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.

Significance and methodology:Preprocessing the big data for machine learning on TBM performance

This paper addresses the significance of preprocessing big data collected during a tunnel boring machine(TBM)excavation before it is used for machine learning on various TBM performance predictions.The research work is based on two water diversion tunneling projects that cover 29.52 km and 17051 boring cycles.It has been found that the penetration rate calculated from the raw measured penetration distances exhibits more random behavior owing to their percussive and vibratory behavior of the cutterhead.A moving average method to process the negative instantaneous velocities and a noise reduction filter to deal with signals with abnormal frequencies have been recommended.An index called the drilling efficiency index is introduced to assess the relationships between the mechanical parameters in a boring cycle,whose linear regression coefficient R^(2)is taken for a preliminary investigation of possible problems requiring preprocessing.The research work defines the irrelevant data whose errors are caused by human or mechanical mistakes,and therefore should be cleaned or amended.These irrelevant data can be divided into five categories:(1)premature cycles,(2)sensor defects,(3)mechanical defects,(4)human interruption,and(5)missing files.A program TBM-Processing has been coded for the recognition and classification of these categories.PDF books generated by the program have been uploaded at GitHub to encourage discussions,collaboration,and upgrading of the data processing work with our peers.