Deep Learning Model for Shield Tunneling Advance Rate Prediction in Mixed Ground Condition Considering Past Operations

The advance rate (AR) is a significant parameter in shield tunneling construction, which has a major impact on construction efficiency. From a practical perspective, it's helpful to establish a predictive model of the AR, which takes into account the instantaneous parameters as well as the past operations. However, for shield tunneling in mixed ground conditions, most researches focused on the average values of AR per ring and neglect the influence of past operations. This article presents a long short-term memory (LSTM) recurrent neural network model, which was developed for the slurry shield tunneling in a mixed ground of round gravel and mudstone in Nanning metro. A temporal aggregated random forest is employed to rank the importance of the explanatory features. The model performances in different ground conditions are investigated. The results show that the LSTM model can be effectively implemented for the AR prediction. A high correlation is observed between predicted and measured AR with a correlation coefficient ($R^{2}$ ) of 0.93. The LSTM based AR predictive model is compared with the random forest (RF) model, the deep feedforward network (DFN) model, and the support vector regression (SVR) model. The comparison shows that the LSTM model has the best performances compared to other models. With one-fourth features, we can achieve a 95% prediction accuracy measured by the $R^{2}$ in the proposed model.

Abstract licence: CC BY-NC-ND

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.