Prediction of Geological Parameters during Tunneling by Time Series Analysis on In-situ Data: A Recent Study | Chapter 06 | Novel Perspectives of Engineering Research Vol. 10

 A tunnel boring machine (TBM) is a piece of heavy-duty equipment used to construct underground tunnels. Geological conditions are crucial components in the tunnelling process, as they have a direct impact on the control of construction equipment. The goal of this work is to use continually gathered aerial characteristics to anticipate geological information ahead of TBM. This study focused on the underlying links between the sequential nature of tunnel in-situ data and the continuous interaction between equipment and geology, and used the long short-term memory (LSTM) time series neural network technique for processing in-situ data. A method for predicting geological characteristics in advance is proposed based on TBM real-time status monitoring data. The proposed method was used to predict five geological parameters for a tunnel project in China, with R2 values more than 0.98 for all five geological parameters. In terms of performance, the LSTM was compared to an artificial neural network (ANN). The LSTM's prediction accuracy was significantly higher than the ANN's, and its generalisation and robustness were also better than the ANN's, showing that the recommended LSTM approach could extract the in-situ data's sequence features. The rule of equipment-geology interaction was mirrored in the model's memory structure thanks to the adoption of the "gate" principle, allowing for precise prediction of geological parameters during tunnelling. The model's impact on the time frame and prediction distance is also investigated. The proposed method provides a novel approach to obtaining geological data during TBM construction, as well as a framework for analysing in-situ data with sequence properties.

Author(S) Details

Shanglin Liu
Key Laboratory of Modern Engineering Mechanics, Tianjin University, Tianjin 300072, China.

Kaihong Yang
Key Laboratory of Modern Engineering Mechanics, Tianjin University, Tianjin 300072, China.

Jie Cai
Design and Research Institute of Tunneling Machine, China Railway Construction Heavy Industry, Changsha 410100, China.

Siyang Zhou
Key Laboratory of Modern Engineering Mechanics, Tianjin University, Tianjin 300072, China.

Qian Zhang
Design and Research Institute of Tunneling Machine, China Railway Construction Heavy Industry, Changsha 410100, China.

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