Highlights

Machine learning model developed based on big database from TBM construction of Yin-Song Diversion Project in China.

Total thrust and cutterhead torque in the stable period predicted through the data in the rising period of TBM boring.

Variation of the TBM performance is superior to the classical theoretical CSM model.

Data efficiency and date deficiency discussed and the most important parameters addressed.

Abstract

Predicting the performance of a tunneling boring machine is vitally important to avoid any possible accidents during tunneling boring. The prediction is not straightforward due to the uncertain geological conditions and the complex rock-machine interactions. Based on the big data obtained from the 72.1 km long tunnel in the Yin-Song Diversion Project in China, this study developed a machine learning model to predict the TBM performance in a real-time manner. The total thrust and the cutterhead torque during a stable period in a boring cycle was predicted in advance by using the machine-returned parameters in the rising period. A long short-term memory model was developed and its accuracy was evaluated. The results show that the variation in the total thrust and cutterhead torque with various geological conditions can be well reflected by the proposed model. This real-time predication shows superior performance than the classical theoretical model in which only a single value can be obtained based on the single measurement of the rock properties. To improve the accuracy of the model a filtering process was proposed. Results indicate that filtering the unnecessary parameters can enhance both the accuracy and the computational efficiency. Finally, the data deficiency was discussed by assuming a parameter was missing. It is found that the missing of a key parameter can significantly reduce the accuracy of the model, while the supplement of a parameter that highly-correlated with the missing one can improve the prediction.

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https://www.sciencedirect.com/science/article/pii/S1674987120300530