Feedback on a shared big dataset for intelligent TBM PartⅠ:Feature extraction and machine learning methods

This review summarizes the research outcomes and findings documented in 45 journal papers using a shared tunnel boring machine(TBM)dataset for performance prediction and boring efficiency optimization using machine learning methods.The big dataset was col-lected during the Yinsong water diversion project construction in China,covering the tunnel excavation of a 20 km-section with 199 items of monitoring metrics taken with an interval of one second.The research papers were the result of a call for contributions during a TBM machine learning contest in 2019 and covered a variety of topics related to the intelligent construction of TBM.This review com-prises two parts.Part I is concerned with the data processing,feature extraction,and machine learning methods applied by the contrib-utors.The review finds that the data-driven and knowledge-driven approaches in extracting important features applied by various authors are diversified,requiring further studies to achieve commonly accepted criteria.The techniques for cleaning and amending the raw data adopted by the contributors were summarized,indicating some highlights such as the importance of sufficiently high fre-quency of data acquisition(higher than 1 second),classification and standardization for the data preprocessing process,and the appro-priate selections of features in a boring cycle.The review finds that both supervised and unsupervised machine learning methods have been utilized by various researchers.The ensemble and deep learning methods have found wide applications.Part I highlights the impor-tant features of the individual methods applied by the contributors,including the structures of the algorithm,selection of hyperparam-eters,and model validation approaches.

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.

Geotechnical centrifuge model tests for explosion cratering and propagation laws of blast wave in sand

This paper presents the explosion cratering effects and their propagation laws of blast waves in dry standard sands using a 450 g-t geotechnical centrifuge apparatus.Ten centrifuge model tests were completed with various ranges of explosive mass,burial depth and centrifuge accelerations.Eleven accelerometers were installed to record the acceleration response in sand.The dimensions of the explosion craters were measured after the tests.The results demonstrated that the relationship between the dimensionless parameters of cratering efficiency and gravity scaled yield is a power regression function.Three specific function equations were obtained.The results are in general agreement with those obtained by other studies.A scaling law based on the combination of the π terms was used to fit the results of the ten model tests with a correlation coefficient of 0.931.The relationship can be conveniently used to predict the cratering effects in sand.The results also showed that the peak acceleration is a power increasing function of the acceleration level.An empirical exponent relation between the proportional peak acceleration and distance is proposed.The propagation velocity of blast waves is found to be ranged between 200 and 714 m/s.

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.

Diarylheptanoids from the root bark of Juglans cathayensis

A new diarylheptanoid glucoside,（8R,9R）-17-methoxy-2-oxatricyclo[13.2.2.1^（3.7）]icosa-l（17）,3（20）,4,6, 15,18-hexaene-4,9,10-triol-9-O-β-D-glucopyranoside（1）,namely jugcathayenoside,together with two known diarylheptanoids,（＋）-galeon（2） and 4-hydroxy-17-methoxy-2-oxatricyclo[13.2.2.1^（3,7） ]icosa-1（17）,3（20）,4,6,15,18-hexaene-9-one（3）,were isolated from the root bark of Juglans cathayensis. Their structures were elucidated on the basis of extensive spectroscopic data analysis.