Data-Driven Prediction of Stability of Rock Tunnel Heading: An Application of Machine Learning Models

Abstract

In this paper, Artificial Neural Networks (ANN) have been utilized to predict the stability of a planar tunnel heading in rock mass based on the well-defined Hoek-Brown (HB) yield criterion. The HB model was developed to capture the failure criterion of rock masses. To provide the datasets for an ANN model, the numerical upper bound (UB) and lower bound (LB) solutions obtained from the finite element limit analysis (FELA) with the HB failure criterion for the problem of tunnel headings are derived. The sensitivity analysis of all influencing parameters on the stability of rock tunnel heading is then performed on the developed ANN model. The proposed solutions will enhance the dependability and preciseness of predicting the stability of rock tunnel heading. Note that the effect of the unlined length ratio has not been explored previously but has been found to be of critical importance and significantly contributes to the failure of rock tunnel heading. By utilizing the machine learning-aided prediction capability of the ANN approach, the numerical solutions of the stability of tunnel heading can be accurately predicted, which is better than the use of the classic linear regression approach. Thus, providing a better and much safer assessment of mining or relatively long-wall tunnels in rock masses.