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

ABSTRACT: 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 verifi

SUBMITTER: Ma J 

PROVIDER: S-EPMC9674632 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature