A dynamic learning method based on the Gaussian process for tunnel boring machine intelligent driving - Long, Haitao; Lu, Xiangqian; Ma, Chunchi; Li, Tianbin; Yan, Wenjin; Zhang, Hang; Dai, Kunkun (Frontiers in Earth Science, 2023)
With the rapid development of deep learning, intelligent learning methods are increasingly being introduced for the mining characteristics and rules of time-series data. The intelligent assistant driving of tunnel boring machines (TBMs) is a critical application. However, the optimization of driving parameters is essential for improving the construction efficiency of tunnels. In practice, the dynamic learning of time-series parameters in a real-time driving cycle is crucial for accurate predictions. However, existing prediction models are “static” and cannot dynamically capture parameter evolution. A novel dynamic learning model of TBM parameters was established by introducing the Gaussian process to solve this problem. The model can learn the decision-making experience of historical driving cycles, subsequently dynamically update the model according to the small sample data of the current driving cycle, and simultaneously achieve driving parameter prediction. Based on a tunnel project in western China, this study focused on the real-time prediction of TBM parameters. Results revealed that the average relative errors of the predicted values of total thrust and torque were 1.9% and 2.7%, respectively, and the prediction accuracy was higher than that of conventional models (e.g., random forest and long short-term memory). The model fully exploited updating of the small sample of parameters such that the average time cost of the model was shortened to 29.7 s, which satisfied the requirements of efficient application. The dynamic learning strategy of time-series data adopted in this study can be used as a reference for other similar engineering applications.