Rock mass type prediction for tunnel boring machine using a novel semi-supervised method

Abstract

Tunnel boring machine is extremely sensitive to geological changes, and the accurate prediction of geological conditions ahead of the tunnel face is helpful for safe and efficient tunneling. Since soft methods can use on-site data to predict geological conditions, they are getting more and more attention. However, there is an imbalance between the machine data and geological data, and current soft methods can only utilize limited machine data with geological labels, limiting the performance of the model. To make full use of the massive unlabeled data and limited labeled data, a novel semi-supervised method is proposed to establish the rock mass type prediction model. In the first step, twenty machine parameters are selected as inputs, and the data preprocessing is performed. Thereafter, a geological feature extractor is established based on the stacked sparse autoencoder and unlabeled machine data. Finally, a feature classifier is obtained based on the deep neural network and labeled geological features to realize the prediction of rock mass type. The on-site data collected from Mumbai metro tunnel was utilized to verify the effectiveness of the proposed method. The results indicate that the unsupervised stacked sparse autoencoder is capable of extracting geological features, and the proposed stacked sparse autoencoder and deep neural network-based semi-supervised method outperforms commonly adopted supervised methods. Its classification performance (F-measure) is 13.84%, 10.29%, 8.71%, 5.23% and 5.13% higher than the support vector machine-based, decision tree-based, K-nearest neighbor-based, random forest-based and deep neural network-based methods, respectively. Therefore, the proposed semi-supervised method can predict the rock mass types ahead of the tunnel face more accurately than the current supervised soft methods.