tbm {bnRep} | R Documentation |
tbm Bayesian Network
Description
Risk assessment of TBM jamming based on Bayesian networks.
Format
A discrete Bayesian network to assess the risk of tunnel boring machine jamming. The Bayesian network was learned as in the referenced paper. The vertices are:
- Expansive_Surrounding_Rock
(High, Low, Medium, None);
- Fault_Zone
(High, Low, Medium, None);
- In.Situ_Stress
(High, Low, Medium, None);
- Large_Deformation_Surrounding_Rock
(Serious, Slight);
- Rock_Mass_Classes
(High, Low, Medium, None);
- Soft.Hard_Interbedded_Rock
(High, Low, Medium, None);
- TBM_Jamming
(No, Yes);
- Tunnell_Collapse
(Serious, Slight);
- Underground_Water
(High, Low, Medium, None);
- Water.And.Mud_Inrush
(Serious, Slight);
Value
An object of class bn.fit
. Refer to the documentation of bnlearn
for details.
References
Lin, P., Xiong, Y., Xu, Z., Wang, W., & Shao, R. (2022). Risk assessment of TBM jamming based on Bayesian networks. Bulletin of Engineering Geology and the Environment, 81, 1-15.
[Package bnRep version 0.0.1 Index]