Applicability of Neural Network in Rock Classification of Mountain Tunnel

Abstract

In construction projects of mountain tunnels, with a purpose of improving accuracies of rock classifications in preliminary survey, we have studied applicability of Artificial Neural Network (ANN). One characteristics of ANN is that it does not require defining clear formula correlating data input and output, by using its learning function. Leveraging the characteristics, accuracy of rock classification improved by using geophysical datasets (seismic velocity and resistivity) at a tunnel face and surrounding. Also, ANN has a problem of reduced applicability caused by over learning to training data. It is possible to avoid the over learning problem by increasing training dataset, but it is not easy to accumulate complete dataset of geophysical properties and actual rock classification obtained in construction stage. We found that it is important to collect various tunnel data without much deviation, for accumulating training datasets effectively in the future.

 

This Paper was Originally Published in Japanese in Journal of the Society of Materials Science, Japan 67 (2018) 354–359. In order to more precisely explain, we add vertical axis label as Estimated rock mass class and horizontal axis label as Actual rock mass class in Fig. 3 and Fig. 4.