Underground excavation stability assessment in squeezing ground conditions using neural network classifier

Squeezing of rock is known to be a time-dependent phenomenon causing large deformation of rock around due essentially to excess of shear stress and occurring within a variety of failure mechanisms. It can result in economic loss and major safety concerns, as controlling the large deformations represent a rock engineering challenge. Therefore, a reliable estimate of tunnel deformation is essential for an adequate excavation, project design, and planning since it can help to build a strategy to manage the ground instability well in advance. The aim of this paper is to develop an artificial intelligence-based tool capable of recognizing the squeezing ground class as an alternative to the existing empirical charts or correlations. Historical squeezing data were compiled. On the basis of these available data, the squeezing potential of the ground was categorized into three classes namely; non-squeezing, minor, and major squeezing. A feed-forward neural network (FFN) classifier was implemented to recognize each type of squeezing class. In general, high accuracies were achieved indicating improvement over some existing squeezing models. It is suggested that the FFN-based classifier could be a useful tool in managing squeezing ground.