Feedback on a shared big dataset for intelligent TBM Part II: Application and forward look

This review discusses the application scenarios of the machine learning-supported performance prediction and the optimization effi-ciency of tunnel boring machines (TBMs). The rock mass quality ratings, which are based on the Chinese code for geological survey, were used to provide labels suitable for supervised learning. As a result, the generation of machine prediction for rock mass grades reason-ably agreed with the ground truth documented in geological maps. In contrast, the main operational parameters, i.e., thrust and torque, can be reasonably predicted based on historical data. Consequently, 18 collapse sections of the Yinsong project have been successfully predicted by several researchers. Preliminary studies on the selection of the optimal penetration rate and cost were conducted. This review also presents a summary of the main achievements in response to the initiatives of the Lotus Pool Contest in China. For the first time, large and well-documented TBM performance data has been shared for joint scientific research. Moreover, the review discusses the technical problems that require further study and the perspectives in the future development of intelligent TBM construction based on big data and machine learning.

Automated summary

This review discusses the application scenarios of machine learning-supported prediction and optimization efficiency in tunnel boring machines (TBMs). It shows that machine prediction for rock mass grades is in reasonable agreement with the ground truth. The review also highlights successful predictions of collapse sections and preliminary studies on optimal penetration rate and cost. Furthermore, it presents the achievements of the Lotus Pool Contest and discusses future prospects for intelligent TBM construction based on big data and machine learning.