A Data-Driven Framework for Tunnel Geological-Type Prediction Based on TBM Operating Data

Abstract

One main challenge in tunnel constructions is to predict the tunnel geological conditions without excavation to ensure safety during the construction process. This paper proposes a data-driven framework for real-time interpreting the operating data of tunnel boring machines (TBMs) without interrupting tunneling operations, and eventually automate the tunneling operation. In this framework, we first convert the indexes of the original data from discontinuous operating time to continuous operating displacement. After screening outliers, to more exhaustively explore the inherent characteristics of the TBM operating data, we then augment features by using the first-order and the second-order difference information. There are two main concerns for developing a desired geological-type predictor: 1) since multiple geological types could coexist in one tunnel section, the predictor should have multiple outputs and 2) since the geological types are specified by the values of 7 kinds of physical-mechanical indexes of geological types, this geological characteristic should also be encoded into the predictor's structure. Therefore, we design a feed-forward multiple-output artificial neural network (ANN) with two hidden layers as the predictor, where the second hidden layer has 7 nodes that correspond to 7 kinds of physical-mechanical indexes. The experimental results show that: 1) the feature augmentation (FA) method indeed improves the prediction performance; 2) the ANN predictor has the best performance on the test set when the second hidden layer has 7 nodes; 3) the proposed ANN predictor outperforms many widely-used learning models, e.g., XGboost, random forest (RF), and support vector regression (SVR); and 4) the predictor is capable of accurately predicting the geological types of stratum.