Effects of data smoothing and recurrent neural network (RNN) algorithms for real-time forecasting of tunnel boring machine (TBM) performance

Abstract

Tunnel boring machines (TBMs) have been widely utilised in tunnel construction due to their high efficiency and reliability. Accurately predicting TBM performance can improve project time management, cost control, and risk management. This study aims to use deep learning to develop real-time models for predicting the penetration rate (PR). The models are built using data from the Changsha metro project, and their performances are evaluated using unseen data from the Zhengzhou Metro project. In one-step forecast, the predicted penetration rate follows the trend of the measured penetration rate in both training and testing. The autoregressive integrated moving average (ARIMA) model is compared with the recurrent neural network (RNN) model. The results show that univariate models, which only consider historical penetration rate itself, perform better than multivariate models that take into account multiple geological and operational parameters (GEO and OP). Next, an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed, and it performs better than other models. A sensitivity analysis shows that the penetration rate is the most important parameter, while other parameters have a smaller impact on time series forecasting. It is also found that smoothed data are easier to predict with high accuracy. Nevertheless, over-simplified data can lose real characteristics in time series. In conclusion, the RNN variant can accurately predict the next-step penetration rate, and data smoothing is crucial in time series forecasting. This study provides practical guidance for TBM performance forecasting in practical engineering.

Keywords

• Tunnel boring machine (TBM)
• Penetration rate (PR)
• Time series forecasting
• Recurrent neural network (RNN)