sections and the intricate relationship between the service states of the elements and the training features. Ensemble learning algorithms, such as LGBM and RF, demonstrate better classification performance when compared to SVM, KNN, DT, and MLP algorithms. The imbalanced classification of three types of tunnel sections presents a challenge that must be addressed by the prediction model. Ensemble learning models have two advantages when it comes to dealing with imbalanced classification problems. Firstly, they employ multiple sampling techniques to avoid additional learning costs. Secondly, their integration of multiple classifiers helps prevent overfitting [22]. For the most critical structural section, the LGBM model exhibits a slightly lower Macro-PRE than the SVM and RF algorithms. However, the Macro-REC and Macro-F1 are significantly better than those of the other five algorithms, indicating that the LGBM model has a stronger ability to identify deteriorations in operational performance with a low false-negative rate. Since the deterioration of a tunnel's structural components can lead to serious safety hazards, it is necessary to select a model with a higher recall rate. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Optimized Hyperparameters**} & \multirow{2}{*}{**Accuracy for Training Set**} & \multicolumn{6}{c}{**Evaluation Metric for Testing Set**} \\ \cline{3-8}  & & & & **Macro-ACC** & **Macro-PRE** & **Macro-REC** & **Macro-F1** \\ \hline \multirow{3}{*}{DT-1} & max\_depth = None, & & & & & & \\  & max\_leaf\_nodes = 600, & & & & & & \\  & min\_samples\_leaf = 1, & & & & & & \\  & min\_samples\_split = 2 & & & & & & \\  & max\_depth = 25, & & & & & & \\  & max\_leaf\_nodes = 600, & & & & & & \\  & min\_samples\_leaf = 1, & & & & & & \\  & min  
To further demonstrate the prediction performance of the LGBM model, which was adopted for the following analysis of the factors influencing tunnel service state, Figure 5 shows the confusion matrix and Receiver Operating Characteristic (ROC) curves of the test set for the prediction of the service states of the three types of elements using the LGBM model. In the matrix, the rows represent the actual conditions of the test set, and the columns represent the predicted conditions. It can be observed that the largest number of labels in each column lies on the diagonal of the confusion matrix, indicating that most of the predictions are correct. For the three sections, the prediction accuracy of service state B (good) was the lowest, as predictions of this category are more likely to be misclassified as A (exceptional).