Dynamic prediction and control of a tunnel boring machine with a particle swarm optimization–random forest algorithm and an integrated digital twin

Abstract

Tunnel boring machines (TBMs) often experience attitude deviation during excavation, impacting the stability and safety of tunnel construction. Traditional attitude adjustment relies on manual adjustment, which has a lagging effect. Therefore, the study combines a digital twin platform and a hybrid intelligence algorithm to enable real-time adjustment of the shield attitude deviation. The particle swarm optimization and random forest (PSO-RF) algorithm is first used to make accurate predictions of the shield attitude. Shapley additive explanations (SHAP) is subsequently employed to identify the key construction parameters. Then, based on these parameters, a control system for the shield attitude is designed in conjunction with a digital twin (DT) technique. A case study of China's Guiyang Metro Line 3 demonstrates the following: (1) The PSO-RF model achieves high accuracy, with R² values ranging from 0.916 to 0.943 for six shield attitude targets. (2) The key shield parameters are continuously optimized and adjusted within the control range to achieve shield attitude control. (3) The digital twin system provides real-time attitude warnings and parametric inference, significantly improving TBM performance and safety. In this paper, a novel method of combining predictive modeling and the DT platform is proposed. Under the proposed intelligent method, the attitude deviation of a TBM during tunneling was significantly reduced.