Prediction of tunnel face stability using a naive Bayes classifier

Abstract

This paper develops a convenient approach for facilitating the prediction of tunnel face stability in the framework of Bayesian theorem. First, a number of values of the features influencing the face-stability of tunnels are chosen according to the full factorial design. Secondly, the software OptumG2 is utilized to performed strength reduction analyses to obtain safety factors regarding tunnel face stability. Based on the simulated safety factors, the chosen samples are labeled as stable (Fs ≥ 1) or unstable samples (Fs < 1). Thirdly, the model parameters that characterize the distribution of the random variables are then estimated by maximizing the well-known likelihood function. After that, the probability density functions (PDF) of the features are identified, and a naive Bayes classifier is constructed with the prior probabilities of the stable and the unstable state. The so-called type I and type II errors are estimated with stable and unstable samples, respectively. The model parameters are then calibrated with additional stable samples to obtain the second classifier. Finally, the two classifiers are evaluated using independent samples that have not been seen in the training dataset. The proposed method allows geotechnical engineers to predict the stability of tunnel faces with great efficiency. It is applicable for general cases of tunnels where the parameters are within the ranges bounded by the specified values.