###### Abstract  
Accurate rock mass quality classification is crucial for the design and construction of underground projects. Traditional methods often rely on expert experience, introducing subjectivity, and struggle with complex geological conditions. Machine learning algorithms have improved this issue, but obtaining complete rock mass quality datasets is often difficult due to high cost and complex procedures. This study proposed a hybrid XGBoost model for predicting rock mass quality using incomplete datasets. The zebra optimization algorithm (ZOA) and Bayesian optimization (BO) were used to optimize the hyperparameters of the model. Data from various regions and types of underground engineering projects were utilized. Adaptive synthetic (ADASYN) oversampling addressed class imbalance. The model was evaluated using metrics including accuracy, Kappa, precision, recall, and F1-score. The ZOA-XGBoost model achieved an accuracy of 0.923 on the test set, demonstrating the best overall performance. Feature importance analysis and individual conditional expectation (ICE) plots highlighted the roles of RQD and UCS in predicting rock mass quality. The model's robustness with incomplete data was verified by comparing its performance with other machine learning models on a dataset with missing values. The ZOA-XGBoost model outperformed other models, proving its reliability and effectiveness. This study provides an efficient and objective method for rock mass quality classification, offering significant value for engineering applications. rock mass classification; XGBoost; underground engineering; metaheuristic optimization; machine learning +
Footnote †: journal: Medical  
fail to comprehensively reflect rock mass quality due to the limited factors considered. As geotechnical engineering evolved, the need for multi-index classification methods became evident. [PERSON] [3] introduced the rock mass rating (RMR) system, which includes factors such as uniaxial compressive strength, RQD, joint spacing, joint condition, and groundwater. [PERSON] et al. [4] proposed the Q-system, which considers RQD, joint characteristics, fracture water, and stress conditions, establishing the relationship between Q-value and support type. Other commonly used empirical methods include the geological strength index (GSI), rock mass index (RMI), hydropower classification (HC), and basic quality (BQ). [PERSON] et al. [5] summarized these methods, highlighting their respective indicators. Multi-index methods provide a more comprehensive assessment, but obtaining certain rock mass indicators can be challenging under complex geological conditions, increasing the difficulty of classification. Delays in rock mass quality assessment can slow project progress and cause safety incidents, especially during the initial stages of underground construction. Moreover, empirical methods often use simple linear relationships, struggling to adapt to complex and variable geological conditions. In recent years, with the rapid advancement of artificial intelligence, machine learning methods have been widely applied in geotechnical engineering [6]. Machine learning, with its ability to handle complex nonlinear data, flexibility, and automated feature extraction, has gained traction among researchers for rock mass quality classification [7]. For example, [PERSON] et al. [8] proposed an intelligent rock mass classification model based on genetic algorithms (GA) and support vector machines (SVM), demonstrating its adaptability and accuracy compared to backpropagation neural networks (BPN). [PERSON] et al. [9] identified factors influencing rock mass quality evaluation through multivariate statistical analysis, including rock mass strength and weathering degree, fragmentation, and water flow conditions, and classified rock masses using artificial neural networks (ANN) for open-pit mines. This study reduced the subjectivity in selecting indicators and classification methods, improving classification quality and accuracy. Additionally, [PERSON] et al.