A probability prediction method for the classification of surrounding rock quality of tunnels with incomplete data using Bayesian networks.
An object of class bn.fit. Refer to the documentation of bnlearn for details.
A discrete Bayesian network to predict the probability for the classification of surrounding rock quality of tunnel with incomplete data. Probabilities were given within the referenced paper. The vertices are:
Basic quality of rock mass (Num1, Num2, Num3, Num4, Num5);
(DryWet, MoistDripping, RainlikeDripping, TubularGushing);
(Low, Medium, High, ExtremelyHigh);
(Hard, SlightlyHard, SlightlySoft, Soft, ExtremelySoft);
(Complete, SlightlyComplete, SlightlyBroken, Broken, ExtremelyBroken);
(State1, State2, State3, State4, State5);
(I, II, III, IV, V);
(Good, Ordinary, Bad, VeryBad);
(Fresh, Slight, Medium, Severe, Extreme).
Ma, J., Li, T., Li, X., Zhou, S., Ma, C., Wei, D., & Dai, K. (2022). A probability prediction method for the classification of surrounding rock quality of tunnels with incomplete data using Bayesian networks. Scientific Reports, 12(1), 19846.