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http://dx.doi.org/10.7474/TUS.2022.32.6.502

A Study on the Prediction of Disc Cutter Wear Using TBM Data and Machine Learning Algorithm  

Tae-Ho, Kang (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Soon-Wook, Choi (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Chulho, Lee (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Soo-Ho, Chang (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Publication Information
Tunnel and Underground Space / v.32, no.6, 2022 , pp. 502-517 More about this Journal
Abstract
As the use of TBM increases, research has recently increased to to analyze TBM data with machine learning techniques to predict the exchange cycle of disc cutters, and predict the advance rate of TBM. In this study, a regression prediction of disc cutte wear of slurry shield TBM site was made by combining machine learning based on the machine data and the geotechnical data obtained during the excavation. The data were divided into 7:3 for training and testing the prediction of disc cutter wear, and the hyper-parameters are optimized by cross-validated grid-search over a parameter grid. As a result, gradient boosting based on the ensemble model showed good performance with a determination coefficient of 0.852 and a root-mean-square-error of 3.111 and especially excellent results in fit times along with learning performance. Based on the results, it is judged that the suitability of the prediction model using data including mechanical data and geotechnical information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of disc cutter data.
Keywords
Slurry shield TBM; Machine learning; Disc cutter wear; Algorithm; Machine data;
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Times Cited By KSCI : 8  (Citation Analysis)
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