Prediction of lining upward movement during shield tunneling using machine learning algorithms and field monitoring data

Abstract

During the construction of shield tunnels, the tunnel lining often has the problem of local or overall upward movement in soft soil areas. Excessive upward movement will lead to lining dislocation, cracks, damage, and even axis deviation. This paper elaborates on how to predict the process of tunnel lining upward movement using machine learning algorithms and field monitoring data systematically. First, fourteen input variables including shield operational parameters, tunnel geometry, geological conditions and anomalous condition are considered to predict the upward displacement of twelve output variables that represent the process of the upward movement of the tunnel lining. In addition, 80% field monitoring data (81 datasets) are selected randomly as the training set, and the remaining 20% (20 datasets) are the test set. Then, the average of 5-fold cross validation mean absolute error is regarded as the fitness function of optimization algorithms to find the optimal hyper-parameters. Finally, the prediction performance of four machine learning (ML) algorithms back-propagation neural network (BPNN), general regression neural network (GRNN), extreme learning machine (ELM), and support vector machine (SVM) optimized by particle swarm optimization (PSO) and genetic algorithm (GA) were compared. All ML algorithms except BPNN predicted successfully the trend of upward movement of tunnel lining. In particular, PSO-GRNN accurately captures the evolution of upward displacement in different periods of each ring with the lowest errors and the largest correlation coefficient values.