Machine learning-based automatic control of tunneling posture of shield machine

For a tunnel driven by a shield machine, the posture of the driving machine is essential to the construction quality and environmental impact. However, the machine posture is controlled by the experienced driver of shield machine by setting hundreds of tunneling parameters empirically. Machine learning (ML) algorithm is an alternative method that can let the computer to learn from the driver's operation and try to model the relationship between parameters automatically. Thus, in this paper, three ML algorithms, i.e. multi-layer perception (MLP), support vector machine (SVM) and gradient boosting regression (GBR), are improved by genetic algorithm (GA) and principal component analysis (PCA) to predict the tunneling posture of the shield machine. A set of the parameters for shield tunneling is extracted from the construction site of a Shanghai metro. In total, 53,785 pairwise data points are collected for about 373 d and the ratio between training set, validation set and test set is 3:1:1. Each pairwise data point includes 83 types of parameters covering the shield posture, construction parameters, and soil stratum properties at the same time. The test results show that the averaged R2 of MLP, SVM and GBR based models are 0.942, 0.935 and 0.6, respectively. Then the automatic control for the posture of shield tunnel is illustrated with an application example of the proposed models. The proposed method is proved to be helpful in controlling the construction quality with optimized construction parameters.