Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Abstract

The moving trajectory of the pipe-jacking machine (PJM), which primarily determines the end quality of jacked tunnels, must be controlled strictly during the entire jacking process. Developing prediction models to support drivers in performing rectifications in advance can effectively avoid considerable trajectory deviations from the designed jacking axis. Hence, a gated recurrent unit (GRU)-based deep learning framework is proposed herein to dynamically predict the moving trajectory of the PJM. In this framework, operational data are first extracted from a data acquisition system; subsequently, they are preprocessed and used to establish GRU-based multivariate multistep-ahead direct prediction models. To verify the performance of the proposed framework, a case study of a large pipe-jacking project in Shanghai and comparisons with other conventional models (i.e., long short-term memory (LSTM) network and recurrent neural network (RNN)) are conducted. In addition, the effects of the activation function and input time-step length on the prediction performance of the proposed framework are investigated and discussed. The results show that the proposed framework can dynamically and precisely predict the PJM moving trajectory during the pipe-jacking process, with a minimum mean absolute error and root mean squared error (RMSE) of 0.1904 and 0.5011 mm, respectively. The RMSE of the GRU-based models is lower than those of the LSTM- and RNN-based models by 21.46% and 46.40% at the maximum, respectively. The proposed framework is expected to provide an effective decision support for moving trajectory control and serve as a foundation for the application of deep learning in the automatic control of pipe jacking.

Graphical abstract

Keywords

dynamic prediction / moving trajectory / pipe jacking / GRU / deep learning

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant No. 52090082), the Natural Science Foundation of Shandong Province, China (No. ZR202103010505) and Fundamental Research Funds for the Central Universities of China (No. 22120210428). The data and relevant materials were provided by the Shanghai Tunnel Engineering Construction Co., Ltd. and Shanghai Shentong Metro Co., Ltd.

Conflict of Interest

The authors declare that they have no conflict of interest.

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