A probability prediction method for the classification of surrounding rock quality of tunnels with incomplete data using Bayesian networks

The classification of surrounding rock quality is critical for the dynamic construction and design of tunnels. However, obtaining complete parameters for predicting the surrounding rock grades is always challenging in complex tunnel geological environment. In this study, a new method based on Bayesian networks is proposed to predict the probability for the classification of surrounding rock quality of tunnel with incomplete data. A database is collected with 286 cases in 10 tunnels, involving nine parameters: rock hardness, weathering degree, rock mass integrity, rock mass structure, structural plane integrity, in-situ stress, groundwater, rock basic quality, and surrounding rock level. Moreover, the Bayesian network structure is built using the collected database and quantitatively verified by strength analysis. Then, the accuracy, precision, recall, F-measure and receiver operating characteristic (ROC) curves are utilized for model evaluation. The average values of accuracy, precision, recall, F-measure, and area under the curve (AUC) are approximately 89.2%, 91%, 92%, 91%, and 0.98, respectively. These results indicate that the established classification model has high accuracy, even with small sample size and imbalanced samples. Ten additional sets of tunnel cases (incomplete data) are also used for verification. The results reveal that compared with the traditional Q-system (Q) and rock mass rating (RMR) classification methods, the proposed classification model has the lowest error rate and is capable of using incomplete data to predict sample results. Finally, sensitivity analysis suggests that the rock hardness and rock mass integrity have the strongest impact on the quality of tunnel surrounding rock. Overall, the findings of this study can serve as a useful reference for future rock mass quality evaluation in tunnels, underground powerhouses, slopes, etc.