Artificial Neural Network Based Online Rockmass Behavior Classification of TBM Data

The tunnel boring machine (TBM) which is currently excavating the exploratory tunnel Ahrental-Pfons of the Brenner Base Tunnel records parameters like cutter head torque or advance pressure on a ten second interval. TBM data like this and derived indicators (e.g.: specific penetration, torque ratio.) are often used as additional help for assessing the response of the rockmass towards the excavation. The goal of this paper is to explore the applicability of a special type of artificial neural network (ANN) for an automatic online classification of the rockmass behavior solely based on TBM data. An ensemble of Long Short Term Memory (LSTM) networks with additional one-dimensional convolutional layers on top, is used to classify individual features of TBM data in mini-batches. The 1D convolutional input layers enhance the ANN's ability to extract significant features of the data. After an experimental phase, the best performance was achieved with an ensemble of eight convolutional LSTM - networks, where four networks each were deployed on the features torque - ratio and torque. Although the final categorical classification of the ensemble only achieved an overall accuracy of 74.4%, the probabilistic, relative output still yields valuable information about the rockmass behavior and could be used to aid geotechnicians in a real-world scenario.